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4/1/2024
The Surface Transportation Board Office of Environmental Analysis (OEA) last week filed a notice of intent (NOI) to announce it will prepare an environmental impact statement for the Green Eagle Railroad LLC’s proposed construction and operation of a 1.3-mile rail line in Maverick County, Texas.
The proposed rail line would be part of the proposed Puerto Verde Global Trade Bridge project, which calls for a new trade corridor for freight rail and commercial motor vehicles between Piedras Negras, Coahuila, Mexico, and Eagle Pass, Texas, STB officials said in a press release.
Because the proposed rail line has the potential to result in significant environmental impacts, the OEA determined that the preparation of an environmental impact statement is appropriate under the National Environmental Policy Act. The NOI begins the scoping period, the first step of the environmental review process.
The OEA will accept comments on the scope of the environmental impact statement until April 29. OEA will hold three public meetings during the comment period.

NCDOT solicits input on updated plan for passenger-rail facility

OIG: FRA lacks needed oversight of fatigue management

NYPD to test weapon-detection technology in subways

Operation Lifesaver awards grants for public awareness campaigns in 11 states

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Airlines, shippers pitch feds on sustainability to keep up with other countries  Toronto Star
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Worldwide Countries outside of the United States and Canada.
We operate responsibly every day to enable a safe and healthy future for people and communities globally. Our sustainability strategy helps us achieve our corporate purpose, positively impact society and deliver long-term business value.
Download our Impact Report 2022/2023
Download our Impact Report 2022/2023 Summary

From finding solutions for some of the world’s most debilitating diseases, to getting our medicines and vaccines to those in need and building more effective health systems, we are always on a mission to create a better world.

Operating responsibly is at the heart of our ability to deliver sustainable impact – driving long-term value for our company and society.
In collaboration with key stakeholders, we work to ensure our science advances health care, and our products are accessible and affordable to those in need.
We make long-term investments in research & development and target diseases where we can make the greatest difference.
We are committed to addressing the global public health burden of infectious diseases.
Through partnership, investment and endless invention, we seek to overcome barriers to providing a healthier future for all.
Our company’s success is built on a culture that embraces different perspectives and values the contributions of each individual. We recognize that our competitiveness is strengthened by a diverse, skilled and engaged workforce.

As inventors, we invest in learning and development because our culture centers around curiosity.
Only when our employees feel their best, in all aspects of their lives, can they perform at their highest level.
The dynamic force driving everything we do.

A healthy planet is essential to human health and the sustainability of our business, while also providing opportunities for product innovation and reducing cost and risk. Our company has a long history of environmental stewardship and compliance, and we realize that our strategy and efforts need to continuously evolve in the face of a changing climate.
Energy conservation, water use reduction, efficient use of raw materials and responsible waste management.
We deploy sustainability strategies to reduce environmental impact.
We team up with our suppliers and customers to understand and minimize the life-cycle impacts of our products.
We operate with the highest standards of ethics and integrity. By putting our ethics and values at the foundation of everything we do, we create an accountable culture that betters our company’s decision-making, adaptability and reliability.
Our code of conduct is at the core of our character, helping us to maintain our reputation as a trustworthy company.
We strive to detect and prevent labor and human rights abuses in our own operations and in our supply chain.
We follow strict ethical sales and marketing practices in all our businesses.

number of people reached with our innovations in 2022
U.S. pay equity for female and male employees, as well as for non-white (including Black, Hispanic and Asian employees) and white employees in 2022
purchased electricity from renewable sources in 2022
spending with diverse Tier 1 and 2 suppliers globally in 2022

We strive to find sustainable solutions to key global health challenges and to strengthen communities where our employees live and work.
There is real word impact behind our work. Dig deeper.
View our stories
When we bring together people from different backgrounds, the possibilities for invention are endless
Next: Inspiring innovation through diversity and inclusion
How we’re advancing health equity through partnerships to help patients navigate cancer care
Next: Collaborating to help make cancer care more accessible worldwide
The basketball legend and businessman talked DE&I, HIV advocacy and leadership in an inspiring visit to our global headquarters
Next: Magic Johnson shares what makes him work harder
From fostering an inclusive and supportive culture to working with diverse suppliers, diversity and inclusion are integral to helping us better serve patients
Next: Diversity and inclusion strengthen everything we do
When we bring together people from different backgrounds, the possibilities for invention are endless
Next: Inspiring innovation through diversity and inclusion
How we’re advancing health equity through partnerships to help patients navigate cancer care
Next: Collaborating to help make cancer care more accessible worldwide
The basketball legend and businessman talked DE&I, HIV advocacy and leadership in an inspiring visit to our global headquarters
Next: Magic Johnson shares what makes him work harder
From fostering an inclusive and supportive culture to working with diverse suppliers, diversity and inclusion are integral to helping us better serve patients
Next: Diversity and inclusion strengthen everything we do
Diversity, equity & inclusion
Our workforce represents the people we serve
Our culture and values
It all comes back to saving and improving lives.
Environmental sustainability
We design and make products in a safe, effective and environmentally sound manner.
This website of Merck & Co., Inc., Rahway, N.J., USA (the “company”) includes “forward-looking statements” within the meaning of the safe harbor provisions of the U.S. Private Securities Litigation Reform Act of 1995. These statements are based upon the current beliefs and expectations of the company’s management and are subject to significant risks and uncertainties. There can be no guarantees with respect to pipeline candidates that the candidates will receive the necessary regulatory approvals or that they will prove to be commercially successful. If underlying assumptions prove inaccurate or risks or uncertainties materialize, actual results may differ materially from those set forth in the forward-looking statements.

Risks and uncertainties include but are not limited to, general industry conditions and competition; general economic factors, including interest rate and currency exchange rate fluctuations; the impact of pharmaceutical industry regulation and health care legislation in the United States and internationally; global trends toward health care cost containment; technological advances, new products and patents attained by competitors; challenges inherent in new product development, including obtaining regulatory approval; the company’s ability to accurately predict future market conditions; manufacturing difficulties or delays; financial instability of international economies and sovereign risk; dependence on the effectiveness of the company’s patents and other protections for innovative products; and the exposure to litigation, including patent litigation, and/or regulatory actions.

The company undertakes no obligation to publicly update any forward-looking statement, whether as a result of new information, future events or otherwise. Additional factors that could cause results to differ materially from those described in the forward-looking statements can be found in the company’s Annual Report on Form 10-K for the year ended December 31, 2023 and the company’s other filings with the Securities and Exchange Commission (SEC) available at the SEC’s Internet site (www.sec.gov).
No Duty to Update
The information contained in this website was current as of the date presented. The company assumes no duty to update the information to reflect subsequent developments. Consequently, the company will not update the information contained in the website and investors should not rely upon the information as current or accurate after the presentation date.

By continuing, you will be directed to a site intended only for residents of the United States and Canada. We are called MSD everywhere, except in the United States and Canada where we are known as Merck & Co Inc, Rahway, NJ USA.

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With fast fashion, the quantity of clothes produced and thrown away has boomed. Find out more about the environmental impact and what the EU is doing about it.
Fast fashion is the constant provision of new styles at very low prices.
To tackle the impact on the environment, the EU wants to reduce textile waste and increase the life cycle and recycling of textiles. This is part of the plan to achieve a circular economy by 2050.

Find out about the circular economy’s definition, its importance and benefits
It takes a lot of water to produce textile, plus land to grow cotton and other fibres. To make a single cotton t-shirt, 2,700 litres of fresh water are required according to estimates, enough to meet one person’s drinking needs for 2.5 years.

The textile sector was the third largest source of water degradation and land use in 2020. In that year, it took on average nine cubic metres of water, 400 square metres of land and 391 kilogrammes (kg) of raw materials to provide clothes and shoes for each EU citizen.
Textile production is estimated to be responsible for about 20% of global clean water pollution from dyeing and finishing products.
A single laundry load of polyester clothes can discharge 700,000 microplastic fibres that can end up in the food chain.

The majority of microplastics from textiles are released during the first few washes. Fast fashion is based on mass production, low prices and high sales volumes that promotes many first washes.

Washing synthetic products leads to the accumulation of more than half a million tonnes of microplastics on the bottom of the oceans every year. In addition to this global problem, the pollution generated by garment production has a devastating impact on the health of local people, animals and ecosystems where the factories are located.
The fashion industry is estimated to be responsible for 10% of global carbon emissions – more than international flights and maritime shipping combined.

According to the European Environment Agency, textile purchases in the EU in 2020 generated about 270 kg of CO2 emissions per person. That means textile products consumed in the EU generated greenhouse gas emissions of 121 million tonnes.
The way people get rid of unwanted clothes has also changed, with items being thrown away rather than donated. Less than half of used clothes are collected for reuse or recycling, and only 1% of used clothes are recycled into new clothes, since technologies that would enable clothes to be recycled into virgin fibres are only now starting to emerge.
On average Europeans use nearly 26 kilos of textiles and discard about 11 kilos of them every year. Used clothes can be exported outside the EU, but are mostly (87%) incinerated or landfilled.

The rise of fast fashion has been crucial in the increase in consumption, driven partly by social media and the industry bringing fashion trends to more consumers at a faster pace than in the past.

The new strategies to tackle this issue include developing new business models for clothing rental, designing products in a way that would make re-use and recycling easier (circular fashion), convincing consumers to buy clothes of better quality that last longer (slow fashion) and generally steering consumer behaviour towards more sustainable options.
As part of the circular economy action plan, the European Commission presented in March 2022 a new strategy to make textiles more durable, repairable, reusable and recyclable, tackle fast fashion and stimulate innovation within the sector.

The new strategy includes new ecodesign requirements for textiles, clearer information, a Digital Product Passport and calls companies to take responsibility and act to minimise their carbon and environmental footprints.
In June 2023, MEPs set out proposals for tougher EU measures to halt the excessive production and consumption of textiles. Parliament’s report calls for textiles to be produced respecting human, social and labour rights, as well as the environment and animal welfare.

The EU has an EU Ecolabel that producers respecting ecological criteria can apply to items. This gives more visibility to products that include fewer harmful substances and cause less water and air pollution.

In 2018, the waste directive was approved by the Parliament. The Commission strategy also includes measures to tackle the presence of hazardous chemicals, while it calls on producers to take responsibility for their products along the value chain, including when they become waste, and aims to help consumers to choose sustainable textiles.

The Parliament put forwardideas for changes to textile waste rules in March 2024. The revision of the waste directive will introduce extended producer responsibility schemes. This means in practice that producers of textiles, such as clothing, footwear hats, accessories, as well as other companies that put such products on the European single market, will have to cover the costs for the separate collection, sorting and recycling.

While the Commission proposed that the extended producer responsibility schemes should be introduced 30 months after the directive enters into force, MEPs pushed for 18 months. In addition, EU countries would be obliged to collect textiles separately by 1 January 2025 for re-use, preparing for re-use and recycling.

“We request a textile waste reduction target, with an oversight of exported used textiles,” said Anna Zalewska (ECR, Poland), the MEP responsible for steering the rules through Parliament. She also called for better infrastructure for separate collection of textiles and more efficient sorting of municipal waste, so that items which can be recycled are extracted before being sent to the incinerator or landfill.

The negotiations with the Council will be done by the next Parliament, which will be elected during the European elections on 6-9 June 2024.
Food waste reduction: what EU actions are there?

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© Copyright & Disclaimer 2024
Waste Week engages the Loyola community in efforts to generate less waste.
Story by Jorge Haddad
Waste production is one of the world’s most significant environmental issues, and the problem is particularly extreme in the United States. The US generates more waste than any other nation in the world. On average, each American disposes of around 1,800 pounds annually, but large companies and institutions still generate the majority of waste disposed of nationwide. To tackle this problem, Loyola University Chicago has taken matters into its own hands. The University recycles over 500 tons of waste and diverts 200 tons of organic materials through composting annually. These efforts are part of Loyola’s broader efforts to reduce the University’s environmental footprint.
Waste management and other issues related to sustainability are becoming increasingly important to uncover, discuss, and tackle. To engage the campus community in these efforts, the Office of Sustainability hosts various events highlighting waste, water, transportation, energy, and food.
This year, February 20 to 24 is Waste Week at Loyola, an event focused on educating students from all campuses about waste and materials management. Throughout the week, students will participate in various seminars and interactive activities to learn how they can reduce their personal waste footprints, as well as discuss what Loyola is doing as an institution. Activities include a clothing swap, three different wipe-out waste challenges, and a special screening of the documentary film “Going Circular.” The film explores solutions for creating a circular economy, and the screening is a pre-festival event taking place as a part of the One Earth Film Festival. View the trailor and reserve your tickets for the screening here.
The week will also feature a discussion of waste in Chicago with Carter O’Brien, assistant commissioner of Streets and Sanitation, and a seminar with Dr. Abigail Derby Lewis of the Keller Science Action Center at the Field Museum.
Join us during Waste Week 2023, and don’t miss out on the opportunity to learn more about waste management and what you can do to make Loyola and the world less wasteful.
Story by Jorge Haddad
Waste production is one of the world’s most significant environmental issues, and the problem is particularly extreme in the United States. The US generates more waste than any other nation in the world. On average, each American disposes of around 1,800 pounds annually, but large companies and institutions still generate the majority of waste disposed of nationwide. To tackle this problem, Loyola University Chicago has taken matters into its own hands. The University recycles over 500 tons of waste and diverts 200 tons of organic materials through composting annually. These efforts are part of Loyola’s broader efforts to reduce the University’s environmental footprint.
Waste management and other issues related to sustainability are becoming increasingly important to uncover, discuss, and tackle. To engage the campus community in these efforts, the Office of Sustainability hosts various events highlighting waste, water, transportation, energy, and food.
This year, February 20 to 24 is Waste Week at Loyola, an event focused on educating students from all campuses about waste and materials management. Throughout the week, students will participate in various seminars and interactive activities to learn how they can reduce their personal waste footprints, as well as discuss what Loyola is doing as an institution. Activities include a clothing swap, three different wipe-out waste challenges, and a special screening of the documentary film “Going Circular.” The film explores solutions for creating a circular economy, and the screening is a pre-festival event taking place as a part of the One Earth Film Festival. View the trailor and reserve your tickets for the screening here.
The week will also feature a discussion of waste in Chicago with Carter O’Brien, assistant commissioner of Streets and Sanitation, and a seminar with Dr. Abigail Derby Lewis of the Keller Science Action Center at the Field Museum.
Join us during Waste Week 2023, and don’t miss out on the opportunity to learn more about waste management and what you can do to make Loyola and the world less wasteful.
© Copyright & Disclaimer 2024 · Privacy Policy

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Traveling is one of the best ways to rest your body and soul and recharge from everyday obligations. One of the newest green trends is the concept of eco-tourism which ties two beautiful ideas together – the love for traveling and the love for our planet.
Numerous resorts and destinations have worked their way up to the eco standards so they can proudly present as eco-conscious for all the tourists out there that care about sustainable vacations. Here is our list of luxury destinations you probably did not know were great for green tourism.
This gorgeous European county is often underestimated as a travel destination idea, which is definitely a mistake. Slovenia has vivid natural beauty, modern cities, and old towns that smell of history.
Besides being a beautiful country, Slovenia is famous for being big on green policies. Ljubljana,  the capital city of Slovenia got the title of the world's greenest City in 2016.  around 3/4 of energy resource comes from sustainable sources such as water power in Slovenia.
So, if you are up to having a blast, and an active vacation in the mountains but also experiencing the culture and the perks of modern cities, and all that while staying green, we suggest you visit Slovenia.

Perhaps you did not know this, but Italy is a country that is working very hard on having more green policy destinations ready for new groups of tourists. Italy may not be something you would normally associate with green tourism, but actually, it was one of the first countries that went in that direction.
So technically Italy is both old and new in the world of eco-tourism. The global pandemic slowed down this process of going green in tourism, so Italia is still trying to catch up with that. This country has numerous small villages with rich histories and breathtaking nature, which are currently promoted as popular options for eco-friendly destinations.
It seems like the perfect combination of the luxury vacation, sustainable visit, and exploring nature is definitely the biggest thing to draw new eco-tourists to Italy. Glamping is especially popular in Italy, but if that is not your cup of espresso, you can always look for luxurious Puglia villas that are a dream come true.

Norway is one of the best green tourism options for all of you who enjoy adventures and active vacations. Literally the perfect place for eco-tourism. Norway is one of the pioneers in this, not only in the area of tourism, but generally in this green state of mind, love for nature, and keeping it sustainable.
Hiking, kayaks, incredible mountains, and Norwegian beaches are something that should not be missed. Imagine seeing Aurora Borealis live at least once in your life! That natural beauty is impossible to be described with words, you simply have to experience it.
That experience alone would be worth the travel, plus a lot more that will come along on every corner. Some life-changing experiences cannot be paid for with money, but you can surely put your Norwegian fairytale on a credit card.
If taking major trips to Europe is not quite what you had in mind for a luxury holiday, you can always create your own eco-friendly luxury experience right in the center of the world.
New York City is on the list of most wanted cities for every secret service on the planet. It’s where everything is happening – always, at the same time. And where everybody wants to be.

Rent a monthly stay in New York and design your fancy holiday centered around making ecological and conscious decisions. Discover planet-friendly events, activities, and initiatives. Enjoy cultural and artistic venues focused on climate change, for example.
Plan your meals in restaurants that promote healthy food choices. Cycle your way through Central Park and reduce your carbon footprint to a minimum. You can do it!
If you are one of the lucky owners of luxury credit cards, you should know there is more to it than a nice amount of money. These cards can be your best friends on the road, giving you a VIP status for travels, and a lot of other fun additions.
If you do not have a credit card, shortly, that is a wonderful piece of plastic (more and more companies are making them from recycled plastic or metal) that allows you to lend money from the card issuer to pay for services and things.
After that, you pay back that loan in monthly installments or in total, whichever is more convenient for you. So, if you have a luxurious trip in mind, a new car, or anything that requires a larger amount of money, getting a card can set you up for it.
These were our ideas for green luxury destinations, and there are and will be many more to see and experience. Green tourism is taking over just yet, so be ready for so much more. Travel safely and stay green!

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The US Securities and Exchange Commission (SEC) recently released a new rule resulting from a process intended to make companies disclose their climate-related financial risks to investors. But in trying to shape a regulation that would mollify opponents—largely industries responsible for the heat-trapping gases that cause climate change—the SEC failed to relieve investors of responsibility for determining how companies will fare in the clean energy transition.
The final rule that arrived in early March, almost two years after its introduction, reflects the tortured process that produced it. The document clocks in at 888 pages, twice the length of the already lengthy draft rule. A close reading reveals that nearly all the draft rule’s tenets were bracketed by legal caveats that greatly weakened it from what supporters had hoped. So, after years of asking for standardized information about a company’s exposure to climate-related risks, investors are left with a regulation of twice the length and half the weight.
The final version of the rule, titled Enhancement and Standardization of Climate-Related Disclosures for Investors, is an improvement over the status quo in some respects, but the bar was low. The rule was created in response to investor complaints that companies weren’t adequately disclosing information about climate-related financial risk. The SEC released guidance in 2010 directing public companies to disclose risks resulting from climate-related regulation, legislation, and weather, among other sources. Few companies did so, however, posing great risk to investors and the economy: A 2015 Union of Concerned Scientists report on the threat to oil refineries from rising sea levels found that investors and taxpayers will likely bear the costs of future disasters if companies fail to disclose and manage these risks.
On the plus side, companies will now have to disclose climate-related information such as global warming emissions and energy transition plans in annual SEC filings that they now report in annual reports and other public-relations products that often currently don’t contain standardized or comparable data. The range of information required for disclosure by the new rule sounds impressive: greenhouse gas (GHG) emissions produced by company operations and purchased from suppliers (known as Scope 1 and Scope 2 emissions); the financial impact of severe weather events; climate-related transition plans or risk analyses, which will hopefully discourage greenwashing; and the role of Renewable Energy Credits and carbon offsets in corporate transition plans.
Unfortunately, however, the requirements left out or weakened in the final rule were so integral to its effectiveness that some analysts—such as former SEC commissioner Allison Herren Lee—believe the rule is useless without them. The biggest of these is Scope 3 emissions, produced further down a company’s value chain and accounting for up to 90 percent of total emissions for high-emitting industries like oil and gas.
The SEC’s draft rule had directed companies to disclose Scope 3 emissions and document climate-related risk in the financial portion of SEC filings. Those steps had long been requested by investors. Yet both provisions were dropped from the final rule.
Why did that happen? In a word, lobbying. High-emitting industries and the trade organizations that represent them launched an all-out assault on the rule that drew heavily from old anti-regulatory and climate-denial playbooks, aided by newer crusades against sustainable investing. Oil and gas companies in particular groused about disclosing Scope 3 emissions and information about how climate-related risk would impact their strategies for the future. Groups like the Republican Attorney Generals’ Association threatened to challenge the SEC’s authority in court, even though the rule clearly falls within the SEC’s mandate to protect investors.
In response to these threats, the SEC attempted to create a Teflon-coated rule designed to slip past even the most anti-regulatory judge. The legal concept providing that grease is “materiality,” a word that occurs in the final rule more than 1,000 times—four times as often as it does in the draft rule. Materiality is a solid concept but tricky to define, somewhat like Judge Potter Stewart’s “I know it when I see it” definition of obscenity. The International Financial Reporting Standards Foundation—a nonprofit that sets global financial reporting standards—says information is material if “omitting, obscuring or misstating it could be reasonably expected to influence investor decisions.” The SEC says, in its 1999 guidance on materiality, that “a matter is material if there is a substantial likelihood that a reasonable person would consider it important” (italics mine).
Since the SEC’s job is to protect investors, you might think investors would decide what kinds of information is material. But the final rule ignores the fact that 97 percent of investors supported Scope 3 disclosures in their comments on the rule. Closer attention was apparently paid to voices such as Chevron’s board of directors, which said in its 2023 proxy statement that “reducing Chevron’s absolute Scope 3 greenhouse gas (“GHG”) emissions is not in stockholders’ interests, nor should it be Chevron’s responsibility.” In the end, decisions about whether climate-related risk is material will be largely left to corporate lawyers, who themselves are another major beneficiary of the rule.
All the materiality qualifiers failed to inoculate the rule from litigation: Within hours of its release, several Republican attorneys general had filed suit accusing the SEC of overstepping its mandate. Meanwhile, environmental groups Earthjustice, National Resources Defense Council, and the Sierra Club filed lawsuits stating the watered-down rule fails to fulfill the SEC’s mandate of protecting investors. As of this writing, nine lawsuits have been filed in six federal appellate courts opposing the rule.
Plaintiffs in one of the lawsuits—fracking companies Liberty Energy Inc. and Nomad Proppant Services LLC—successfully persuaded a judge in the notoriously conservative U.S. Court of Appeals for the Fifth Circuit to temporarily halt the rule on March 15, but the stay was lifted a week later when the cases were merged and transferred to the Eighth Circuit court in Missouri. The Eighth Circuit—also stacked with predominantly Republican appointees—was chosen by lottery after the SEC accused opponents of “forum shopping,” or filing with courts where judges are known to have anti-regulatory sympathies.
Many of the rule’s opponents are linked by a campaign to stop the global economy’s transition to clean energy. This effort is fueled by tens of millions in so-called “dark” money funneled through industry trade associations and front groups; well-established organizations such as the US Chamber of Commerce and American Petroleum Institute have also long worked to obstruct shareholder rights and attempts to regulate climate-related financial risk. These same actors are behind the misguided effort to demonize any evaluation of investments by environmental, social, or governance (ESG) factors as “woke” and bad for shareholders. Their tactics are on display in a lawsuit ExxonMobil brought against two investors who called on the company to reduce its Scope 3 emissions, as well as in subpoenas of some of the same groups filed by Republicans in Congress.
But investors are pushing back. Several major Chamber members, including Microsoft, Meta and Pfizer, have expressed their opposition to the organization’s climate obstruction. And CALPERS—the California employee pension fund, one of the country’s largest—has warned ExxonMobil that it will consider divestment if the company doesn’t drop its lawsuit against shareholders. “We don’t think it’s particularly helpful for companies to be suing the people who provide their capital,” CalPERS investment director Drew Hambly told the board at a March meeting.
Anti-ESG antics might make some political donors happy, but most of the guidance coming out of the financial industry tells companies that the future is here and it’s time to buckle up. Climate disclosure regulations are taking shape in markets around the world, such as the European Union, China, and Singapore. US states such as New York and Illinois are also preparing to launch disclosure requirements similar to those passed last year in California. The steady advance of these regulations is driven by shocking data about the immense costs investors will face if business and government don’t unite in bringing emissions down. Most vulnerable to climate-related financial risk are long-term investments most often associated with institutions like pensions, retirement funds and endowments, bringing the crisis home to many—if not most—people in the United States.
The final SEC rule is a long way from giving these investors the protection they deserve. Allowing reactionary politicians and judges to strip away what little protection the rule affords is unconscionable. The rule can be strengthened by additional enforcement and guidance in the years ahead—guidance that might even answer concerns of those critical of the rule, if they would engage with it. Despite what some might think, throwing up roadblocks against transparency doesn’t buy time for those critics. It wastes time we don’t have.
Posted in: Corporate Accountability
Tags: climate accountability, Climate risk disclosure, divestment, ESG investing, regulations, SEC
About the author
Laura Peterson is the corporate analyst and advocate for the accountability campaign in the Climate & Energy Program.
Carly Phillips
Research Scientist
Derrick Z. Jackson
Fellow
Laura Peterson
Corporate Analyst & Advocate
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William, left, employee at association Eco-Citoyen, and Larry, volunteer at association Eco-Citoyen, refill with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, left, employee at association Eco-Citoyen, and Larry, volunteer at association Eco-Citoyen, refill with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
A couple talk food in a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, employee at association Eco-Citoyen, refills with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
A woman takes food in a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, left, employee at association Eco-Citoyen, and Larry, volunteer at association Eco-Citoyen, refill with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, left, employee at association Eco-Citoyen, and Larry, volunteer at association Eco-Citoyen, refill with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, left, employee at association Eco-Citoyen, and Larry, volunteer at association Eco-Citoyen, refill with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, left, employee at association Eco-Citoyen, and Larry, volunteer at association Eco-Citoyen, refill with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
A couple talk food in a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
A couple talk food in a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, employee at association Eco-Citoyen, refills with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
William, employee at association Eco-Citoyen, refills with food a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
A woman takes food in a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
A woman takes food in a public refrigerator of project Free Go of the association Eco-Citoyen where people can give and take food that might otherwise perish, in Geneva, Switzerland, Friday, July 28, 2023. (Salvatore Di Nolfi/Keystone via AP)
GENEVA (AP) — That head of lettuce you forgot to make a salad with starting to wilt? Did you buy too much bread before heading out for a holiday?
In an effort to help eco-conscious consumers, a Geneva nonprofit is ramping up its rollout of street-side, free-access public refrigerators that restaurateurs, at-home cooks and others can use to give away food that’s about to go bad. It’s part of a bigger effort by communities in Switzerland and other European countries to do their part for the environment while helping to cut down on food waste.
The nonprofit Free-Go — whose name riffs off the word “frigo,” a colloquial French word for refrigerator — has rolled out refrigerators and pantry shelves in Geneva where passersby can grab fruit, vegetables, bread, croissants and other perishables to take home for free.
The program costs about $40,000 to run each year and enjoys the support from both charity groups and the city government. It launched a year ago with a single fridge outside a community center in western Geneva and it now has four fridges, strategically placed around town. A fifth one is planned before year’s end.
Marine Delevaux, the project’s director, says the deposited food is generally snapped up within an hour after delivery. For health and regulatory reasons, no frozen foods, open food containers, prepared meals or alcohol are allowed in the fridges.
Free-Go is also experimenting with scheduled pickups at apartment buildings to make it easier for residents to participate in the program. It has also set up a “hotline” that restaurateurs can use to call for retrieval of unused food.

“Generally, when the food collected from shops and restaurants arrives in the morning, people are already waiting to help themselves,” Delevaux said, adding that the first Geneva fridge helped save some 3.2 tons of food from going to waste last year. Of the food donated, only about 3% had to be thrown away because nobody wanted it.
Free-Go says contributors of food from the private sector — such as restaurants or food vendors — must make a commitment to ensure the donated food is safe to eat. Swiss law says food past the “recommended use-by date” can be consumed for up to a year afterwards, Delevaux said.
The Swiss government estimates that nearly one-third of all food products destined for consumption is wasted or thrown away needlessly — amounting to about 330 kilograms (about 730 pounds) of food waste per inhabitant each year. Of that, about 100 kilograms (about 220 pounds) are attributed to waste by households.
Free-Go says about 1 billion tons of food go to waste every year around the world — using up energy and other resources in the farming and transportation process.
“Wasting food is not only an ethical and economic issue but it also depletes the environment of limited natural resources,” the EU’s Commission says.
Similar food-sharing campaigns are in place in the capital, Bern, and in western Neuchatel, after the idea was imported from Germany.
According to Foodsharing.de, a community group in Germany that started more than a decade ago, more than a half-million people in Germany, Switzerland and Austria have made “the food-sharing initiative an international movement” and have helped save 83 million tons of food from going to waste.
Because the food is free, and the donations can vary, it’s uncertain what will turn up in the fridges — and some recipients might end up being disappointed.
Outside a community-center in a working-class area of Geneva on Friday, Shala Moradi, a 65-year-old housewife from Iran who has lived in Geneva for a decade, said she had been looking for some bread — and there was none. Yet, she says she appreciates the initiative.
“It’s very good. I can take strawberries, cherries, things like that,” she said. “The free (part): I like that too.”
Fresh off depositing some tomatoes from her vegetable garden, Severine Cuendet, a 54-year-old teacher, said “we have too much” and applauded the initiative “because this neighborhood has a lot of need.”
“And it happens to all of us to buy too much,” she added with a smile.
Geir Moulson and Kirsten Grieshaber contributed from Berlin.
Copyright 2024 The Associated Press. All Rights Reserved.

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Scientists continue to document many effects of climate change on freshwaters, estuaries, and marine environments, and those effects are predicted to be exacerbated in the future. These effects, along with nutrient pollution, might cause algal blooms to become more severe and to occur more often in more waterbodies. Blooms with the potential to harm human health, pets, livestock, or aquatic ecosystems are referred to as harmful algal blooms, or HABs, and they can also have wide ranging economic impacts. 
In freshwaters, cyanobacteria (microscopic photosynthetic bacteria previously known as blue-green algae due to their color) are the most common HAB producers. Some cyanobacterial HABs, or cyanoHABs, produce toxins that cause illness in humans and other animals.
HAB forming cyanobacteria thrive in warm, slow-moving water, and typically occur when water temperatures are warmer. As a result, increases in water temperature with climate change are expected to increase the magnitude and duration of cyanoHABs. Warmer water temperatures favor cyanoHABs in several ways, including:
In some regions, climate change is predicted to reduce freshwater runoff due to drought and increased evaporation. These effects will combine to increase salinity in inland waters. This can impact irrigation, harm crops, contaminate drinking water, and allow the invasion of salt-tolerant and marine algae to inland lakes. In the southwestern and south-central United States, more salt-tolerant algal HABs known as “golden algae” have regularly recurred, expanded into lagoons, and killed fish in freshwater lakes since 2000.
In other regions, increased runoff from more intense rainfalls may increase freshwater flows to coastal areas. This freshwater sits on top of saltwater because it is lighter. This can prevent mixing of oxygen rich surface water with deeper layers where the oxygen has been removed by the decomposition of HABs after they die and sink to the bottom layer. These low oxygen (or hypoxic) zones harm or kill animals that require oxygen to live, including many commercially important species.
Algae need carbon dioxide to survive. Higher levels of carbon dioxide in the air and water can lead to rapid growth of algae, especially cyanoHABs that can float to the surface of the water and use the increased carbon dioxide. Increased levels of carbon dioxide also increase the acidity of the water, which affects competition among algal species and impacts the organisms that graze on algae. These effects can combine to increase the competitive advantage of HAB species.
Climate change is affecting rainfall patterns, increasing both rainfall intensity and the duration of drought. Increased rainfall causes higher nutrient runoff from land into waterbodies fueling HABs like those observed in Lake Erie in 2011 and 2015. If followed by extended drought, waterbodies can retain those nutrients for longer, favoring HAB species that compete better under higher nutrient conditions.
Moreover, increases in extreme rainfall and subsequent increases in freshwater flows may flush large loads of nutrients, freshwater HABs and their associated toxins into estuaries and marine areas. As a result, estuarine and marine waters may be at greater risk of developing HABs or seeing an exacerbation of existing HABs.
Scientists predict that sea level could rise one meter by the year 2100. This would increase the area of shallow, stable coastal waters that provide more favorable conditions for HABs.
Coastal upwelling occurs when wind pushes warm, surface water offshore and deep, nutrient rich waters rise to replace it. Climate change is expected to alter the timing and intensity of coastal upwelling. Along the west coast of the United States, excess nutrients delivered by upwelling might lead to more algal blooms, including HABs, especially when combined with increased runoff of nutrient pollution from the land.

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Mountain ranges contain high concentrations of endemic species and are indispensable refugia for lowland species that are facing anthropogenic climate change^1,2. Forecasting biodiversity redistribution hinges on assessing whether species can track shifting isotherms as the climate warms^3,4. However, a global analysis of the velocities of isotherm shifts along elevation gradients is hindered by the scarcity of weather stations in mountainous regions⁵. Here we address this issue by mapping the lapse rate of temperature (LRT) across mountain regions globally, both by using satellite data (SLRT) and by using the laws of thermodynamics to account for water vapour⁶ (that is, the moist adiabatic lapse rate (MALRT)). By dividing the rate of surface warming from 1971 to 2020 by either the SLRT or the MALRT, we provide maps of vertical isotherm shift velocities. We identify 17 mountain regions with exceptionally high vertical isotherm shift velocities (greater than 11.67 m per year for the SLRT; greater than 8.25 m per year for the MALRT), predominantly in dry areas but also in wet regions with shallow lapse rates; for example, northern Sumatra, the Brazilian highlands and southern Africa. By linking these velocities to the velocities of species range shifts, we report instances of close tracking in mountains with lower climate velocities. However, many species lag behind, suggesting that range shift dynamics would persist even if we managed to curb climate-change trajectories. Our findings are key for devising global conservation strategies, particularly in the 17 high-velocity mountain regions that we have identified.
Paul R. Elsen, William B. Monahan & Adina M. Merenlender
Andrew J. Suggitt, Christopher J. Wheatley, … Alistair G. Auffret
Dennis Rödder, Thomas Schmitt, … Jan Christian Habel
Mountainous regions represent 25% of Earth’s land surface and are rich in biodiversity, owing in part to their steep climatic gradients and complex topography^1,2. The assumption that mountain species are responding faster to anthropogenic climate change through rapid upward range shifts leading to potential mountaintop extinctions has attracted extensive research^3,4,7,8,9. Whether species are closely tracking the rate of climate warming is assessed chiefly by comparing the velocities of species range shifts with the velocities of climate change; that is, the rates at which isotherms move through the geographical space^3,4,10,11,12. Past studies that assessed climate velocities have focused mainly on horizontal velocities, in km per year; that is, how fast isotherms are moving along the latitudinal and longitudinal clines of the horizontal plane (see the seminal work from Loarie et al.¹² for terrestrial systems; this was then applied to marine systems by Burrows et al.¹³). Because isotherms are located closer to one another in mountainous regions, horizontal velocities of isotherm shifts are much slower and potentially omnidirectional in mountains, whereas they are much faster and oriented mainly poleward in the lowlands¹³. However, we know that climate warming also causes terrestrial species to shift along mountain slopes and thus not only horizontally but also ‘vertically’ when projected along elevation gradients—moving at very different speeds (usually expressed in m per year), and mainly upward but sometimes downward^3,14,15. Despite this knowledge, global maps of the velocities of isotherm shifts projected along the vertical dimension of elevational clines in mountain regions still do not exist. This shortfall stems partly from the complex topography and the scarcity of weather stations in most mountain ranges globally^5,16, which makes it difficult to accurately measure vertical velocities of climate change in mountain regions worldwide. Therefore, it is still an open question whether mountain species better track isotherm shifts vertically in elevation rather than horizontally in latitude.
Because we still lack global maps of the velocities at which isotherms are shifting vertically along elevation gradients as the climate warms, most local studies compute a rough estimate of this vertical projection of climate velocities by relying on a constant lapse rate of temperature (LRT). The LRT is defined here along mountain slopes as the normalized temperature difference at approximately 2 m above ground level between a low-elevation and a high-elevation weather station and thus it differs from a sensu stricto vertical lapse rate measured above a single geographical position. According to the laws of thermodynamics⁶, the LRT is 9.8 °C per km in the case of dry air^1,6. Nonetheless, given that Earth’s atmosphere is not entirely dry, the LRT experienced by terrestrial organisms in reality will be less steep than 9.8 °C per km. Because of that, most studies that have compared the observed velocities of species range shifts along elevation gradients with the velocities of climate change inside a given mountain range inferred the vertical shift of isotherms by relying on a constant rate of 5.5 °C per km for the LRT¹¹—a constant that is borrowed from limited ground observations concentrated in Europe^7,17. Using this fixed rate, one can assume that if the temperature increases by 1 °C over a given period of time, then it is expected that isotherms will move upslope by about 181.8 m during that same period, which gives a vertical velocity that varies depending only on the magnitude of temperature change per unit of time. However, the LRT is not constant and varies across elevation gradients among mountain ranges as well as within a single mountain range^18,19,20,21. For instance, by using long-term climatology (30-year means) from 269 weather stations in northern Italy, 205 in the Tyrol area and 166 in the Trentin–upper Adige region, covering a wide range of elevations, one study²¹ found that the annual mean of the LRT ranges between 5.4 and 5.8 °C per km in the Alps. In the southeastern Tibetan Plateau, the LRT was estimated²² to reach 8.5 °C per km. This large variation in the LRT partly stems from water vapour pressure because if the air condenses moisture as it cools—for example, in cloud forests—it gains some heat from condensation, which slows the cooling rate. Thus, moisture and surface temperature generate spatial variability in the LRT and consequently also generate spatial variation in the velocities at which isotherms may shift along mountain slopes as the climate warms by a given amount of temperature increase. Assessing mountain climate velocities by explicitly considering the determinants of the LRT is a crucial step in improving our understanding of species range shifts under anthropogenic climate change. Here, instead of relying on a constant LRT value of 5.5 °C per km in the Alps or of 8.5 °C per km in the Himalayas, we propose two different methods to map the spatial variation in the LRT, and we generate more meaningful estimates of the vertical velocities of isotherm shifts in mountain systems worldwide. First, we use satellite observations of land surface temperatures at fine spatial resolution to compute a satellite-derived version of the LRT (SLRT), based on local slope estimates of the relationship between temperature and elevation (Fig. 1a and Extended Data Fig. 1); and second, we use a more mechanistic approach based on the moist adiabatic LRT (MALRT), building on the laws of thermodynamics⁶ (Fig. 1c and Extended Data Fig. 2a,b). By combining information on the spatial variation of the SLRT and the MALRT at relatively fine spatial resolution worldwide with data on the magnitude of temperature change over time per spatial unit, we then compute maps of the vertical velocities of isotherm shifts in mountain systems: one that is based on satellite observations (SLRT); and one that mechanistically accounts for water vapour pressure conditions (MALRT). These two global maps of the vertical velocities of isotherm shifts in mountain regions are also compared to a third naive map that is based on a constant LRT of 5.5 °C per km. By using these global velocity maps, we subsequently identify the mountain regions with the highest vertical velocities of isotherm shifts in the world, and we quantify the variation in velocity values along several elevation gradients worldwide. Finally, we relate those vertical velocities of isotherm shifts, in m per year, to empirical observations of species range shifts, also in m per year, along several elevation gradients in mountain systems worldwide.
a, An example mountain range in Taiwan with a series of elevation transects, in red, defined by the highest peak at one end of the gradients and several foothills and valleys at the other end of the gradients. The background raster layer depicts the mean elevation (in m above sea level) for each spatial unit of 0.05° (around 5 km at the equator) resolution. Details can be found in the Methods and in Extended Data Fig. 1. b, Global map of the SLRT, generated at 0.5° (around 50 km at the equator) resolution across all mountain regions worldwide (except Antarctica) using satellite observations from 2011–2020. c, Three-dimensional plot showing the effect of mean annual temperature and mean annual water vapour pressure on the absolute magnitude of the MALRT (in °C per km). d, Global map of MALRT, generated at 50-km resolution across all mountain regions worldwide (except Antarctica) using climatic data from 2011–2020. Note that the colour scheme does not show the full range of data to prevent highly skewed visualization driven by extreme outliers.
Source Data
We found that there was very large spatial variation when mapping the lapse rate at a global extent (Fig. 1), either from satellite observations (SLRT; Fig. 1b) or from the laws of thermodynamics (MARLT; Fig. 1d), with values ranging (at the 5th and 95th percentiles) from −5.14 to 8.45 °C per km and from 2.94 to 8.09 °C per km, respectively. Although the two global maps show a certain degree of spatial agreement (Supplementary Results), the SLRT shows much shallower lapse rates than does the MALRT in mountain regions that are located at higher latitudes, such as in northeastern Siberia, Alaska and northwestern Canada (Fig. 1b,d). The mountain regions showing the steepest lapse rates are located in the Himalayas, with values that are very consistent with the values recently reported for the southeastern Tibetan Plateau, which range between the values of free-air dry (10 °C per km) and moist (6.5 °C per km) adiabatic lapse rates²². For comparison purposes and external validation, we also extracted data from the Global Historical Climatology Network²³, focusing on empirical field data recorded by weather stations situated in mountain regions worldwide. We manage to obtain temperature lapse rates from 144 weather stations (station-based LRT; see Methods) across a total of 48 mountain sites from 2011 to 2019 (Extended Data Fig. 3a). This validation exercise confirms that there are very few mountain regions worldwide in which the network of weather stations is dense enough along mountain slopes (n > 2) to compute the LRT. Nevertheless, we found a positive relationship between the station-based LRT calculated from these very limited networks of weather-station data and our computations of the MALRT (linear regression, F_{1, 46} = 5.54, p = 0.02, R² = 0.108, n = 48, Extended Data Fig. 3a). By contrast, the relationship between the SLRT and the station-based LRT did not reach statistical significance (linear regression, F_1,46 = 0.774, P = 0.38, R² = 0.017, n = 48; Extended Data Fig. 3b). Owing to the relative scarcity of weather-station data and the fact that these data are concentrated mainly in North America and Europe, our subsequent analyses will focus solely on our computations of the MALRT and the SLRT.
After combining maps of the spatial variation in the LRT with data on the rate of temporal changes in mean annual temperature (Extended Data Fig. 2c), we found notable differences in the vertical velocities (in m per year) of isotherm shifts depending on the approach we used (Fig. 2), with the constant LRT-based and MALRT-based estimates generally yielding conservative climate velocities and the SLRT-based climate velocities showing the greatest variability. Velocity values for the SLRT-based map ranged from highly negative (−26.01 m per year; at the 5th percentile) to highly positive (34.08 m per year; 95th percentile) (Fig. 2g–i). By contrast, the MALRT-based map shows velocity values ranging (at the 5th and 95th percentile) from 1.81 m per year to 10.83 m per year. When we combined the SLRT-based velocity map with the MALRT-based velocity map to reach a consensus map on the mountain regions most threatened by climate change (Methods and Fig. 3a,b), we found that 32% of the surface area covered by mountains worldwide, Antarctica excluded, is exposed to high vertical velocities of isotherm shifts that exceed the 80th percentile by either the MALRT (80th percentile: 8.25 m per year; Fig. 3) or the SLRT (80th percentile: 11.67 m per year; Fig. 3). We delineated 17 mountain regions that are partly exposed to high vertical velocities, including those in the Alaska–Yukon region, western America and Mexico, Appalachia, the Brazilian highlands, Greenland, Scandinavia, the Mediterranean basin, southern Africa, the Ural mountains, the Iran–Pakistan region, the Putorana mountains, Mongolia, northern Sumatra, the Kodar mountains, Yakutiya, northeast Asia and Kamchatka (Fig. 3c and Supplementary Data 1). Intuitively, higher rates of warming lead to higher vertical velocities of isotherms shifting faster along elevation gradients. This is the case chiefly in dry regions with a low water vapour pressure, such as Greenland, the Putorana Plateau in northern Siberia, Kamchatka, Mongolia and the Alaska–Yukon region—owing probably to the limited heat capacity of these arid areas^24,25 (Fig. 3d). In addition, by relying on laws of thermodynamics, we can also anticipate that regions with higher surface temperatures and/or higher water vapour pressure might also generate high vertical velocities because of shallower lapse rates: isotherms will shift faster along such elevation gradients for the same amount of temperature change over time. Notably, these regions are not necessarily those showing significant surface warming over time. For instance, northern Sumatra, the Brazilian highlands, southern Africa and Iran–Pakistan are typical representatives of such shallow lapse rates with little surface temperature increase (Fig. 3c,d). These are mountain regions threatened by high vertical velocities of isotherm shifts that have been difficult to detect in the past by surface temperature change alone, and thus are particularly worthy of further investigation.
a–i, Vertical velocities of isotherm shifts (m per year) in mountain regions worldwide using a constant LRT (a–c), the MALRT (d–f) or the SLRT (g–i) (1971–1980 versus 2011–2020). b,e,h, Normalized value from the corresponding panel (a,d,g) to show clear spatial variation in each panel. c,f,i, Histograms of the velocity values across all mountain regions for the constant LRT, the SLRT or the MARLT, respectively. Note the log₁₀ scale for the histogram displaying the range of velocity values for the SLRT. The SLRT values were rescaled using the function sign(x) × log₁₀(abs(x) + 1) to ensure that the shifting direction is preserved and to avoid interference from the value range of logarithmic transformation. Black dashed lines indicate the median; yellow solid lines show the 80% quantile; red solid lines show the 90% quantile. The corresponding values are labelled above. Note that the colour scheme does not show the full range of data to prevent highly skewed visualization driven by extreme outliers.
Source Data
Consensus map of the vertical velocities of isotherm shifts as estimated from the SLRT or from the MALRT (see Fig. 2). a–c, Mountain regions in which velocities are greater than the 80% quantile (that is, retaining 20%) in the calculation of either the MALRT or the SLRT are labelled as critically threatened (a,b) and displayed in red (c). d, Orange points and segments represent the mean annual temperature change between the periods 1971–1980 and 2011–2020; blue bars represent the mean water vapour pressure during 2011–2020 for each of the 17 mountain regions affected by relatively fast vertical velocities of isotherm shifts. Error bars represent s.d. See Supplementary Data 1 and ‘Data availability’ for a comprehensive breakdown for each region, including sample size information. Considering that near-zero SLRT values result in extremely high climate velocity, we removed 1% outliers that are close to zero in c. Data with alternative levels of outlier removal (0.5%, 2% and 5%) are shown in Supplementary Fig. 2. Supplementary Data 3 provides a high-resolution map.
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We further compared the effects of high warming rates and steep temperature lapse rates, which act as compensatory effects on climate velocities, between arid and more humid regions. We found that in arid mountain regions with a low water vapour pressure, the temperature lapse rate accounts for 3.6% of the observed variation in climate velocity, whereas changes in surface temperature account for 96.4% of the observed variation, on the basis of the random forest analysis we performed. A detailed analysis using the Shapley value further revealed that steeper lapse rates have a smaller negative effect on climate velocities compared with higher warming rates, which increase climate velocities (Extended Data Fig. 4a). In humid regions, the temperature lapse rate accounts for 11.32% of the observed variation in climatic velocity, whereas changes in surface temperature explain 88.68% of the observed variation, on the basis of the random forest analysis we performed. The Shapley value analysis showed that steeper lapse rates still have a smaller negative effect on climate velocities than do higher warming rates (Extended Data Fig. 4b). Of note, the explanatory power of the lapse rate in wet mountains is nearly four times higher than it is in arid mountains. This difference is likely to be due to the lower magnitude of the surface temperature increase in wetter mountains (Extended Data Fig. 4c,d). Although the explanatory power of the lapse rate is, in general, relatively much lower than that of the warming rate, the striking differences that we found between arid and humid regions, in terms of the relative importance, affects the spatial variation that we report in the vertical velocity of isotherm shifts.
Focusing on the MALRT-based velocity map, we found a complex pattern of elevation-dependent velocities for isotherm shifts (also known as climate velocities; Fig. 4), with the highest vertical velocities of isotherm shifts being concentrated at low elevations. This was especially the case in the Northern Hemisphere and at a latitude of 20–30° S in the Southern Hemisphere, whereas the lowest vertical velocities were located at high elevations in the Himalayas and the Andes. Statistical results indicate that isotherm velocities are significantly higher at lower elevations (slope: −0.285 m per year∙km, degrees of freedom (df) = 12,028, t = −4.243, P < 0.001) and higher absolute latitudes (slope: 0.048 m per year∙deg, df = 12,028, t = 24.163, P < 0.001) in the Northern Hemisphere, whereas the magnitude of the effect significantly changed in the Southern Hemisphere (P < 0.001 for all interaction terms composed of elevation, latitude and hemisphere; see Methods). In the Southern Hemisphere, the elevational effect is stronger with a more negative slope estimate (slope: −1.178 m per year∙km), but the latitudinal effect was completely reversed compared with the Northern Hemisphere (slope: −0.040 m per year∙deg). The reversed latitudinal effect we detected here is likely to be due to the reduction of land area towards higher absolute latitudes in the Southern Hemisphere, where oceans predominate over landmasses, leading to a relatively higher water vapour pressure (Extended Data Fig. 2b) and consequently a lower temperature rate (Extended Data Fig. 2c). We further analysed the effects of changes in surface temperature and the MALRT on the rates of isotherm shift with elevation (Supplementary Fig. 1). We found no significant linear correlation between the rate of surface temperature change and elevation when the effect of latitude was statistically controlled. However, the MALRT becomes steeper with increasing elevation, leading to lower vertical velocities of isotherm shifts at higher elevations compared with lower elevations (that is, a steeper MALRT corresponds to lower vertical velocities of isotherm shifts). On islands in the Northern Hemisphere, we found higher vertical velocities of isotherm shifts (7.46 ± 2.33 m per year) exceeding, on average, the mean vertical velocity we found across all main continents in the Northern Hemisphere (6.29 ± 2.61 m per year; Fig. 4d,e; df = 3, F = 352.9, P < 0.001). These results suggest that mountain islands in the Northern Hemisphere are even more threatened by the effects of climate change than are mountains on the mainland, and this poses a high threat to island biodiversity given that mountain islands have many endemic species^26,27. However, mountain islands in the Southern Hemisphere do not show vertical velocities of isotherm shifts that are as high as those in the Northern Hemisphere (Fig. 4e).
a, Mean climate velocity of mountains worldwide. Mountain summits are labelled for reference. b,c, The corresponding s.d. (b) and sample size (c) for a. d, Mean climate velocity of mountain islands. The s.d. and sample size for d can be found in Supplementary Fig. 3. The colour legend in d is the same as in a. e, The comparison between mainland and islands in the Northern and Southern hemispheres relies on ANOVA and post-hoc Tukey HSD tests. Other than the P = 0.002 between Southern Hemisphere mainland (S. Mainland) and Southern Hemisphere island (S. Island) (by Tukey HSD test), P < 10⁻¹⁶ is shown in all statistics (labelled as ***). The centre line of the box plot represents the median; box limits, upper and lower quartiles; whiskers, 1.5 times the interquartile range. The sample sizes for S. Mainland, S. Island, Northern Hemisphere mainland (N. Mainland) and Northern Hemisphere island (N. Island) are 1,222, 199, 10,331 and 284, respectively. f, Observed species range shifts against the vertical velocities of isotherm shifts. Areas labelled as ‘not applicable’ (in grey) denote instances in which the number of records in a taxonomic group falls below the stipulated minimum (in this case, 30) required to conduct a meaningful statistical comparison to the predicted environmental climate velocities. g, The different probabilities of species tracking climate velocities under a P = 0.05 threshold. Only mean values are shown. Upward and downward shifts are shown together with their absolute values. For results based on different P value thresholds, see Extended Data Fig. 6d,e. A total of 83 taxon–region pairs are plotted. Each plot represents 1 to more than 400 raw data points. See Extended Data Fig. 6b,c for details and Supplementary Fig. 4 for raw data points. All statistics used a two-tailed approach without adjustment for multiple comparisons.
Source Data
Next, we used our estimates of the vertical velocities of isotherm shifts in mountains and linked them to empirical data on the velocities of species range shifts along mountain slopes. We used a carefully curated dataset—BioShifts⁴—which provides the vertical velocities of species range shifts (in m per year along elevation gradients) per taxonomic unit after standardizing the raw range shift estimates reported by authors in their original studies. Because our analysis shows that the MALRT has a much greater explanatory power for predicting the velocities of species range shifts than does the SLRT (Supplementary Results and Extended Data Fig. 5), we report only on the relationship between the velocities of species range shifts along elevation gradients and the vertical velocities of isotherm shifts in mountains as calculated by the MALRT. Indeed, the Akaike information criterion (AIC) values from our models are 35,887, 37,016 and 51,398 for the MALRT, constant LRT and SLRT, respectively, ranking from best to worst in terms of model fit. This discrepancy between the MALRT and the SLRT is likely to be due to the fact that the satellite (MODIS) data measure the actual land surface temperature, which is influenced by microscale surface properties such as albedo, emissivity, rock type and vegetation cover. Hence, for the SLRT, the calculated lapse rate is characterized by considerable noise. Moreover, the SLRT data are available mainly in cloud-free conditions, which intensify these spatial variations. As a consequence, satellite data present several limitations, and thus have a limited capacity to explain species range shifts compared with insights obtained from theoretical calculations of the MALRT. Comparing the vertical velocities of isotherm shifts based on the MALRT with the observed rates of species range shifts, the probability that a given taxonomic unit tracks the vertical velocities of isotherm movements decreases sharply with increasing absolute velocities of isotherm shifts (Fig. 4f,g). Thus, we found that species seem to track climate change only at lower velocities along the elevational gradients, irrespective of the taxonomic group (Fig. 4g, Extended Data Fig. 6d,e and Extended Data Fig. 7). These results reveal the potentially catastrophic effects of rapid climate change on mountain biodiversity. Although the MALRT will probably undergo changes over time owing to temporal variations in the spatial distribution of temperature and water vapour along elevation gradients, it is important to note that the effects resulting from a shallow MALRT are expected to be worrisome.
Our assessment of mountain climate velocity yields a mechanistic understanding of the variability in mountain climate change globally. The thermodynamic theories of the MALRT, which consider water vapour and latent heat release, suggest that threats to mountain biodiversity can occur in the absence of rapid surface warming. As our range shift analysis shows, species are unlikely to track isotherms quickly enough to match the high velocities at which isotherms are moving along some elevation gradients. Our results suggest that the vertical distance between isotherms in mountains is a crucial factor driving species migration. Likewise, on the basis of thermodynamic theory, colder and drier conditions at higher elevations make temperature lapse rates steeper, which, in turn, leads to a contraction of the vertical distance separating isotherms (that is, isotherm spacing contracts when projected on the vertical axis), generating lower vertical velocities of isotherm shifts. This suggests that in many mountain regions, the vertical shift of isotherms decreases with increasing elevation. From the perspective of isotherms shifting upslope owing to warming, higher elevations will experience a slower rate of isotherm shift, meaning that organisms can reach habitats with suitable temperatures by moving shorter vertical distances. However, a steeper temperature lapse rate also means that the environment changes more rapidly with elevation. Therefore, in the case of mountains with a broader base and narrower peaks²⁸, warming might result in a reduction of habitat area for organisms. Because the shape of a mountain affects the amount of habitat available to organisms²⁸, understanding the velocity of climate change, as well as quantifying the suitable habitat area under warming conditions, will be essential for understanding the effects of climate change on mountain biodiversity.
Moreover, our findings suggest that all taxonomic groups will be similarly affected in their abilities to track isotherms along mountain slopes. Considering that the distance of climate tracking is several orders of magnitude shorter in elevation compared with latitudinal gradients, the moving capability of organisms is less likely to be the key constraint in mountain systems. Mountainous regions, with their complex topography, occupy a relatively smaller proportion of landmasses compared with other terrains in the lowlands²⁸. As described above, the available habitat area for organisms in mountain regions is influenced by the shape of the mountain, and many mountains exhibit a reduction in area with increasing elevation. This, combined with biotic interactions such as interspecific competition^29,30, might collectively limit the ability of mountain species to track isotherm shifts in the future. Mountains that we identified as facing high risks under climate change are particularly threatened by biotic attrition¹⁷, biotic homogenization³¹, population extirpation^32,33,34 and changing ecosystem properties³⁵. Many of these mountains are located in biodiversity hotspots (for example, Sundaland, Irano-Anatolia, southern Africa, the Mediterranean basin, the Atlantic forest, Mesoamerica, the California Floristic Province and Japan)^36,37, reinforcing the need to develop climate-change adaptation strategies for the conservation of mountain biota. Other climatic drivers and mechanisms such as precipitation, snow albedo, radiation flux variability, aerosols and land-use changes can also influence energy balance regimes and further mediate mountain climates^5,38,39. Despite many efforts to collect data on species range shifts in mountainous regions, the vast majority of data on species range shifts are still concentrated in Europe and North America⁴. This also creates uncertainty in assessing the biological effects of climate change at a global extent.
We emphasize that our results are crucial for assessing the vulnerability of mountain regions to climate change globally. By integrating surface temperature and water vapour pressure data with a thermodynamic model, we are able to make effective qualitative comparisons of global lapse rates and identify regions with comparatively higher or lower climate velocities. In particular, this approach enhances the explanatory power of our methodology over other existing methods (such as satellite data analysis) for assessing global species range shifts. However, it is important to recognize that our thermodynamic model still suffers from a low predictive accuracy when compared with field measurements of temperature lapse rates, and we cannot accurately quantify local-scale lapse rates solely on the basis of thermodynamic models. This highlights the need for refined mountain meteorological networks along elevational gradients to improve our holistic understanding of the processes that underlie local temperature lapse rates along mountain slopes. Furthermore, some studies have shown that changes in precipitation patterns can affect the range shifts of mountain species^15,40, but historical data on precipitation patterns along mountain slopes are extremely scarce compared with data on temperature lapse rates. For that reason, establishing weather stations that also monitor precipitation patterns along mountain slopes remains key for assessing the large-scale effects of precipitation changes on mountainous organisms. We call for the establishment of networks to monitor climate change and its effects in mountain biodiversity hotspots, especially in mountains that are threatened by high velocities of isotherm shifts, such as those we have identified in our study.
Before producing global maps of the vertical velocities of isotherm shifts across mountain regions worldwide, we first had to compute global maps of the LRT. To do this, and as well as using a constant LRT for comparison purposes, we used two different approaches for mapping the LRT. On the one hand, we used a statistical or correlative approach relying on satellite observations (SLRT). On the other hand, we used a more mechanistic approach that relies on the laws of thermodynamics to account for the effect of air moisture (MARLT). Please note that all statistical tests were performed using a two-tailed approach.
In assessing the SLRT, we focused on daily land surface temperature data from the MODIS Land Surface Temperature and Emissivity (MOD11C3) product⁴¹. These data, encompassing the period 2011–2020 and featuring a native spatial resolution of 1 km at the equator, were averaged from both daytime and night-time observations. Monthly mean values from this product were aggregated at an annual resolution to derive the mean annual temperature, which was subsequently averaged over the 2011–2020 decade. To harmonize the spatial resolution for subsequent computations with other gridded products relying on the Climate Research Unit (CRU) Time-Series (TS) 4.05 data, the MODIS data were aggregated, using the mean value, from their native spatial resolution to a 0.05° resolution (Extended Data Table 1), which is approximately 5 km at the equator, ensuring that there were ample grid cells for subsequent analyses. Using a moving window centred on a grid cell of 0.5° resolution, which is about 50 km at the equator, elevational transects were derived to empirically compute the LRT from satellite observations. This involved pinpointing regional peaks and foothills in a 1.5° by 1.5° window centred on the target grid cell of 0.5° resolution, with elevation data sourced from a digital elevation model (DEM) that was aggregated to match the 0.05° resolution of the aggregated MODIS grid (Extended Data Fig. 1a). From these peaks and foothills, elevational transects connecting the nearest topographical features were established (Extended Data Fig. 1b,c). Linear regressions between mean annual temperature and elevation, both at the 0.05° resolution, were subsequently fitted for each transect intersecting the target 0.5° grid cell (Extended Data Fig. 1d–f). All pixel units intersected by a focal transect were considered, even if only marginally. Transects yielding significant lapse rates (R² ≥ 0.5 and P ≤ 0.05) were retained, with the slope coefficient (β) representing the SLRT value in °C per m (later converted to °C per km). If more than ten transects intersected a target 0.5° grid cell, the median SLRT value was calculated to mitigate biases from transect count extremities. Within the framework of our SLRT computations, the median transect count per grid cell was 8, with an interquartile range of 12 (Extended Data Fig. 8a,b). We noticed that a higher transect availability in a grid cell was correlated with increased average R² values between temperature and elevation (R² = 0.16, P < 0.001; Extended Data Fig. 8c), underscoring the dependency of the reliability of the SLRT on the number of accessible transects.
To compute the MALRT, we extracted monthly mean temperature and monthly mean water vapour pressure data from the gridded CRU TS4.05 database (at 0.5° spatial resolution), covering the decade 2011–2020 to match the time period covered by satellite observations (see ‘Assessing the LRT through satellite observations’). In the CRU TS4.05 dataset, both monthly mean temperature and monthly mean water vapour pressure were derived from local weather stations and processed to obtain the final values^42,43. The MALRT of each grid cell was computed using the following formula:
where Г_w is the moist adiabatic lapse rate in Kelvin per metre, g denotes Earth’s gravitational acceleration (9.8076 m per s²), H_v denotes the heat of vaporization of water (2,501,000 J kg⁻¹), R_sd denotes the specific gas constant of dry air (287 J kg⁻¹ K⁻¹), ϵ denotes the dimensionless ratio of the specific gas constant of dry air to the specific gas constant for water vapour (0.622), C_pd denotes the specific heat of dry air at constant pressure (1,005 J kg⁻¹ K⁻¹) and T denotes the air temperature (K). The parameter γ is the mixing ratio of the mass of water vapour to the mass of dry air:
where e represents the water vapour pressure of the air and p represents the pressure of the air. Here, p was derived from the barometric formula (see Supplementary Methods).
The processing of climatic variables (from monthly data to annual data) was done using Python v.3.7.9. Note that the original MARLT values, expressed in Kelvin per metre, were subsequently transformed into °C per kilometre for comparative purposes with the SLRT and the constant LRT. The increase in mean annual surface temperature and mean annual water vapour pressure both cause a decrease in the MALRT (see Fig. 1c).
For comparison purposes, the same approach was also applied to the datasets available from the ‘Climatologies at high resolution for Earth’s land surface areas’ data (CHELSA v2.1)⁴⁴ after the datasets were aggregated from the native spatial resolution at 1 km to 0.5° spatial resolution, using the mean value. Note, however, that the data are available only for the period 2011–2019 and do not entirely cover the 2011–2020 decade. Information on water vapour is not available in CHELSA, so water vapour was derived by multiplying relative humidity and the saturated water vapour obtained by applying the Clausius–Clapeyron equation⁴⁵. This derived MALRT using CHELSA data shows high consistency with that derived from the CRU dataset, with the strength of the correlation varying slightly depending on the elevation band considered (ranging from 0.79 to 0.96, P < 0.001; Extended Data Fig. 9).
To assess the vertical projection of the velocities at which isotherms are moving along elevation gradients in mountain regions as the climate is warming globally, we combined information on the spatial variation, at 0.5° spatial resolution, of the LRT, assessed through either the SLRT or the MALRT method, with data on the rate of temperature change over time per spatial unit. For computing the temporal rate of temperature change per spatial unit of 0.5° resolution, we used mean annual temperature time series from the gridded CRU TS4.05 dataset covering the period 1971–2020. More specifically, for each spatial unit of 0.5°, we first averaged the mean annual temperature for the periods 1971–1980 versus 2011–2020 before computing the difference between the two and dividing this difference by the time duration in years (40 years), so that the magnitude of temperature change was expressed in °C per year. The gridded layer of temporal changes in mean annual temperature between 1971–1980 and 2011–2020 was subsequently divided by the gridded layer of either the SLRT or the MARLT, expressed in °C per km, such that the vertical projection of velocity values on a map is expressed in km per year. For further comparison with the velocities of species range shifts, usually reported in m per year, we multiplied the vertical velocity map by 1,000 so that the unit is in m per year. Finally, we also generated a map of the vertical velocities of isotherm shifts in mountain systems using a constant LRT of 5.5 °C per km to be used as a control for what is usually done in the scientific literature to compute the vertical velocities of isotherm shifts in mountains^11,46,47.
To validate our maps of the SLRT and MALRT, we used an external dataset of the LRT along elevation gradients by relying on field observations from local weather stations. We extracted time series of monthly temperature data from several weather stations belonging to the Global Historical Climatology Network that extend to 2019 (ref. ²³). First, we selected weather stations covering the period 2011–2019: (1) when more than eight years of data were available; and (2) only if more than 10 months were recorded per year. Then, to match our gridded LRT values with station-based LRT values, we selected only the weather stations that are located within or in the vicinity of each grid cell belonging to a given mountain region. In particular, we collected data from the weather stations located within the central grid itself along with weather stations located within the eight adjacent grid cells, forming a nine-cell cluster, which we term a ‘mountain site’, within a mountain region, for ease of reference. Mountain sites that included at least three weather stations at different elevations were used for the computation of the station-based LRT. After excluding two extreme outliers from the set of station-based LRT values we computed, we ran two separate linear models (with two-tailed statistical tests) to assess the relationship between station-based LRT values (the response variables) and either MALRT or SLRT values as separate explanatory variables.
We can use the climate velocities calculated above, which carefully consider the spatial heterogeneity that affects the LRT, to determine which mountains around the globe are threatened by the highest velocities of isotherm shifts as a surrogate of the vulnerability risk for mountain biota as climate warms. We simultaneously considered both the MALRT- and the SLRT-based approaches (Fig. 2d–i) to accommodate the heterogeneity of climatic conditions that is inherent to the complex topography and sparse instrumental data available in mountain regions. We defined high-risk mountain areas as those with velocity values of isotherm shifts exceeding the 80th percentile calculated by either method (Fig. 2f,i). The threat level was then defined by the intersection or union of the highest 20% or 10% velocities of isotherm shifts of either method (Fig. 3a,b). Given that SLRT values close to 0 will provoke extremely high climate velocities, we removed 1% of outliers that were close to zero when we plotted Fig. 3c. Other levels of outlier removal (0.5%, 2% and 5%) can be found in Supplementary Fig. 2.
In addition to mapping the spatial distribution of the vertical projection of the velocities at which isotherms are shifting along mountain slopes worldwide and to better understand how velocity values distribute along elevation gradients at a global extent, we investigated the distribution of vertical velocity values across the bidimensional space of the elevation–latitude plane. Because the exposure to climate warming is greater at higher elevations^5,16 and towards higher latitudes in the Northern Hemisphere⁴⁸, we expect a non-random distribution of vertical velocity values in the elevation–latitude plane. Because the MALRT mechanistically incorporates the effects of surface temperature and water vapour pressure on the vertical velocities of isotherm shifts, and the biological analyses also suggest the importance of the MALRT over the SLRT in explaining the observed variation in the velocities of species range shifts (see Supplementary Information), we decided to focus solely on the MALRT-based velocity map to analyse the distribution of velocity values in the elevation–latitude plane. To do that, we reorganized all 12,036 spatial units from the MARLT-based velocity map at 0.5° resolution into a raster image with pixel units of 250-m resolution along the elevation axis and 2° resolution along the latitude axis. For each cell of the elevation–latitude plane, we computed and plotted the mean vertical velocity as well as the standard deviation and the sample size.
In the case of mountain islands, we repeated the above analysis for the elevation–latitude plane representation but relied on spatial data at finer resolution. Islands are defined as landmasses smaller than Australia and surrounded by water⁴⁹. In this study, the DEM that we used is derived from the Shuttle Radar Topography Mission (SRTM)⁵⁰ rather than from the CRU’s DEM. The SRTM⁵⁰, boasting a finer spatial resolution of 30 m, offers superior suitability for island detection, particularly for insular landforms proximate to the coast that remain unconnected to the mainland. Greenland is not included because it is not surrounded by the ocean in the dataset. These analyses were run in Wolfram Mathematica v.12 (ref. ⁵¹). The comparison between mainland and island velocities of isotherm shifts was done separately for the Northern and Southern hemisphere by the mean of a one-way ANOVA with post-hoc Tukey HSD test⁵².
To test whether the vertical velocities of isotherm shifts are greater at higher elevations in general and greater towards higher latitudes in the Northern Hemisphere, we ran a multivariate least square regression with elevation, absolute latitude, hemisphere (a factor variable with two levels: Northern versus Southern), the two-way interaction terms between all possible combinations of two of the three independent variables as explanatory variables explaining the mean vertical velocity of isotherm shifts, and also the three-way interaction terms (elevation, absolute latitude and hemisphere). This analysis was done on the basis of the original raster map (longitude–latitude) before summarizing into latitude–elevation dimensions.
We used the BioShifts database⁴ which provides quantitative data on the velocities of species range shifts (in m per year along the elevation gradient). To assess how the vertical velocities of isotherm shifts, after incorporating the spatial variation in the MALRT, relate to the observed velocities of species range shifts along elevation gradients, we first extracted empirical observations of species range shifts along the elevation gradients of mountain regions as delineated by original studies, thus excluding latitudinal range shifts. Then, we extracted the vertical velocity values for isotherms at the centroid of a given mountain region for which we could retrieve elevational range shift data from BioShifts (https://doi.org/10.6084/m9.figshare.7413365.v1). To avoid substantial spatial variation from studies conducted on a larger spatial extent, such as those spanning national or continental areas, we specifically chose datasets covering a spatial extent that approximates the resolution of our environmental dataset (0.5°). Hence, we focused on spatial features or polygons (that is, the spatial delineation of the study areas) smaller than approximately 100 km × 100 km (1° × 1°) to ensure that the environmental variables at the centroids of these polygons were less susceptible to spatial variation. A total of 5,452 datasets were retained for our subsequent analyses. To achieve this, we superimposed the centroid of the spatial polygons or shapefiles, as provided in the BioShifts database, of each of the selected study areas associated with elevational range shift data onto the MALRT-based velocity map. Here, we decided to focus solely on the MARLT-based map of the vertical velocities of isotherm shifts, because the MALRT is better correlated to the velocities of species range shifts than the SLRT is (see Supplementary Information).
Then, we computed the likelihood that a specific species from a designated taxonomic group (plants, birds, mammals, gastropods, insects, amphibians or reptiles; details provided in the Supplementary Information) tracks the vertical velocities of isotherm shifts within a particular mountainous area. To achieve this, we randomly resampled a fixed number of elevational range shift observations for each taxonomic group in each mountain region. This ensured relatively consistent and balanced sample sizes across all of the examined mountain regions and taxonomic groups. More specifically, for each taxonomic group in each mountain region (that is, the source region provided in the original dataset⁴ and available as shapefiles (.shp files) in the BioShifts database), we set the maximum sample size to n (see below for a sensitivity analysis on the effect of n) and resampled n records if the number of records was greater than n (see Extended Data Fig. 6a). If the total number of records for a given taxonomic group in a given mountain region was less than n, all records were used. The randomly sampled data on the observed velocities of range shifts were then compared to the corresponding set of vertical velocity values as obtained from the MALRT-based velocity map for that focal mountain region. To test for statistical differences between the two, we used a nonparametric method—the bilateral Wilcoxon signed rank test. This procedure (plotting and statistical comparison using a Wilcoxon signed rank test) was then iterated 1,000 times (see Extended Data Fig. 6a) and we calculated the number of iterations in which the empirical velocities of species range shifts did not differ significantly from the corresponding vertical velocities of isotherm shifts (that is, did not reach the significance level of P < 0.05; see Extended Data Fig. 6) and divided it by the total number of iterations (1,000). The obtained proportion value, ranging between 0 and 1, gives the probability that a given focal taxonomic group has more or less tracked the vertical velocity of isotherm shifts in the focal mountain region as the climate warms globally. A logistic-type (probit) function was then applied to estimate the probability curve. We also performed a sensitivity analysis by setting different maximum sample sizes for n (10, 20, 30, 40, 50, 60, 70, 80, 90 and 100), and the results became stable when n was larger than 30 (Supplementary Data 2), so we decided to set n = 30 to address the problem of studies with a small sample size. The data processing and statistical analysis in this section were done in R v.4.04 (ref. ⁵³).
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
The data supporting the findings of this study are available in the paper and at https://doi.org/10.5061/dryad.1rn8pk0wm. CRU TS4.05 is available at https://crudata.uea.ac.uk/cru/data/hrg/; MOD11C1 at https://lpdaac.usgs.gov/#nav-heading; MOD11C2 at https://lpdaac.usgs.gov/#nav-heading; MOD11C3 at https://lpdaac.usgs.gov/#nav-heading; EarthEnv at https://www.earthenv.org/; ETOPO1 at https://www.ncei.noaa.gov/products/etopo-global-relief-model; SRTM at https://www.earthdata.nasa.gov/sensors/srtm; GMBA at https://www.gmba.unibe.ch/services/tools/mountain_inventory_v1; CHELSA at https://chelsa-climate.org/; GHCN at https://www.drought.gov/data-maps-tools/global-historical-climatology-network-ghcn; and BioShifts at https://doi.org/10.6084/m9.figshare.7413365.v1.  Source data are provided with this paper.
Code is available at https://doi.org/10.5061/dryad.1rn8pk0wm.
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We thank Y.-S. Jang, T.-C. Hsieh, S.-H. Wang and C.-Y. Lin for their help in the early development of this study; J.-H. Chen for providing statistical suggestions at the early stages of this study; and C. D. Thomas, R. K. Colwell, R. R. Childers, S. Ashe and C.-N. Chou for their comments on the early version of the manuscript. We acknowledge grants 108-2314-B-001-009-MY3 (S.-F.S.) and 104-2311-B-006-006-MY3 (I.-C.C.) from the Ministry of Science and Technology, Taiwan, and grants AS-SS-106-05 (S.-F.S.) and AS-SS-110-05 (S.-F.S.) from the Academia Sinica.
Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
Wei-Ping Chan, Guan-Shuo Mai & Sheng-Feng Shen
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
Wei-Ping Chan
Bachelor Program in Data Science and Management, Taipei Medical University, Taipei, Taiwan
Wei-Ping Chan
Rowland Institute at Harvard University, Cambridge, MA, USA
Wei-Ping Chan
UMR CNRS 7058, Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN), Université de Picardie Jules Verne, Amiens, France
Jonathan Lenoir
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Hung-Chi Kuo
Department of Life Sciences, National Cheng Kung University, Tainan, Taiwan
I-Ching Chen
Department of Biology, Stanford University, Stanford, CA, USA
I-Ching Chen
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Conceptualization: S.-F.S. Methodology: S.-F.S., I.-C.C. and W.-P.C. Formal analysis: W.-P.C. Random forest analysis: G.-S.M. Visualization: W.-P.C. Writing (original draft): S.-F.S., I.-C.C. and W.-P.C. Writing (review and editing): S.-F.S., I.-C.C., J.L., W.-P.C., G.-S.M. and H.-C.K.
Correspondence to I-Ching Chen or Sheng-Feng Shen.
The authors declare no competing interests.
Nature thanks Alexandre Antonelli, Joshua Lawler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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a, 10 × 10 grids (all at 0.05-degree spatial resolution) of the DEM were included in a target grid (0.5-degree spatial resolution). b, Mountain peaks and feet/valleys are automatically searched and identified. c, A transect can be defined by a peak-foot/valley pair. d, An exemplar transect on the land surface temperature map (at 0.05-degree spatial resolution). e, The elevation and the mean annual temperature data across 2011–2020 along the exemplar transect are specified. f, The relationship between mean annual temperature and elevation. The data points were extracted according to e. The regression line is provided as the orange solid line, where the slope (beta in the regression model) is considered as the lapse rate of a transect.
Source Data
a, Averaged mean annual temperature (2011–2020). b, Averaged mean annual water vapour (2011–2020). c, Temperature differences between the two periods divided by the temporal period (40 years). Note that the colour scheme does not show the full range of data to prevent highly skewed visualization driven by extreme outliers.
Source Data
a,b, Scatterplots show MALRT values against station-based LRT values (a) and SLRT values against station-based LRT values (b) for available mountain sites (n = 48). Two conspicuous outliers (circled in grey) derived from the weather-station data were excluded. The regressive slopes of the plots are labelled. The statistics were done using a two-tailed approach without adjustment for multiple comparisons.
Source Data
a,b, SHAP (SHapley Additive exPlanations) value distributions that elucidate the model’s decision-making process under low (a) and high (b) water vapour conditions. In these sub-figures, each point signifies a prediction made by the model. They are coloured according to the feature’s (temperature rate and MALRT) value, creating a spectrum that indicates the feature’s effect; warmer colours symbolize higher values and cooler colours represent lower values. The x axis demonstrates the SHAP values, portraying the magnitude and direction of a feature’s effect on the model’s output, with negative values suggesting a decrease and positive values indicating an increase in the prediction. c,d, Histograms of temperature rate (c) and MALRT (d) under conditions of high (blue) and low (orange) water vapour. The x axis corresponds to temperature rate and MALRT, measured in °C per year; the y axis represents the frequency of occurrence.
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Different columns indicate different datasets. a,b, Models include the basic geographical factors (latitude and longitude) as well as the vertical velocities of isotherm shifts (Clim. V.) derived from either the MALRT (a) or the SLRT (b). c, Models consider all possible factors influencing the velocities of species range shifts (Supplementary Methods). The centre and the error bars indicate mean and s.d., respectively. Sample sizes for datasets filtered for upward shifts, downward shifts and all directions are 3,635, 1,401 and 5,452, respectively.
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a, Diagram summarizing how the probability of tracking climate velocities was calculated (i = 1,000). b, Replicate of Fig. 4f with colour-labelled exemplar taxonomic groups. The raw values are shown in c. The centre line of the box plot represents median; box limits, upper and lower quartiles; whiskers, 1.5 times the interquartile range. The sample size for three examples are 219 (green), 372 (blue) and 433 (orange). d,e, The different probabilities of species tracking climate velocities under different P thresholds (P = 0.01 (d) and P = 0.001 (e)). A total of 83 taxon–region pairs are plotted. Each plot represents 1 to more than 400 raw data points. Only mean values are shown. Upward and downward shifts are shown together with their absolute values. For raw data points, see Supplementary Fig. 4. The statistics were done using a two-tailed approach without adjustment for multiple comparisons.
Source Data
a,b, Velocities based on SLRT. c,d, Velocities based on constant lapse rate (5.5 °C per km). The relationships between observed shifting rate and elevational climate velocities are shown in a,c. Only mean values are shown. The probabilities that species may track climate velocity are shown in b,d.
Source Data
a, Distribution of the number of available transects. b, Correlation between the number of available transects and the SLRT interquartile range. c, Correlation between the number of available transects and the averaged R² between elevation and temperature. Blue lines indicate simple regression between the two variables, with statistics labelled at the bottom right of each panel. Orange lines represent LOESS (locally estimated scatter plot smoothing) lines. The statistics were done using a two-tailed approach without adjustment for multiple comparisons.
Source Data
a–d, Comparisons of MALRT at different elevational ranges (0–1,000 m (a); 1,000–2,000 m (b); 2,000–3,000 m (c); and more than 3,000 m (d)). Statistics are labelled at the bottom right of each panel. Significance levels are indicated: ***P < 10⁻¹⁶. The statistics were done using a two-tailed approach without adjustment for multiple comparisons.
Source Data
A file contains Supplementary Methods, Supplementary Results and Supplementary Figures.
Statistical information on major regions of high-velocity regions.
The probabilities of species tracking climate velocities under p = 0.05 thresholds with different sampling sizes (n = 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100).
A Google Earth layer file (*.kmz) that enables self-exploration.
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Chan, WP., Lenoir, J., Mai, GS. et al. Climate velocities and species tracking in global mountain regions. Nature (2024). https://doi.org/10.1038/s41586-024-07264-9
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Received: 13 November 2023
Accepted: 01 March 2024
Published: 27 March 2024
DOI: https://doi.org/10.1038/s41586-024-07264-9
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