BIEN 4.2: A Reproducible Standard for Global Plant Biodiversity Data Post provided by the BIEN Working Group For hundreds of years, biologists have carefully collected information on plants, animal…

Changes to landscapes across Europe, including farmland abandonment and forest management methods, are reshaping the amount of light different plants receive. However, this important driver of biodive...

Bioindication reveals a 60-year shift toward denser, more productive vegetation, but a weak thermophilization signal.

Citizen science data are increasingly used in biodiversity monitoring. This study applies a digital twin approach to biodiversity monitoring using a large citizen science dataset on birds from Finland...

We assessed tree diversity recovery across five reforestation methods (white background) and compared them to three reference systems (grey background). In the rainforest, none of the restoration met...

Aim Understanding the relative influence of past and present environmental and anthropogenic drivers on biodiversity is crucial for predicting future biodiversity trends. We assessed how past climat...

Ecosystem scientist Yadvinder Malhi takes us on a jaw-dropping journey through the hidden flows of energy that make life on Earth tick. From sun-soaked forests to tropical islands, he shows how his te...

We are excited to invite applications for a new PhD position on large mammals and land-use change in Europe. Join us at the Conservation and Biogeography Lab at Humboldt University Berlin!

Urban greening (UG) affects local climate by altering surface energy balance, while long-term UG cooling potential, patterns, and contribution to curbing urban warming remain unclear. Here, we designed an novel statistical model to evaluate the cooling potential of UG (CPUG) and created the first CPUG map for China. By exploring the trends in observed and simulated urban surface temperatures (UST), we quantified the CPUG of 0.20 K over the past two decades, which slowed down the warming trend by 14.17% in Chinese cities. We found that the CPUG varied significantly between the urban core and sprawl areas. Specifically, the CPUG in the urban core was approximately 1.01 K, and it contributed to curbing urban warming by 56.08%, which was more than 7.2 times higher than in the sprawl areas, where the CPUG was only 0.14 K and contributed to curbing urban warming by 9.93%. We further revealed that urbanization and major ecological restoration projects are the key factors influencing CPUG, emphasizing the need for anthropogenic vegetation management to curb urban warming. The proposed model in this study provides a powerful tool for quantitatively assessing the impact of long-term UG trends on urban warming. The results of the study are an important reference for building climate-adaptive cities.