Gaps and opportunities in modelling human influence on species distributions in the Anthropocene.

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no 'rule of thumb' for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.

Reciprocal inhibition and competitive hierarchy cause negative biodiversity-ecosystem function relationships.

The relationship between biodiversity and ecosystem function (BEF) captivates ecologists, but the factors responsible for the direction of this relationship remain unclear. While higher ecosystem functioning at higher biodiversity levels ('positive BEF') is not universal in nature, negative BEF relationships seem puzzlingly rare. Here, we develop a dynamical consumer-resource model inspired by microbial decomposer communities in pitcher plant leaves to investigate BEF. We manipulate microbial diversity via controlled colonization and measure their function as total ammonia production. We test how niche partitioning among bacteria and other ecological processes influence BEF in the leaves. We find that a negative BEF can emerge from reciprocal interspecific inhibition in ammonia production causing a negative complementarity effect, or from competitive hierarchies causing a negative selection effect. Absent these factors, a positive BEF was the typical outcome. Our findings provide a potential explanation for the rarity of negative BEF in empirical data.

The metacoupled Arctic: Human-nature interactions across local to global scales as drivers of sustainability.

The Arctic is an epicenter of complex environmental and socioeconomic change. Strengthened connections between Arctic and non-Arctic systems could threaten or enhance Arctic sustainability, but studies of external influences on the Arctic are scattered and fragmented in academic literature. Here, we review and synthesize how external influences have been analyzed in Arctic-coupled human and natural systems (CHANS) literature. Results show that the Arctic is affected by numerous external influences nearby and faraway, including global markets, climate change, governance, military security, and tourism. However, apart from climate change, these connections are infrequently the focus of Arctic CHANS analyses. We demonstrate how Arctic CHANS research could be enhanced and research gaps could be filled using the holistic framework of metacoupling (human-nature interactions within as well as between adjacent and distant systems). Our perspectives provide new approaches to enhance the sustainability of Arctic systems in an interconnected world.

Assessing the water and carbon footprint of hydropower stations at a national scale.

Hydropower is among the most widely-adopted renewable energy sources worldwide. Its development has, however, led to environmental impacts such as carbon emissions and water loss. To date, the water footprint (WF) and carbon footprint (CF) of hydropower stations have been assessed, but not simultaneously or at a large scale such as national scale. Previous WF and CF studies rarely assessed all life-cycle stages of a hydropower station, calling for a more holistic understanding of the environmental impacts of hydropower. We developed a complete WF and CF assessment method and applied it to a case study on 50 of China's most influential hydropower stations, representing over 80% of the country's total hydropower. The total annual WF of these hydropower stations was 5.50 × 1011 m3, equal to 18.9% of Yellow River's annual runoff. The total CF of these stations was 1.06 × 107 tCO2e, with extremely large variations found, ranging from 1850 to 1.56 × 106 tCO2e. This study provides the first environmental impact assessment to simultaneously include the WF and CF of multiple influential hydropower stations at a national scale. We were able to show spatial variations in their environmental impacts from different life-cycle stages of the hydropower station. Most of the WF was due to surface water loss from reservoirs, while most of the CF was derived from the operational and maintenance stage of these stations. This initial WF and CF assessment of hydropower at a national scale provides insights for water resource management and carbon reduction during hydropower development.