• Energy Research

According to Bergmann's and Allen's rules, climate change may drive morphological shifts in species, affecting body size and appendage length. These rules predict that species in colder climates tend to be larger and have shorter appendages to improve thermoregulation. Bats are thought to be sensitive to climate and are therefore expected to respond to climatic changes across space and time. We conducted a phylogenetic meta‐analysis on > 27 000 forearm length (FAL) and body mass (BM) measurements from 20 sedentary European bat species to examine body size patterns. We assessed the relationships between body size and environmental variables (winter and summer temperatures, and summer precipitation) across geographic locations, and also analysed temporal trends in body size. We found sex‐specific morphological shifts in the body size of European bats in response to temperature and precipitation patterns across space, but no clear temporal changes due to high interspecific variability. Across Europe, male FAL decreased with increasing summer and winter temperatures, and BM increased with greater precipitation. In contrast, both FAL and BM of female bats increased with summer precipitation and decreased with winter temperatures. Our data can confirm Bergmann's rule for both males and females, while females' BM variations are also related to summer precipitation, suggesting a potential link to resource availability. Allen's rule is confirmed only in males in relation to summer temperature, while in females FAL and BM decrease proportionally with increasing temperature, maintaining a constant allometric relationship incompatible with Allen's rule. This study provides new insights into sex and species‐dependent morphological changes in bat body size in response to temperature and precipitation patterns. It highlights how body size variation reflects adaptations to temperature and precipitation patterns, thus providing insights into potential species‐level morphological responses to climate change across Europe.

ABSTRACTUnderstanding how species respond to climate change is key to informing vulnerability assessments and designing effective conservation strategies, yet research efforts on wildlife responses to climate change fail to deliver a representative overview due to inherent biases. Bats are a species‐rich, globally distributed group of organisms that are thought to be particularly sensitive to the effects of climate change because of their high surface‐to‐volume ratios and low reproductive rates. We systematically reviewed the literature on bat responses to climate change to provide an overview of the current state of knowledge, identify research gaps and biases and highlight future research needs. We found that studies are geographically biased towards Europe, North America and Australia, and temperate and Mediterranean biomes, thus missing a substantial proportion of bat diversity and thermal responses. Less than half of the published studies provide concrete evidence for bat responses to climate change. For over a third of studied bat species, response evidence is only based on predictive species distribution models. Consequently, the most frequently reported responses involve range shifts (57% of species) and changes in patterns of species diversity (26%). Bats showed a variety of responses, including both positive (e.g. range expansion and population increase) and negative responses (range contraction and population decrease), although responses to extreme events were always negative or neutral. Spatial responses varied in their outcome and across families, with almost all taxonomic groups featuring both range expansions and contractions, while demographic responses were strongly biased towards negative outcomes, particularly among Pteropodidae and Molossidae. The commonly used correlative modelling approaches can be applied to many species, but do not provide mechanistic insight into behavioural, physiological, phenological or genetic responses. There was a paucity of experimental studies (26%), and only a small proportion of the 396 bat species covered in the examined studies were studied using long‐term and/or experimental approaches (11%), even though they are more informative about the effects of climate change. We emphasise the need for more empirical studies to unravel the multifaceted nature of bats' responses to climate change and the need for standardised study designs that will enable synthesis and meta‐analysis of the literature. Finally, we stress the importance of overcoming geographic and taxonomic disparities through strengthening research capacity in the Global South to provide a more comprehensive view of terrestrial biodiversity responses to climate change.

Greenspoon et al. (1) used of global population estimates of 392 mammal species to predict the global biomass of mammals. We caution against important limitations in their approach, which likely results in gross underestimations of biomass and its uncertainty. The authors derive >97% of their estimates from the IUCN Red List (RL) database, which is particularly ill-suited for this scope since it is compiled using different standards than scientific ecological investigation and based on inconsistent approaches that are influenced by precautionary to evidentiary attitudes of different RL assessors (2). Because reliable population estimates are available only for relatively small areas (3), RL figures are generally obtained by summing up local estimates based on relatively scarce and biased data, including expert-based guesses with little supporting evidence (Table 1). Depending on the original purpose, RL figures might be over- or under-inflated (3, 4). Some RL’s reported population sizes are derived by applying an average density across an area, which may not align with the area used in Greenspoon et al. to estimate density, hence resulting in biased and unrealistic estimates when compared with field density estimates (Table 1). The RL dataset includes an overrepresentation of threatened species (60 vs. 27% of all mammals). The authors control for this bias by including RL categories as models’ predictors. However, no RL criteria relate to density, with ~80% of mammal species threatened due to small or decreasing ranges. Finally, model extrapolations are based on data heavily biased toward ungulates (40 vs. 4.7% of all mammals) while not controlling for phylogenetic relatedness but reassigning species to other taxonomic order for model predictions. Consequently, predicted density estimates by Greenspoon et al. (1) deviate substantially from published estimates obtained in hundreds of field studies (6) (median absolute orders of magnitude deviation = 0.64, 95% = 0.06 to 2.5, N = 159, Fig. 1A) and are on average underestimated by threefolds (median orders of magnitude deviation = −0.47). This may have profound effects on global estimates (Fig. 1B), leading to a potential underestimation of biomass of ~5.5-fold difference (9.4 vs. 52 Mt for 159 species). Furthermore, we could not replicate the results of 7 of the top 10 marine mammals’ biomass due to inconsistent use of RL data (Table 1). The uncertainty computed by Greenspoon et al. (1) is based on several expert-based intervals and applied to all species, including marine mammals for which estimates of variability are available from literature and the RL but were not used (8). This results in a gross underestimation of the uncertainty around the global biomass prediction (Table 1). While estimating biomass globally provides important insights, we call for a more careful consideration of data quality and consistency and a more robust reporting of uncertainty. We recommend fitting models on empirically derived field density estimates while controlling for phylogeny, environmental variables, and inconsistent sampling methods (e.g. refs. 6 and 9). Uncertainty should be derived directly from the statistical predictive error and, when possible, also from the underlying data. Alternatively, mechanistic eco-physiological models can be used to estimate global biomass following trait-based theory and validated with independent data (9).

Several hundred species are hunted for wild meat in the tropics, supporting the diets, customs, and livelihoods of millions of people. However, unsustainable hunting is one of the most urgent threats to wildlife and ecosystems worldwide and has serious ramifications for people whose subsistence and income are tied to wild meat. Over the past 18 years, although research efforts have increased, scientific knowledge has largely not translated into action. One major barrier to progress has been insufficient monitoring and evaluation, meaning that the effectiveness of interventions cannot be ascertained. Emerging issues include the difficulty of designing regulatory frameworks that disentangle the different purposes of hunting, the large scale of urban consumption, and the implications of wild meat consumption for human health. To address these intractable challenges, wepropose eight new recommendations for research and action for sustainable wild meat use, which would support the achievement of the United Nations Sustainable Development Goals.

AbstractAimClimate change will likely modify the global distribution of biomes, but the magnitude of change is debated. Here, we followed a trait‐based, statistical approach to model the influence of climate change on the global distribution of biomes.LocationGlobal.MethodsWe predicted the global distribution of plant community mean specific leaf area (SLA), height and wood density as a function of climate and soil characteristics using an ensemble of statistical models. Then, we predicted the probability of occurrence of biomes as a function of the three traits with a classification model. Finally, we projected changes in plant community mean traits and corresponding changes in biome distributions to 2070 for low (RCP 2.6; +1.2°C) and extreme (RCP 8.5; +3.5°C) future climate change scenarios.ResultsWe estimated that under the low climate change scenario (sub)tropical biomes will expand (forest by 18%–22%, grassland by 9%–14% and xeric shrubland by 5%–8%), whereas tundra and temperate broadleaved and mixed forests contract by 30%–34% and 16%–21%, respectively. Our results also indicate that over 70%–75% of the current distribution of temperate broadleaved and mixed forests and temperate grasslands is projected to shift northwards. These changes become amplified under the extreme climate change scenario in which tundra is projected to lose more than half of its current extent.Main conclusionsOur results indicate considerable imminent alterations in the global distribution of biomes, with possibly major consequences for life on Earth. The level of accuracy of our model given the limited input data and the insights on how trait–environment relationships can influence biome distributions suggest that trait‐based correlative approaches are a promising tool to forecast vegetation change and to provide an independent, complementary line of evidence next to process‐based vegetation models.

AbstractThe urgent need to mitigate and adapt to climate change necessitates a comprehensive understanding of carbon cycling dynamics. Traditionally, global carbon cycle models have focused on vegetation, but recent research suggests that animals can play a significant role in carbon dynamics under some circumstances, potentially enhancing the effectiveness of nature‐based solutions to mitigate climate change. However, links between animals, plants, and carbon remain unclear. We explored the complex interactions between defaunation and ecosystem carbon in Earth's most biodiverse and carbon‐rich biome, tropical rainforests. Defaunation can change patterns of seed dispersal, granivory, and herbivory in ways that alter tree species composition and, therefore, forest carbon above‐ and belowground. Most studies we reviewed show that defaunation reduces carbon storage 0−26% in the Neo‐ and Afrotropics, primarily via population declines in large‐seeded, animal‐dispersed trees. However, Asian forests are not predicted to experience changes because their high‐carbon trees are wind dispersed. Extrapolating these local effects to entire ecosystems implies losses of ∼1.6 Pg CO2 equivalent across the Brazilian Atlantic Forest and 4−9.2 Pg across the Amazon over 100 years and of ∼14.7−26.3 Pg across the Congo basin over 250 years. In addition to being hard to quantify with precision, the effects of defaunation on ecosystem carbon are highly context dependent; outcomes varied based on the balance between antagonist and mutualist species interactions, abiotic conditions, human pressure, and numerous other factors. A combination of experiments, large‐scale comparative studies, and mechanistic models could help disentangle the effects of defaunation from other anthropogenic forces in the face of the incredible complexity of tropical forest systems. Overall, our synthesis emphasizes the importance of—and inconsistent results when—integrating animal dynamics into carbon cycle models, which is crucial for developing climate change mitigation strategies and effective policies.