Ecography最新文献

Accurate forecasts of species distributions in response to changing climate is essential for proactive management and conservation decision-making. However, species distribution models (SDMs) often have limited capacity to produce robust forecasts under novel environmental conditions, partly due to limitations in model training data. Model-based approaches that leverage diverse types of data have advanced over the last decade, yet their forecasting skill, especially during episodic climatic events, remains uncertain. Here, we develop a suite of SDMs for a commercially important fishery species, albacore tuna Thunnus alalunga, to evaluate forecast skill under marine heatwave conditions. We compare models that use different methods to leverage data sources (data-pooling versus joint-likelihood) and to address spatial dependence (environmental and spatial effects versus environmental-only) to assess their relative performance in predicting species distributions under novel environmental conditions. Our results indicate model performance declined across all model types as environmental novelty increased as expected. However, joint-likelihood approaches were more resilient to novel conditions, demonstrating greater predictive skill and ecological realism than traditional SDMs. These results suggest that ecological forecasts under novel environmental conditions are more skillful with a model framework that accounts for unmeasured spatial and temporal variability and uses model-based data integration to explicitly leverage diverse data types. As access to diverse data sources continues to increase, maximizing their utility will be key for delivering accurate forecasts of species distributions and advancing proactive, climate-ready management and conservation strategies.

Plant roots have been observed up to 70 m in depth – what would compel a plant to root so deeply? Earlier work shows that the climate, soil and drainage all affect rooting depth, but with conflicting results. For example, both the deepest and shallowest roots are found in arid regions. Here, we compiled > 2400 globally distributed rooting-depth observations of individual plants and applied simple correlation analysis to assess the impact of global climate, local topography and substrate, and individual plant size, and their combinations controlling where and why plants root deeply. At the global scale, deep roots are driven by climate. Both concentrated wet periods and prolonged droughts are required to drive deep roots, and we find the deepest roots in semi-arid climates with strong precipitation seasonality or interannual variability. At the landscape scale, drainage modulates rooting depth. An accessible water table facilitates deep roots at midslopes, but it is too deep to impact roots further upslope. Instead, the deep vadose zone moisture reserve is the primary driver for deep rooting. Thus, the deepest roots are observed on well-drained uplands with deep vadose zones under climates with distinct wet and dry periods. At the plot scale, substrate structure and hydraulic properties modulate deep rooting – B-horizons limit deep roots, while woody plants often root below the bedrock surface, provided it is fractured. At the individual plant scale, deep roots are limited to high-biomass woody plants. Together, these findings sharpen our understanding of where and why plants root deeply, highlighting intersections of climate, drainage, terrain and biomass and identifying conditions where deep roots may serve as a lifeline during prolonged drought, meanwhile weathering rock, sequestering carbon, and bringing the living world far deeper than the conventional ‘root zone'.

Habitat is a key aspect of any species' niche and can affect populations at multiple spatial scales. Basic ecology and effective conservation thus require an understanding of which habitats matter and at what scales. Yet, habitat studies are rarely scale-optimized, and what determines the scale(s) at which populations are affected by surrounding habitat (the ‘scale of effect') is poorly understood. In this study, we test the ‘mobility hypothesis,' which predicts that species with larger foraging ranges should have larger scales of effect. The mobility hypothesis is the most popular explanation of what determines species' scales of effect, but empirical support is mixed. We test the mobility hypothesis using wild bee species and, in doing so, also assess landscape-scale habitat associations of 84 bee species. We collected 30 376 specimens of 84 bee species from 165 sites in the northeastern USA and used linear models to determine landcover associations and scales of effect for each species. To test the mobility hypothesis, we asked whether scales of effect varied with two mobility-related traits – body size or sociality, which are the strongest known predictors of bee foraging ranges. Controlling the false discovery rate at 5%, we found 193 significant species–landcover associations across 60 (of 84) species. Scales of effect ranged from 100 to 8000 m (mode = 200 m; median = 1000 m) and, counter to the mobility hypothesis, were not associated with body size or sociality. As a result, we argue that ecologists should reconsider making assumptions about species' scales of effect and should instead explicitly measure scales of effect for their particular study organism and system. Considering the landcover associations themselves, we found these were broadly explained by phenology, with spring-flying bees being associated with forests and summer-flying bees being associated with more open, non-forested habitats.

Niche partitioning is one of the key mechanisms allowing species coexistence and is especially relevant in species-rich communities. For pollinators, morphology is a major axis in which species differentiate their foraging niche, as it influences the match with flower morphology. Bumblebees Bombus spp. are important pollinators globally, showing their highest diversity of co-occurring species in the Hengduan Mountains region of southwestern China. This community context makes this region an ideal model system to test the importance of niche partitioning for plant–pollinator interactions. In high-elevation, flower-rich meadows, we sampled over four years pollinator–plant interaction networks containing 12 sympatric bumblebee species, varying more than fourfold in tongue length from 4.7 to 21.7 mm. We then assessed the degree of niche partitioning occurring between these bumblebees. We analysed bumblebees' foraging niche widths and overlap, and found that species with longer tongues foraged from a narrower range of flowers. Accordingly, bumblebee species with shorter tongues, who visited a higher diversity of flowering species also showed consistently higher floral overlap with other bumblebee species across years. Despite this morphology-driven niche pattern for species, the interaction network was consistently characterised by a high degree of generalisation across the years. Our results indicate that the co-occurrence of a large number of potentially competing pollinators with high generalisation and niche overlap is possible in flower-rich habitats. We suggest that, in regions of extraordinarily high plant and pollinator diversity and abundance, diverse pollinator communities may also be maintained without strong foraging niche partitioning.

Diel activity rhythms, representing the behavioral pattern of the sleep–wake cycle, may be adjusted by wildlife in response to changes in environmental conditions. An increase in nocturnality is typically recognized as an adaptive strategy to segregate from humans and mitigate heat stress. Numerous studies have investigated spatial patterns and habitat use of large carnivores in human-modified landscapes, but little research has examined their activity rhythms. We compiled Global Positioning System data (2004–2022) for 139 brown bears Ursus arctos from six populations across Europe, representing a human-modified landscape, and the Greater Yellowstone Ecosystem, U.S.A., representing a landscape with limited human impact, which we used to calculate hourly movement rates as an activity proxy. Using a Bayesian approach to model the temporal autocorrelation of activity data, we tested if the extent of nocturnality in brown bears is modulated by intensity of human encroachment, accounting for primary productivity and maximum ambient temperature. All bear populations exhibited a predominantly bimodal, crepuscular pattern of activity, although Yellowstone bears were proportionally more crepuscular and diurnal. Whereas the effect of primary productivity was variable, all European populations became more nocturnal in response to higher human encroachment and reduced diurnal and crepuscular activity at higher summer temperatures, decreasing overall diel activity levels. Yellowstone bears displayed the greatest shift towards nocturnality among all populations in response to increasing human encroachment, and increased nocturnal activity to compensate for lower diurnal and crepuscular activity at higher summer temperatures. Our research indicates that European bears in human-modified landscapes may be reaching a limit in the behavioral plasticity they can manifest in their activity patterns, being already constrained into increased nocturnality. Our findings enhance the understanding of brown bear adaptive capacity to accommodate future changes, such as urbanization and increasing temperatures, to the ecosystems they inhabit.

Terrestrial mammals are found nearly everywhere on Earth. Yet, most taxa are endemic to a single continent; geological, evolutionary, ecological, or physiological filters constrain geographic distributions. Here, we synthesize data on geography, taxonomy, lineage age, dispersal, body size, and diet for > 4000 terrestrial mammals prior to detectable human-mediated biodiversity losses and quantify factors correlated with the likelihood of dispersal between continents. We confirm the uniqueness of being on multiple continents: excluding humans and commensals, only 260 mammals are found on two continents, while six span three or more continents (the red deer, red fox, brown bear, least weasel, and common bent-wing bat), and just a single species – the lion – once had a geographic range that included four continents. Clearly the challenges of colonizing and persisting on multiple continents are severe. No single characteristic enables taxa to be on more than one continent. Rather, a suite of prerequisite conditions under some circumstances lead to distributions spanning multiple continents. The suite of factors facilitating the occupation of two continents, like being volant, are distinct from those that lead to the occupation of three or more, which are primarily faunivores. Other than humans and our commensals, very few species have become truly cosmopolitan over evolutionary time and geographic space.

Phytoplankton communities affect carbon dynamics worldwide, strongly influencing the quality and quantity of organic carbon in coastal ecosystems. Yet, we still know little about the impacts of changing phytoplankton community composition on the potential carbon pathways in estuaries and coasts. Here, we sampled 25 sites along a coastal salinity and nutrient gradient, collecting water for water chemistry and phytoplankton for community composition analyses. For each site, we determined phytoplankton taxonomic diversity and used Bayesian joint species distribution models considering species interactions, taxonomic relatedness and traits to identify key environmental factors driving phytoplankton community composition. Subsequently, we used structural equation modelling to establish direct and indirect links between the identified key environmental factors, taxonomic diversity (richness and evenness) and particulate organic carbon (POC). We found that the phytoplankton distribution along the estuarine gradient was mainly driven by changes in salinity. Increasing salinity (ranging between 0.8–6.4) benefited motile species and reduced the phytoplankton richness, which resulted in a decrease in POC concentration. This indirect effect of salinity on POC was stronger than a direct one, highlighting the mediating role of phytoplankton richness. This emphasizes the importance of diversity regulating coastal biogeochemical processes and suggests that future changes in salinity might shift coastal carbon dynamics due to changes in phytoplankton community composition.

Sampling bias is an inherent problem in widely available biodiversity data, undermining the robustness of correlative species distribution models (SDMs). To some extent, subsampling occurrence data can account for uneven sampling efforts; yet, conventional approaches subsample in geographical space, while subsampling in environmental space remains underexplored. Here, we compared the effectiveness of subsampling methods that correct sampling bias either in geographical space (spatial gridding, spatial distance thinning) or directly in environmental space (environmental gridding), including two novel approaches introduced here: environmental clustering and environmental distance thinning. We hypothesised that environmental subsampling methods would be more effective in improving SDM performance across its three primary uses: explaining, predicting, and projecting. Using a virtual ecologist framework, we assessed SDM performance against four evaluation tests: replicating true species–environment response curves, predicting within the sampling region via internal cross-validation and evaluation against independent data, and projecting outside the sampling region. Our findings demonstrate that environmental subsampling methods, especially environmental clustering and environmental distance thinning, outperformed other methods in yielding robust SDMs in almost all evaluation tests. Interestingly, cross-validation favoured SDMs with no sampling bias correction, highlighting the inability of cross-validation to identify unbiased models. Our findings emphasise a critical conceptual disconnect: SDMs appearing to perform well in predicting species' distributions may not reliably estimate species–environment relationships, nor transfer predictions onto novel environments. Environmental subsampling methods are reliable approaches for all uses, but are particularly suited for explaining species' niches and transferring predictions across space and/or time, such as when anticipating species' responses to climate change or assessing the risk of biological invasions. Conversely, geographic subsampling methods may suffice for predicting species' distributions within their current environmental context, as required in conservation planning. Our study firmly establishes the critical importance of correcting environmental sampling bias, while also providing reliable solutions for supporting biodiversity conservation in an ever-changing world.