Ecography最新文献

Mountains are home to steep elevational gradients in environmental factors, biodiversity and ecosystem functionality. Though these gradients are tightly connected, little is known about the relative contribution of environmental and biotic factors in driving elevational changes in ecosystem functionality. Here, we conducted a comprehensive survey of three > 2000 m long elevational gradients within the Hengduan Mountains region (southern China), quantifying key metrics of ecosystem functionality, as well as community composition and species richness of both plants and soil microbes. We found significant elevational patterns of plant and bacterial richness and ecosystem functionality, varying from unimodal (hump-shaped or U-shaped) to monotonic (linear) among the three mountains. Unexpectedly, plant and bacterial richness were either negatively or not significantly correlated with key indicators of ecosystem functionality. We further demonstrated that climate and soil pH were the key predictors of ecosystem functionality. Ecosystem functionality was additionally affected by changes of the bacterial species composition. Our results suggest that climate and microbial community composition jointly drive elevational changes in ecosystem functionality, with no clear role for species richness per se. These findings advance our understanding of mountain biogeography and the links between biodiversity and ecosystem function.

The center–periphery hypothesis (CPH) states that species' demographic performance declines from the center towards the periphery of their geographic range due to increasingly suboptimal environmental conditions. We tested the predictions under the CPH using two lizard lineages with different activity patterns and distributions, taking lizard body condition and gastrointestinal parasitism as proxies of demographic performance. We sampled Ameiva ameiva, Tropidurus itambere, and T. madeiramamore from core localities and peripheral Cerrado isolates in southwestern Amazonia. To assess predictions under the CPH, we built generalized linear mixed models using the indicators of demographic performance as the response variables. Environmental (climate, elevation, soil) and spatial (landscape parameters, distance to Cerrado's center and periphery) variables were predictor variables, along with lizard genus and their interactions. We applied generalized dissimilarity modeling (GDM) and variance partitioning to assess geographic, environmental, and spatial influences on parasite beta diversity. Lizard lineage was the most important predictor of body condition and lizard parasite abundance/richness. Centrality, connectivity, and precipitation of the warmest quarter significantly predicted lizard gastrointestinal parasitism. Soil, centrality, landscape, and elevation had a non-zero sum of coefficients in GDM's I-spline for lizard parasite beta diversity. Geographic distance had a negligible influence, and environmental variation was the primary driver of parasite beta diversity. For Ameiva, demographic performance did not vary across the sampled central and peripheral areas, both central to Ameiva's distribution, consistent with CPH predictions of stable demographic performance between central areas. Tropidurus displayed better body condition and higher parasite abundance in peripheral isolates, contrary to predictions under the CPH, likely due to ecological release. Soil and proximity to the Cerrado's center were the strongest predictors of parasite beta diversity, suggesting environmental and spatial factors outweigh biotic or climatic influences. These results suggest that the CPH's predictions may not always hold, especially when ecological release affects demographic performance.

Global change will impact the distribution and abundance of predators through a combination of abiotic variables, such as temperature; and biotic variables, such as prey availability. However, there is a poor understanding of how distribution projections with biotic variables differ from those with abiotic variables, particularly in resource-limited marine systems. We address this knowledge gap using the planktonic larvae of iconic and economically important pelagic fish predators. We leverage a multidecadal, long-term sampling program from the western Atlantic Ocean to assess the efficacy of using zooplankton prey (copepods, larvaceans and cladocerans) and climate variables to predict the distribution of larvae of seven pelagic fish species, including tunas, billfishes and mahi-mahi. Zooplankton prey, particularly larvaceans, showed high importance for predicting the distribution of smaller tunas. Temperature showed high importance for true tuna Thunnus spp., billfish and mahi-mahi. Statistical models linking predator, prey and abiotic variables were forced with climate projections from an ensemble of earth system models to assess zooplankton and fish larvae distribution changes. Redistributions and declines of zooplankton prey led to minimal changes in abundance and distribution for most larval taxa. However, direct climate change effects, driven partially by ocean warming, led to increases in abundance and northward distribution shifts for multiple larval taxa. These climate change–zooplankton–fish larvae relationships highlight that future distribution and abundance changes of predators can be dampened when assessing impacts of prey availability changes. We also show that in a resource-limited system, key pelagic predators, many of which produce lucrative fisheries, are spatiotemporally linked with their preferred zooplankton prey.

Biogeographical partitioning of ecological communities has been renewed in recent decades to illustrate broad distributional patterns. In the oceans, observational datasets have grown substantially and open new access to test bioregional patterns beyond classically fixed thresholds of endemism to differentiate regions. This work combines a recently collated dataset of 29 different scientific bottom trawl surveys spanning 21 years with network-based clustering to illustrate biogeographical partitions of vast tracts of the Northern Hemisphere's continental shelf seas. Our work contributes to testing bioregionalization patterns in demersal fishes using observational data, totaling 138 227 trawls and > 1700 species, with bipartite network clustering weighted by species occurrence frequencies. We propose eight major biogeographical partitions of marine demersal fish communities across shelf seas in the Northern Hemisphere. These patterns capture known biogeographical boundaries (e.g. North Sea–Baltic Sea, Cape Hatteras) alongside potential transition areas deduced from uncertainty estimates based on shared network nodes between bioregions. The most species-rich areas include the Southeast US Shelf, Temperate Pacific, Northeast Atlantic Shelf, and the Outer European Shelf – corresponding to relatively high endemicity. However, the relatively species-poor partitions including the Baltic Sea and the North and Celtic Seas display comparatively low endemicity (~10%), illustrating apparent statistical differences in partitions captured by bipartite networks and occurrence frequencies that would otherwise be missed using a fixed endemic criterion. Our proposed bioregionalization can be compared against the growing availability of species occurrence data, dispersal limitations, or other quantitative observations of ecological communities.

Balancing food security and biodiversity conservation – two often conflicting objectives – is essential for achieving global goals (e.g. SDG 2 and 15; GBF Targets 1, 3, and 10). While previous studies have explored global or national-level trade-offs, there is a lack of spatially explicit, scenario-based planning frameworks at regional scales to reconcile cropland expansion and biodiversity conservation. The study develops a multi-objective spatial planning framework to assess how future cropland expansion may be optimized to reduce biodiversity impacts while ensuring food security in the northwestern dry geo-eco region of China. It uses a random forest model trained with environmental, socio-economic, and trend variables to project cropland expansion from 2020 to 2030 and identify areas of spatial conflict with biodiversity priority regions. Results reveal intense conflicts in ecologically sensitive areas such as the Altai and Tianshan Mountains. Under a food security-first scenario, expanding 300 000 km² of cropland would result in 167 978 km² of conflict areas and a 12.20% habitat loss rate. In contrast, a biodiversity-priority scenario achieves only 199 782 km² of cropland expansion, reducing habitat loss to 2.39%. A trade-off coordination scenario offers an optimized balance, enabling 300 000 km² of cropland expansion while protecting 30% of biodiversity priority areas and limiting habitat loss to 3.52%. This study highlights a novel framework for integrating food security and biodiversity conservation, offering spatially explicit strategies to support region-specific sustainable land-use planning.

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.