Ecography最新文献

The spatial scale at which an environmental variable is summarized can have considerable impacts on ecological inference of species distribution and abundance. While several analytical approaches have emerged to determine biologically relevant spatial scales – the spatial scale that most strongly influences the ecological patterns observed – identifying key ecological drivers of scale of effect is still underway. Additionally, several predicted ecological drivers of scale of effect can vary across space and time, but little research on spatiotemporal patterns has occurred. Here, I assessed spatial and temporal variation in scales of effect across 32 North American bird species using 18 years of empirical data from the North American breeding bird survey. Scale estimation was then coupled with trait-based analyses and hypotheses testing of underlying processes of spatial and temporal variation in scales of effect. All 32 species tested exhibited varied scales of effect across years (average annual scales of effect ranging from 0.2 to 4.97 km) and Bird Conservation Regions (BCR), with spatial variability being the most pronounced. Trait-based analyses revealed a contrary relationship between hand-wing index, body size, and scale of effect, though the strength of this relationship was contingent on migratory status. Temporal variation in scales of effect was best explained by changes in human development over time, indicating that avian space use can be explained by an increasing human footprint. Additionally, relative population size, consistent with theoretical predictions stemming from density-dependent population dynamics, explained notable variation in spatial and temporal scales of effect. These findings contribute to the growing landscape ecology literature by providing empirical evidence for hypothesized drivers of scales of effect. By delineating species-specific scales of effect and elucidating their ecological drivers, this study enhances our understanding of spatial and temporal scales in ecological processes, aiding conservation efforts in a rapidly changing world.

There is an increasing need for ecosystem-level distribution models (EDMs) and a better understanding of which factors affect their quality. We investigated how the performance and transferability of EDMs are influenced by 1) the choice of predictors and 2) model complexity. We modelled the distribution of 15 pre-classified ecosystem types in Norway using 252 predictors gridded to 100 × 100 m resolution. The ecosystem types are major types in the ‘Nature in Norway' system mainly defined by rule-based criteria such as whether soil or specific functional groups (e.g. trees) are present. The predictors were categorised into four groups, of which three represented proxies for natural, anthropogenic, or terrain processes (‘ecological predictors') and one represented spectral and structural characteristics of the surface observable from above (‘surface predictors'). Models were generated for five levels of model complexity. Model performance and transferability were evaluated with data collected independently of the training data. We found that 1) models trained with surface predictors only performed considerably better and were more transferable than models trained with ecological predictors, and 2) model performance increased with model complexity, levelling off from approximately 10 parameters and reaching a peak at approximately 20 parameters, while model transferability decreased with model complexity. Our findings suggest that surface predictors enhance EDM performance and transferability, most likely because they represent discernible surface characteristics of the ecosystem types. A poor match between the rule-based criteria that define the ecosystem types and the ecological predictors, which represent ecological processes, is a plausible explanation for why surface predictors better predict the distribution of ecosystem types. Our results indicate that, in most cases, the same models are not well suited for contrasting purposes, such as predicting where ecosystems are and explaining why they are there.

The opening of habitats associated with the emergence of C₄ grasslands during the Neogene had a massive influence on the evolution of plant and animal communities. Strikingly, the impacts of grassland expansion on species diversification in Africa, where the largest surface of grasslands and savannas in the world is located, are not well understood. To explore the impact of habitat opening, we investigate the evolution of noctuid stemborers, a group of moths mostly associated with open habitats, and whose diversity is centered in the Afrotropics. We generate a dated molecular phylogeny for ca 80% of the known stemborer species, and assess the role of habitat opening on the evolutionary trajectory of the group through a combination of parametric historical biogeography, ancestral character state estimation, life history traits and habitat-dependent diversification analyses. Our results support an origin of stemborers in Southern and East Africa ca 20 million years ago (Ma), with range expansions linked to the increased availability of open habitats to act as dispersal corridors, and closed habitats acting as potent barriers to dispersal. Early specialization on open habitats was maintained over time, with shifts towards closed habitats being rare and invariably unidirectional. Analyses of life history traits showed that habitat changes involved specific features likely associated with grassland adaptations, such as variations in larval behavior and color. We compare these findings to those previously inferred for an Afrotropical butterfly group that diversified roughly in parallel with the stemborers but distributed predominantly in closed habitats. Remarkably, these two groups show nearly opposite responses in relation to habitat specialization, whether in terms of biogeographical patterns, or in terms of rates of transition between open and closed habitats. We conclude that habitat opening played a major role in the evolutionary history of Afrotropical lineages through dispersal and adaptation linked to habitat shifts.

Forest–grassland ecotone (FGE) has essential ecological and economic value. Unfortunately, it is impacted greatly by environmental changes and anthropogenic disturbance, and is considered one of the most severely threatened biomes in China. To protect Chinese FGE, identifying its exact boundary and exploring its landscape structure dynamic are badly needed, especially on nationwide scale at one-year temporal resolution. Here, we mapped the annual FGE distribution of China from 1990 to 2020, investigated its changing trends of area, location and landscape patterns, and revealed the underlying driving factors. Our results showed that FGE area over the 31 years totaled 1 011 870 km², covering about 10.54% of China's land. The FGE area first increased from 1990 and peaked in 1999, and then kept decreasing until 2020. The FGE gravity center has moved accumulatively 590.15 km over the 31 years, with the net moving distance of 228.76 km southwestward. From 1990 to 2020, forest area increased continuously while grassland and cropland area decreased, but these three landscape types had been dominating the FGE. The increase in forest area was largely converted from grassland. The decline in grassland mainly resulted from its conversion into cropland and forest. Meanwhile, the conversion of cropland to grassland supplemented grassland loss to a certain extent. At landscape level, the total area with decreased fragmentation is larger than that with increased fragmentation. Returning Farmland to Grassland Project and land reclamation were primary drivers for changes of fragmentation in the northern and middle part of the FGE, while temperature and precipitation were primary drivers in southern part. Our results will improve the understanding into the dynamic trends of distribution and pattern of FGE at nationwide scale, and thus help to optimize the designing of ecological projects and protective schemes for FGE as a unique and integral biome.

Macroscale environmental gradients can have contrasting effects on organisms that occupy different vertical niches, but we have little understanding of how this might result in different macroscale diversity patterns in ground and arboreal communities. We also have little understanding of how different dimensions of diversity, such as functional and phylogenetic diversity, vary along macroscale environmental gradients. Here we examine latitudinal and elevational patterns of different dimensions of diversity for both ground and arboreal assemblages in Neotropical savanna ants. The study was based on ant species occurring at 32 sites covering a 22° range of latitude and > 1000 m range in elevation in Brazil. Functional and phylogenetic richness were positively correlated with species richness, all increasing with latitude. However, the greater phylogenetic richness on the ground than in trees did not simply reflect differences in species richness. The mean functional and phylogenetic divergence among species was also greater on the ground than in trees, indicating a stronger role of competition. Both mean functional and phylogenetic divergence showed negative correlations with elevation in trees but not on the ground. In trees, the standardized effect size (taking into account differences in species richness) of mean functional divergence was negatively related to elevation and mean phylogenetic divergence was negatively related to both latitude and elevation. These findings suggest that as temperature decreases the relative importance of environmental filtering in arboreal but not ground communities increases (and that of competition and niche partitioning decreases). Overall, we show that the macroecological patterns of ant species richness that have previously been reported for Brazilian savannas do not adequately represent other dimensions of diversity, and that the representativeness differs between vertical strata. Macroecological patterns of functional and phylogenetic divergence indicate that the relative importance of competition and environmental filtering also differs between vertical strata.

Exploring new approaches and methodologies to characterize species distribution dynamics, instead of solely relying on static spatial patterns, should be a priority for species distribution modelling research. Dynamic occupancy models (here, ‘dynocc models') are a promising tool to capture temporal patterns of distribution change but their spatial accuracy has been shown to be limited. In this study, we evaluated the effectiveness of incorporating neighborhood connectivity effects into the colonization and extinction functions of dynocc models. To accomplish this, we compared dynocc models accounting either for neighborhood connectivity only, for site-level habitat covariates only, or combining both neighborhood and habitat explanations in the models for species extinction and colonization. All models were evaluated for a total of 46 bird species typical of forests and shrublands using data at 1 km² scale from two Catalan breeding bird atlases (CBBA2: 1999–2002 and CBBA3: 2015–2018). Models' predictive performance varied across species between dynocc models incorporating habitat covariates alone and those considering neighborhood connectivity alone. Among species, 68% exhibited a predominant response to habitat effects, 24% showed similar responses for habitat and connectivity effects, and 9% were mostly associated with connectivity effects. Dynocc models combining connectivity and habitat covariates achieved the best predictive performance for most species, with bigger gains for species with similar results from habitat-only and connectivity-only models. However, relative performance gains compared to dynocc models using only habitat or connectivity variables were generally modest for most species. This study shows the benefits of considering more spatially explicit formulations in dynocc models, specifically incorporating neighborhood connectivity into the extinction and colonization processes. Our work also highlights the importance of evaluating different model formulations and assessing which aspects of the model are more important depending on the study species.

The rapid loss of biodiversity in freshwater systems asks for a robust and spatially explicit understanding of species' occurrences. As two complementing approaches, habitat suitability models provide information about species' potential occurrence, while environmental DNA (eDNA) based assessments provide indication of species' actual occurrence. Individually, both approaches are used in ecological studies to characterize biodiversity, yet they are rarely combined. Here, we integrated high-resolution habitat suitability models with eDNA-based assessments of aquatic invertebrates in riverine networks to understand their individual and combined capacity to inform on species' occurrence. We used eDNA sampling data from 172 river sites and combined the detection of taxa from three insect orders (Ephemeroptera, Plecoptera, Trichoptera; hereafter EPT) with suitable habitat predictions at a subcatchment level (2 km²). Overall, we find congruence of habitat suitability and eDNA-based detections. Yet, the models predicted suitable habitats beyond the number of detections by eDNA sampling, congruent with the suitable niche being larger than the realized niche. For local mismatches, where eDNA detected a species but the habitat was not predicted suitable, we calculated the minimal distance to upstream suitable habitat patches, indicating possible sources of eDNA signals from upstream sites subsequently being transported along the water flow. We estimated a median distance of 1.06 km (range 0.2–42 km) of DNA transport based on upstream habitat suitability, and this distance was significantly smaller than expected by null model predictions. This estimated transport distance is in the range of previously reported values and allows extrapolations of transport distances across many taxa and riverine systems. Together, the combination of eDNA and habitat suitability models allows larger scale and spatially integrative inferences about biodiversity, ultimately needed for the management and protection of biodiversity.

Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst perfect analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world.

The present distribution of Siberian boreal forests that are dominated by larches (Larix spp.) is influenced, to an unknown extent, by glacial history. Knowing the past treeline dynamics can improve our understanding of future treeline shifts under changing climate. Here, we study patterns in the genetic variability of Siberian Larix to help unravel biogeographic migration routes since the Last Glacial Maximum (LGM).

We infer the spatial distribution and the postglacial demographic history of Larix using genome-wide single nucleotide polymorphisms (SNPs) derived through genotyping by sequencing (GBS) from 130 individuals sampled across eastern Siberia.

Our analysis gives statistical support for two or three clusters, spanning from western to eastern Siberia. These clusters reveal a genetic structure influenced by isolation resulting from geographical distance, barriers imposed by geographic features, and distinct glacial histories. Assuming three clusters, our demographic inference indicates that the common ancestor of the current Larix populations existed in northeast Siberia well before the LGM. This suggests that Larix persisted in the northern region throughout previous glacials.

Our genetic studies suggest that Larix likely survived the cold LGM in northern refugia, enabling a fast colonization of Siberia. Instead of complete repopulation from southern areas postglacially, the northernmost Larix expansion during the Holocene seems to have benefitted from refugial populations ahead of the treeline. Present-day migration is expected to be slow initially, due to the absence of current refugial populations in the far north, in contrast to the early-Holocene situation.