Ecography最新文献

Understanding how and why β-diversity varies along latitude is a long-standing challenge in community ecology and rarely addressed in both space and time. We aimed to explore the spatiotemporal variations in macroinvertebrate β-diversity and their underlying drivers in eight biogeographic regions covering a substantial latitudinal gradient of more than 40 degrees. By combining β-diversity partitioning and distance decay of community similarity analyses, we found that subtropical β-diversity varies more in space relative to variation in time compared with temperate β-diversity, as we predicted. This is probably because subtropical β-diversity is shaped by species–environment sorting (SS), caused by habitat heterogeneity and species specialization, more strongly in space relative to time than temperate β-diversity. Our study highlights the importance of SS in shaping latitudinal gradients of β-diversity in space and time.

Plant–plant interactions regulate plant community structure and function. Shifts in these interactions due to global climate change, mediated through disproportional increases of certain species or functional groups, may strongly affect plant community properties. Still, we lack knowledge of community-level effects of climate-driven changes in biotic interactions. We examined plant community interactions by experimentally removing a dominant functional group, graminoids, in semi-natural grasslands in Southern Norway. To test whether the effect of graminoid removal varied with climate, the experiment was replicated across broad-scale temperature and precipitation gradients. To quantify community-level interactions across sites, we tested for changes in the remaining vascular community (i.e. forbs) cover, richness, evenness, and functional traits reflecting leaf-economic investment and plant size over five years. The effect of graminoid removal on forb community structure and functioning varied over time, and along the climate gradients. Forb cover increased in response to graminoid removal, especially at warmer sites. Species richness increased following removal irrespective of climate, whilst evenness increased under warmer and wetter conditions irrespective of removal. No climate or removal effect was found for species turnover. Functional trait responses varied along the precipitation gradient – compared to controls, forb mean SLA decreased in drier conditions after graminoid removal. Leaf thickness increased under cooler and drier conditions irrespective of removal. These community structure alterations demonstrate stronger competitive interactions between forbs and graminoids under warmer conditions, whilst functional trait responses indicate a facilitative effect of graminoids under drier conditions. This indicates that both competition and facilitation regulate plant communities, suggesting complexity when scaling from populations to communities. Finally, both temperature and precipitation determine the direction and intensity of biotic interactions, with ecosystem-wide implications for forb persistence and ecosystem functioning under future climates. Further work is needed to generalise the role of changing interactions in mediating community responses to climate change.

Dengue and yellow fever have complex cycles, involving urban and sylvatic mosquitoes, and non-human primate hosts. To date, efforts to assess the effect of climate change on these diseases have neglected the combination of such crucial factors. Recent studies only considered urban vectors. This is the first study to include them together with sylvatic vectors and the distribution of primates to analyse the effect of climate change on these diseases. We used previously published models, based on machine learning algorithms and fuzzy logic, to identify areas where climatic favourability for the relevant transmission agents could change: 1) favourable areas for the circulation of the viruses due to the environment and to non-human primate distributions; 2) the favourability for urban and sylvatic vectors. We obtained projections of future transmission risk for two future periods and for each disease, and implemented uncertainty analyses to test for predictions reliability. Areas currently favourable for both diseases could keep being climatically favourable, while global favourability could increase a 7% for yellow fever and a 10% increase for dengue. Areas likely to be more affected in the future for dengue include West Africa, South Asia, the Gulf of Mexico, Central America and the Amazon basin. A possible spread of dengue could take place into Europe, the Mediterranean basin, the UK and Portugal; and, in Asia, into northern China. For yellow fever, climate could become more favourable in Central and Southeast Africa; India; and in north and southeast South America, including Brazil, Paraguay, Bolivia, Peru, Colombia and Venezuela. In Brazil, favourability for yellow fever will probably increase in the south, the west and the east. Areas where the transmission risk spread is consistent to the dispersal of vectors are highlighted in respect of areas where the expected spread is directly attributable to environmental changes. Both scenarios could involve different prevention strategies.

Climate change affects biodiversity in a variety of ways, necessitating the exploration of multiple climate dimensions using appropriate metrics. Despite the existence of several climate change metrics tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed ‘climetrics' which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are implemented, including 1) standardized local anomalies; 2) changes in probabilities of local climate extremes; 3) changes in areas of analogous climates; 4) novel climates; 5) changes in distances to analogous climates; and 6) climate change velocity. For climate change velocity, three different algorithms are implemented in the package including; 1) distanced-based velocity (‘dVe'); 2) threshold-based velocity (‘ve'); and 3) gradient-based velocity (‘gVe'). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The 'climetrics' R package is integrated with the 'rts' package for efficient handling of raster time-series data. The functions in 'climetrics' are designed to be user-friendly, making them suitable for less-experienced R users. Detailed descriptions in help pages and vignettes of the package facilitate further customization by advanced users. In summary, the 'climetrics' R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.

Fine-scale spatial climate variation fosters biodiversity and buffers it from climate change, but ecological studies are constrained by the limited accessibility of relevant fine-scale climate data. In this paper we introduce a novel form of species distribution model that uses species occurrences to predict high-resolution climate variation. This new category of ‘bioclimate' data, representing micro-scale climate as experienced by one or more species of interest, is a useful complement to microclimate data from existing approaches. The modeling method, called BISHOP for ‘bioclimate inference from species' high-resolution occurrence patterns,' uses data on species occurrences, coarse-scale climate, and fine-scale physiography (e.g. terrain, soil, vegetation) to triangulate fine-scale bioclimate patterns. It works by pairing a climate-downscaling function predicting a latent bioclimate variable, with a niche function predicting species occurrences from bioclimate. BISHOP infers how physiography affects bioclimate, estimates how these effects vary geographically, and produces high-resolution (10 m) maps of bioclimate over large regions. It also predicts species distributions. After introducing this approach, we apply it in an empirical study focused on topography and trees. Using data on 216 North American tree species, we document the biogeographic patterns that enable BISHOP, estimate how four terrain variables (northness, eastness, windward exposure, and elevational position) each influence three climate variables, and use these results to produce downscaled maps of tree-specific bioclimate. Model validation demonstrates that inferred bioclimate outperforms macroclimate in predicting distributions of separate species not used during inference, confirming its ecological relevance. Our results show that nearby bioclimates can differ by 5°C in temperature and twofold in moisture, with equator-facing, east-facing, windward-facing, and locally elevated sites exhibiting hotter, drier bioclimates on average. But these effects vary greatly across climate zones, revealing that topographically similar landscapes can differ strongly in their bioclimate variation. These results have important implications for micrometeorology, biodiversity, and climate resilience.

Climatic changes can affect species distributions, population abundance, and evolution. Such organismal responses could be determined by the amount and quality of available habitats, which can vary independently. In this study, we assessed changes in habitat quantity and quality independently to generate explicit predictions of the species' responses to climatic changes between Last Glacial Maximum (LGM) and present day. We built ecological niche models for genetic groups within 21 reptile, mammal, and plant taxa from the Baja California peninsula inhabiting lowland or highland environments. Significant niche divergence was detected for all clades within species, along with significant differences in the niche breadth and area of distribution between northern and southern clades. We quantified habitat quantity from the distribution models, and most clades showed a reduction in distribution area towards LGM. Further, niche marginality (used as a measure of habitat quality) was higher during LGM for most clades, except for northern highland species. Our results suggest that changes in habitat quantity and quality can affect organismal responses independently. This allows the prediction of genomic signatures associated with changes in effective population size and selection pressure that could be explicitly tested from our models.

Migration is a prevalent strategy among birds used to track seasonal resources throughout the year. Individual and population-level migratory movements provide insight to life-history variation, carry-over effects, and impacts of climate change. Our understanding of how geographic variation in a species' breeding or wintering grounds can impact migration distances is limited. However, changes in migration distances can have important fitness consequences for individuals and conservation implications for populations, particularly if migratory connectivity is altered during the annual cycle. In this study, we use three decades of data from the United States Geological Survey Bird Banding Laboratory for six migratory species of Arctic and subarctic breeding geese. We employ a Bayesian hierarchical framework to test if the distance between breeding and wintering locations has changed over time, while accounting for the latitude of the breeding grounds. A model that included only a temporal trend estimated the average rate of change in migration distance, across all six species, at −3.0 km/year over the period 1990–2019. Five of the six species showed a significant decrease in migration distances. Including an interaction effect with breeding latitude revealed that the reduction in migration distance was strongest in the southernmost populations for four of the six species. For those species, migration distance in northern populations were all either relatively unchanged or increasing. This indicates that southern breeding populations of geese had a stronger association with the observed spatiotemporal changes in wintering ranges, potentially influenced by a combination of climatic and biotic factors (e.g. resource availability or competitive interactions) that uniquely impact these populations. Abundant, long-term banding data shows promise for use in illuminating changes in migratory patterns under climate change, leading to improved management and conservation outcomes, from regional to continental scales.

Biodiversity metrics have become a ubiquitous component of conservation assessments across scales. However, whilst indices have become increasingly widely used, their ability to perform in the face of different biases has remained largely untested under realistic conditions. Citizen science data are increasingly available, but present new challenges and biases, thus understanding how to use them effectively is essential. Here, we built a virtual world incorporating BirdLife data and accounting for their biases, then explored how well commonly-used diversity metrics could estimate known values across a suite of representative scenarios. We used predictive modelling to model bird diversity globally and overcome biases using the approaches found most accurate in prior assessments. Performance was highly variable across the different types of biases, but in many instances Simpson's index performed best, followed by Hill numbers, whereas Pielou's index was almost universally worst. From standardised tests, we then applied these metrics to eBird data using 611 520 112 samples of 10 359 species of bird (around 88% of known species), to reconstruct global diversity patterns at five and ten km resolutions. However, when we mapped out diversity using Maxent based on these indices, Simpson's index generally over-predicted diversity, whereas Hill numbers were more conservative. Based on an average of the better projected indices, one can map out diversity across resolutions and overcome biases accurately predicting diversity patterns even for data-poor areas, but if a single metric is used, Hill numbers are most robust to bias. Going forward, this workflow will enable standardized best practices for diversity mapping based on a clear understanding of the performance of different metrics.$$

Understanding spatial patterns of biodiversity within the context of long-term climatic shifts is of high importance, particularly in the face of contemporary climate change. In comparison to aboveground taxa, subterranean organisms respond to changing climates with generally much lower dispersal and recolonization potential, yet possible persistence in refugial groundwater habitats under ice-shields. However, knowledge on general and geographically large-scale effects of glaciation on contemporary groundwater biodiversity patterns is still very limited. Here, we tested how Late Pleistocene glaciation influenced the diversity and distribution of 36 groundwater amphipod species in Alpine and peri-Alpine regions, characterized by extensive glaciation cycles, and how its legacy explains contemporary diversity patterns. We based our analysis on an unprecedented density of ~ 1000 systematic sampling sites across Switzerland. Using presence–absence data, we assessed biodiversity and species' ranges, and calculated for each site within-catchment distance to the Last Glacial Maximum (LGM) glacier extent. We then applied a sliding window approach along the obtained distance gradient from LGM ice-covered to ice-free sites to compute biodiversity indices reflecting local richness, regional richness, and differentiation, respectively. We found a strong signal of the LGM ice extent on the present-day distribution of groundwater amphipods. Our findings revealed pronounced species turnover and spatial envelopes of individual species' occurrences in formerly ice-covered, ice-free, or transitional zones, respectively. While local richness remained constant and low along the LGM distance gradient, groundwater communities in LGM ice-covered areas were more similar to each other and had lower gamma diversities and decreased occurrence probabilities per sliding window compared to communities in Pleistocene ice-free areas. These results highlight the significant impact of Pleistocene glaciation on shaping biodiversity patterns of subterranean communities and imprinting contemporary distribution of groundwater organisms.