Ecography最新文献

Himalayan orogeny and consequent climatic changes, such as the strengthening of the Asian monsoon, are considered as two main drivers in shaping local biogeography. The mountainous Sinopoda spiders, which are widely distributed in East Asia and Southeast Asia and especially abundant in the mountains near the Himalayas, represent an ideal model lineage for investigating Himalayan biogeography. This is due to their high diversity, limited dispersal ability, and wide elevational distribution, ranging from sea level up to 3500 meters. We investigated the evolutionary history of Sinopoda spiders, focusing on ecological, molecular, and morphological traits in relation to local geological events and fluctuations in Neogene (23.0–2.6 Ma) Asian monsoon patterns. Distribution modeling results show that extant Sinopoda spiders are sensitive to humidity fluctuations. They are mainly distributed in two distinct habitats: areas with moderate precipitation at high altitude (relatively cold) and areas with high precipitation at low altitude (relatively warm). The biogeographical and elevation reconstruction analyses show that as the Himalayas rose and the Asian monsoon intensified, Sinopoda spiders (Sparassidae: Heteropodinae) moved out of the Himalayas (ca 18.1 Ma) then ‘down' the rising mountain slopes (ca 9.6 Ma). We then see a secondary return to the mountains (ca 3.3 Ma) as the severity of the East Asian monsoon decreased. We hypothesize that our ‘out of Himalaya' dispersal pattern hypothesis will also apply to closely related spider groups with limited ballooning ability (e.g. Lycosidae, Thomisidae) or other organisms with low vagility (such as herpetofauna) that are sensitive to humidity and possess similar geographical distributions.

The relative importance of the different processes that determine the distribution of species and the assembly of communities is a key question in ecology. The distribution of any individual species is affected by a wide range of environmental variables as well as through interactions with other species; the resulting distributions determine the pool of species available to form local communities at fine spatial scales. A challenge in community ecology is that these interactions (e.g. competition, facilitation, etc.) often are not directly measurable. Here, we used hierarchical modelling of species communities (HMSC), a recently developed framework for joint species distribution modelling, to estimate the role of biotic effects alongside environmental factors using latent variables. We investigate the role of these factors determining species distributions in communities of Artiodactyla, Perissodactyla and Proboscidea in the Afrotropics, an area of peak species richness for hoofed mammals. We also calculate pairwise trait dissimilarity between these species, from a mixture of morphological and behavioural traits, and investigate the relationship between dissimilarity and estimated residual co-occurrence in the model. We find that while ungulate distributions appear to be predominantly determined (~ 70%) by climatic variables, such as precipitation, a substantial proportion of the variance in ungulate species distributions (~ 30%) can also be attributed to modelled latent variables that likely represent a combination of dispersal barriers and biotic factors. Although we find only a weak relationship between residual co-occurrence and trait dissimilarity, we suggest that our results may show evidence that biotic factors, likely influenced by historical barriers to species dispersal, are important in determining species communities over a continental area. The HMSC framework can be used to provide insight into factors affecting community assembly at broad scales, and to make more powerful predictions about future species distributions as we enter an era of increasing impacts from anthropogenic change.

While much attention has been paid to the climatic controls of species' range limits, other factors such as dispersal limitation are also important. Temperature is an important control of the distribution of coastal mangrove forests, and mangrove expansion at multiple poleward range limits has been linked to increasing temperatures. However, mangrove abundances at other poleward range limits have been surprisingly insensitive to climate change, indicating other drivers of range limitation. For example, along the west coast of North America, the poleward mangrove range limits are found on the Baja California and mainland coasts of Mexico, between 26°48ʹ and 30°18ʹN. Non-climatic factors may play an important role in setting these range limits as 1) the abundance of range limit populations has been relatively insensitive to climate variability and 2) an introduced population of mangroves has persisted hundreds of kilometers north of the natural range limits. We combined a species distribution model with a high-resolution oceanographic transport model to identify the roles of climate and dispersal limitation in controlling mangrove distributions. We identified estuarine habitat that is likely climatically suitable for mangroves north of the current range limits. However, propagules from current mangrove populations are unlikely to reach these suitable locations due to prevailing ocean currents and geomorphic factors that create a patchy distribution of estuarine habitat with large between-patch distances. Thus, although climate change is driving range shifts of mangroves in multiple regions around the world, dispersal is currently limiting poleward mangrove expansion at several range limits, including the west coast of North America.

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

Island ecosystems are particularly susceptible to the impacts of invasive species. Many rare and endangered species that are endemic to islands are negatively affected by invasions. Past studies have shown that the establishment of non-native species on islands is related to native plant richness, habitat heterogeneity, island age, human activity, and climate. However, it is unclear whether the factors promoting establishment (i.e. the formation of self-sustaining populations) also promote subsequent invasion (i.e. spread and negative impacts). Using data from 4308 non-native plant species across 46 islands and archipelagos globally, we examined which biogeographic characteristics influence established and invasive plant richness using generalized linear models nested within piecewise structural equation models. Our results indicate that anthropogenic land use (i.e. human modification) is strongly associated with establishment but not invasion, that climate (maximum monthly temperature) is strongly associated with invasion but not establishment, and that habitat heterogeneity (represented by maximum elevation and island area) is strongly associated with both establishment and invasion. Island isolation explains native plant richness well, but is not associated with established and invasive plant richness, likely due to anthropogenic introductions. We conclude that anthropogenic land use on islands is likely to be a proxy for the number of introductions (i.e. propagule pressure), which is more important for establishment than invasion. Conversely, islands with more diverse habitats and favorable (warm) climate conditions are likely to contain more available niche space (i.e. ‘vacant niches') which create opportunities for both establishment and invasion. By evaluating multiple stages of the invasion process, we differentiate between the biogeographic characteristics that influence plant establishment (which does not necessarily lead to ecological impacts) versus those that influence subsequent plant invasion (which does lead to negative impacts).

Every night during spring and autumn, the mass movement of migratory birds redistributes bird abundances found on the ground during the day. However, the connection between the magnitude of nocturnal migration and the resulting change in diurnal abundance remains poorly quantified. If departures and landings at the same location are balanced throughout the night, we expect high bird turnover but little change in diurnal abundance (stream-like migration). Alternatively, migrants may move simultaneously in spatial pulses, with well-separated areas of departure and landing that cause significant changes in the abundance of birds on the ground during the day (wave-like migration). Here, we apply a flow model to data from weather surveillance radars (WSR) to quantify the daily fluxes of nocturnally migrating birds landing and departing from the ground, characterizing the movement and stopover of birds in a comprehensive synoptic scale framework. We corroborate our results with independent observations of the diurnal abundances of birds on the ground from eBird. Furthermore, we estimate the abundance turnover, defined as the proportion of birds replaced overnight. We find that seasonal bird migration chiefly resembles a stream where bird populations on the ground are continuously replaced by new individuals. Large areas show similar magnitudes of take-off and landing, coupled with relatively small distances flown by birds each night, resulting in little change in bird densities on the ground. We further show that WSR-inferred landing and take-off fluxes predict changes in eBird-derived abundance turnover rate and turnover in species composition. We find that the daily turnover rate of birds is 13% on average but can reach up to 50% on peak migration nights. Our results highlight that WSR networks can provide real-time information on rapidly changing bird distributions on the ground. The flow model applied to WSR data can be a valuable tool for real-time conservation and public engagement focused on migratory birds' daytime stopovers.

Key Biodiversity Areas (KBAs) represent the largest global network of sites critical to the persistence of biodiversity, which have been identified against standardised quantitative criteria. Sites that hold very high biodiversity value or potential are given specific attention on site-based conservation targets of the Kunming-Montreal Global Biodiversity Framework (GBF), and KBAs are already used in indicators for the GBF and the Sustainable Development Goals. However, most of the species that trigger KBA status are birds and to maximise benefits for biodiversity under the actions taken to fulfil the GBF, countries need to update their KBAs to represent important sites across multiple taxa. Here we introduce KBAscope, an R package to identify potential KBAs using multiple taxonomic groups. KBAscope provides flexible, user-friendly functions to edit species data (population, range maps, area of occupancy, area of habitat and localities); apply KBA criteria; and generate outputs to support the delineation and validation of KBAs. The details of the analysis – such as the spatial units tested or the KBA criteria applied – can be decided according to the scope of the analysis. We demonstrate the functionality of KBAscope by using it to identify potential KBAs in Greece based on multiple terrestrial taxonomic groups and four sizes of grid cells (4 km², 25 km², 100 km², 225 km²).

Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance into unobserved areas or periods of time. However, such models often poorly predict the distribution of animal space-use intensity beyond the place and time of data collection, presumably because space-use behaviors vary between individuals and environmental contexts. Similarly, ecological inference based on habitat selection models could be muddied or biased due to unaccounted individual and context dependencies. Here, we present a modeling workflow designed to allow transparent variance-decomposition of habitat-selection patterns, and consequently improved inferential and predictive capacities. Using global positioning system (GPS) data collected from 238 individual pronghorn, Antilocapra americana, across three years in Utah, USA, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30 × 30 m resolution but an extent of 220 000 km². We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models – variability in habitat selection – into a tool to understand and predict species-habitat associations across space and time.