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Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined 'regions' (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the 'ideal' and 'minimal' criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products., (© 2023 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.)

Perhaps the most widely used quantitative approach in metacommunity ecology is the estimation of the importance of local environment vs. spatial structuring using the variation partitioning framework. Contrary to metapopulation models, however, current empirical studies of metacommunity structure using variation partitioning assume a space-for-dispersal substitution due to the lack of analytical frameworks that incorporate patch connectivity predictors of dispersal dynamics. Here, a method is presented that allows estimating the relative importance of environment, spatial variation and patch connectivity in driving community composition variation within metacommunities. The proposed approach is illustrated by a study designed to understand the factors driving the structure of a soft-bottom marine polychaete metacommunity. Using a standard variation partitioning scheme (i.e. where only environmental and spatial predictors are used), only about 13% of the variation in metacommunity structure was explained. With the connectivity set of predictors, the total amount of explained variation increased up to 51% of the variation. These results highlight the importance of considering predictors of patch connectivity rather than just spatial predictors. Given that information on connectivity can be estimated by commonly available data on species distributions for a number of taxa, the framework presented here can be readily applied to past studies as well, facilitating a more robust evaluation of the factors contributing to metacommunity structure., (© 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.)

The distribution of species is mostly influenced by climate synergistic effects and land use. The prediction of endangered species is dependent on fine-scale environmental features, especially in forests. The capture of fine-scale suitable habitats is bounded by low spatial resolution and coarse categorization. In this study, we improved the land use information and forest spatial detail through high forest thematic resolution land use data. To understand the relative influence of environmental factors, suitable habitat models for Chinese endangered tree species (Firmiana danxiaensis) under three climate and high forest thematic resolution land use conditions was constructed. We also assess F. danxiaensis 's response to climate and land use, and differential performance in land use on the suitable habitat. The area of suitable habitat for F. danxiaensis grows slowly under the SSP1-2.6 (shared social pathway scenario) scenario, decreases by 21.33% from the present to 2090 under the SSP2-4.5 scenario, and expands significantly under the SSP5-8.5 scenario. We conclude that high forest thematic resolution land use is beneficial in capturing species' requirements for specific habitats and especially necessary in predicting endangered species. At the same time, the distribution of suitable habitats for species is primarily driven by climate, and limited their development by land use. The study shows that the synthetic effects of climate and land use change on F. danxiaensis are positive, with a marked trend toward the northeast. As a consequence, some endangered species are able to benefit from future SSPs scenarios, in particular they respond more strongly to the SSP5-8.5 scenario than to the other scenarios. Assessing the role of changes in the climate and high forest thematic resolution land use on F. danxiaensis will help promote sustainable land use management and contribute to the conservation policy for Chinese endangered species.

Effective conservation efforts and predictions of future impacts on biodiversity depend heavily on publicly available information about species distributions. However, data on species distributions is often patchy, especially in many countries of the Global South where resources for biological surveys have been historically limited. In this study, we use biodiversity ignorance scores to quantify and visualize gaps and biases in biodiversity data for Namibia, with a focus on five terrestrial taxa at a spatial scale of 10 x 10 km. We model the relationship between ignorance scores and socio-geographical variables using generalized additive models for location, scale and shape (GAMLSS). Our findings demonstrate that despite a high volume of occurrence records available on the Global Biodiversity Information Facility (GBIF), publicly available knowledge of Namibia's terrestrial biodiversity remains very limited, with large areas contributing few or no records for key taxa. The exception is birds that have benefitted from a massive influx of data from the citizen science platform eBird. Our study also highlights the importance of citizen science initiatives for biodiversity knowledge and reinforces the usefulness of ignorance scores as a simple intuitive indicator of the relative availability and distribution of species occurrence records. However, further research, biological surveys, and renewed efforts to make existing data held by museums and other institutions widely available are still necessary to enhance biodiversity data coverage in countries with patchy data.

Abstract Biodiversity data records contain inaccuracies and biases. To overcome this limitation and establish robust geographic patterns, ecologists often curate records keeping those that are most suitable for their analyses. Yet, this choice is not straightforward and the outcome of the analysis may vary due to a trade‐off between data quality and volume. This problem is particularly recurrent for less‐studied groups with patchy sampling effort. The latitudinal pattern of mosses richness remains inconsistent across studies and these may emerge purely from sampling artefacts. Our main objective here is to assess the effect of different curation criteria on this spatial pattern in the Temperate Northern Hemisphere (above 20° latitude). We contrasted the geographical distribution of moss species records and the latitude‐species richness relation obtained under different data curation scenarios. These scenarios comprehend five sources of taxonomical standardisations and eight data cleaning filters. The analyses are based on the selection of well‐surveyed cells at 100 km cell resolution. The application of some ‘data curation scenarios’ severely affects the number of records selected for analysis and substantially changes the proportion of richness per cell. The sensitivity to data curation becomes detectable at regional and at the cell scales showing a large shift in the latitudinal richness peak in Europe, from 60° N to 45° N latitude, when only preserved specimens are selected and duplicates based on date of collection and coordinates are excluded. Our results stress the importance of justifying the criteria used for filtering biodiversity data retrieved from biodiversity databases to avoid detecting misleading patterns. Curating records under particular criteria compromises the information in some areas displaying different spatial information of mosses. This problem can be ameliorated if data filtering is combined with identifying well‐surveyed cells, render relatively constant results under different combinations of filtering even for less well‐known groups such as mosses.

Abstract Due to historical under‐sampling of the deep ocean, the distributional ranges of mesopelagic zooplankton are not well documented, leading to uncertainty about the mechanisms that shape midwater zooplankton community composition. Using a combination of DNA metabarcoding (18S‐V4 and mtCOI) and trait‐based analysis, we characterized zooplankton diversity and community composition in the upper 1000 m of the northeast Pacific Ocean. We tested whether the North Pacific Transition Zone is a biogeographic boundary region for mesopelagic zooplankton. We also tested whether zooplankton taxa occupying different vertical habitats and exhibiting different ecological traits differed in the ranges of temperature, Chl‐a, and dissolved oxygen conditions inhabited. The depth of the maximum taxonomic richness deepened with increasing latitude in the North Pacific. Community similarity in the mesopelagic zone also increased in comparison with the epipelagic zone, and no evidence was found for a biogeographic boundary between previously delineated mesopelagic biogeochemical provinces. Epipelagic zooplankton exhibited broader temperature and Chl‐a ranges than mesopelagic taxa. Within the epipelagic, taxa with broader temperature and Chl‐a ranges also had broader distributional ranges. However, mesopelagic taxa were distributed across wider dissolved oxygen ranges, and within the mesopelagic, only oxygen ranges covaried with distributional ranges. Environmental and distributional ranges also varied among traits, both for epipelagic taxa and mesopelagic taxa. The strongest differences in both environmental and distributional ranges were observed for taxa with or without diel vertical migration behavior. Our results suggest that species traits can influence the differential effects of physical dispersal and environmental selection in shaping biogeographic distributions.

A major challenge in predicting species' distributional responses to climate change involves resolving interactions between abiotic and biotic factors in structuring ecological communities. This challenge reflects the classical conceptualization of species' regional distributions as simultaneously constrained by climatic conditions, while by necessity emerging from local biotic interactions. A ubiquitous pattern in nature illustrates this dichotomy: potentially competing species covary positively at large scales but negatively at local scales. Recent theory poses a resolution to this conundrum by predicting roles of both abiotic and biotic factors in covariation of species at both scales, but empirical tests have lagged such developments. We conducted a 15-y warming and herbivore-exclusion experiment to investigate drivers of opposing patterns of covariation between two codominant arctic shrub species at large and local scales. Climatic conditions and biotic exploitation mediated both positive covariation between these species at the landscape scale and negative covariation between them locally. Furthermore, covariation between the two species conferred resilience in ecosystem carbon uptake. This study thus lends empirical support to developing theoretical solutions to a long-standing ecological puzzle, while highlighting its relevance to understanding community compositional responses to climate change.

Abstract Large‐scale monitoring of seasonal animal movement is integral to science, conservation and outreach. However, gathering representative movement data across entire species ranges is frequently intractable. Citizen science databases collect millions of animal observations throughout the year, but it is challenging to infer individual movement behaviour solely from observational data. We present BirdFlow, a probabilistic modelling framework that draws on citizen science data from the eBird database to model the population flows of migratory birds. We apply the model to 11 species of North American birds, using GPS and satellite tracking data to tune and evaluate model performance. We show that BirdFlow models can accurately infer individual seasonal movement behaviour directly from eBird relative abundance estimates. Supplementing the model with a sample of tracking data from wild birds improves performance. Researchers can extract a number of behavioural inferences from model results, including migration routes, timing, connectivity and forecasts. The BirdFlow framework has the potential to advance migration ecology research, boost insights gained from direct tracking studies and serve a number of applied functions in conservation, disease surveillance, aviation and public outreach.

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

Species around the world are shifting their ranges in response to climate change. To make robust predictions about climate-related colonizations and extinctions, it is vital to understand the dynamics of range edges. This study is among the first to examine annual dynamics of cold and warm range edges, as most global change studies average observational data over space or over time. We analyzed annual range edge dynamics of marine fishes-both at the individual species level and pooled into cold- and warm-edge assemblages-in a multi-decade time-series of trawl surveys conducted on the Northeast US Shelf during a period of rapid warming. We tested whether cold edges show stronger evidence of climate tracking than warm edges (due to non-climate processes or time lags at the warm edge; the biogeography hypothesis or extinction debt hypothesis), or whether they tracked temperature change equally (due to the influence of habitat suitability; the ecophysiology hypothesis). In addition to exploring correlations with regional temperature change, we calculated species- and assemblage-specific sea bottom and sea surface temperature isotherms and used them to predict range edge position. Cold edges shifted further and tracked sea surface and bottom temperature isotherms to a greater degree than warm edges. Mixed-effects models revealed that for a one-degree latitude shift in isotherm position, cold edges shifted 0.47 degrees of latitude, and warm edges shifted only 0.28 degrees. Our results suggest that cold range edges are tracking climate change better than warm range edges, invalidating the ecophysiology hypothesis. We also found that even among highly mobile marine ectotherms in a global warming hotspot, few species are fully keeping pace with climate.

Abstract Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf‐nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities.

Abstract Broad‐scale ecological research on species distributions commonly presumes that the correlative relationships discovered are stationary over space. This is an assumption of most species distribution models (SDMs) that combine observations of species occurrence with environmental characteristics to understand current ecological correlates and to predict distributions based on those relationships. However, ecological relationships may vary spatially because of changes in the environment (i.e., resource availability) or the organism itself (i.e., local adaptation). Discovering this within‐species variation typically requires dense datasets over large geographic areas, which are now being provided by the recent proliferation of open‐access biodiversity occurrence records. Using nearly 4000 sampling locations from an open‐access, state‐wide camera‐trapping project, we explore the space‐varying effects of covariates on the distribution of four mammal species at two scales: region‐specific and fine resolution, with the latter estimated using spatially varying coefficients (SVC) models, to understand the scale of spatial variation in ecological relationships. Among the four species tested, the ecological relationships for two were best explained with the regional models, equivocal results for one species, while the SVC model had superior fit and predictive performance for the final species (white‐tailed deer, Odocoileus virginianus). Spatial nonstationarity was more common in relationships with landscape composition characteristics, such as housing density, than in landscape configuration metrics, such as patch richness density. One of the most appealing results of an SVC approach is not only the improved predictions across large landscapes but also understanding how animals are responding to the environment differently at the management unit level. For example, we found that deer's spatially varying relationship with forest cover was best explained by an interactive effect of deer management units (i.e., differences in deer populations) and predator pressure. These findings lead to a new hypothesis about how deer may be differentially using forested environments across space and could be a promising area of future research. Given sufficient data, accounting for nonstationarity in SDMs can show large‐scale ecological patterns while also detecting local level changes in animal ecology in areas small enough that management or protection can be readily implemented.

Abstract Both abiotic and biotic drivers influence species distributions. Abiotic drivers such as climate have received considerable attention, even though biotic drivers such as hybridization often interact with abiotic drivers. We sought to explore the (1) costs of co‐occurrence for ecologically similar species that hybridize and (2) associations between ecological factors and condition to understand how abiotic and biotic factors influence species distributions. For two closely related and ecologically similar songbirds, black‐capped and mountain chickadees, we characterized body condition, as a proxy for fitness, using a 1358‐individual range‐wide dataset. We compared body condition in sympatry and allopatry with several abiotic and biotic factors using species‐specific generalized linear mixed models. We generated genomic data for a subset of 217 individuals to determine the extent of hybridization‐driven admixture in our dataset. Within this data subset, we found that ~11% of the chickadees had hybrid ancestry, and all hybrid individuals had typical black‐capped chickadee plumage. In the full dataset, we found that birds of both species, independent of demographic and abiotic factors, had significantly lower body condition when occurring in sympatry than birds in allopatry. This could be driven by either the inclusion of cryptic, likely poor condition, hybrids in our full dataset, competitive interactions in sympatry, or range edge effects. We are currently unable to discriminate between these mechanisms. Our findings have implications for mountain chickadees in particular, which will encounter more black‐capped chickadees as black‐capped chickadee ranges shift upslope and could lead to local declines in mountain chickadee populations.

Urbanization and agricultural intensification of landscapes are important drivers of global change, which in turn have direct impacts on local ecological communities leading to shifts in species distributions and interactions. Here, we illustrate how human-altered landscapes, with novel ornamental and crop plant communities, result not only in changes to local community diversity of floral-dependent species, but also in shifts in seasonal abundance of bee pollinators. Three years of data on the spatio-temporal distributions of 91 bee species show that seasonal patterns of abundance and species richness in human-altered landscapes varied significantly less compared to natural habitats in which floral resources are relatively scarce in the dry summer months. These findings demonstrate that anthropogenic environmental changes in urban and agricultural systems, here mediated through changes in plant resources and water inputs, can alter the temporal dynamics of pollinators that depend on them. Changes in phenology of interactions can be an important, though frequently overlooked, mechanism of global change.

Systematic conservation planning has a substantial theoretical underpinning that allows optimization of tradeoffs between biodiversity conservation and other socioeconomic goals. However, thistheory assumes perfect spatial information about the locations of biodiversity features (e.g., species distributions). In practice, planners represent well-known taxa and other biodiversity ''surrogates'' inprotected area systems, hoping that unmapped species will also be conserved. However, empirical research finds that surrogates predict species presence imperfectly, and sometimes rather poorly, at scales relevant toplanning, and existing theory provides no further guidance. We developed new theory, explicitly incorporating aspects of spatial scale, for the representation problem when the locations of speciesdistributions are unknown. Using probability theory and simulated and real species distributions, we found that the probability of adequately representing an unmapped species in a protected area system willbe low unless the total fraction of the region being protected is larger than the species representation target. Furthermore, successful conservation depended critically on the relative sizes of the species distributionand of the individual protected areas; fewer, larger protected areas allowed the entire species distribution to fall into an unprotected gap. This scale-dependence varied with the configuration of the protected areasystem, with the conservation objective most likely to be attained if the individual protected areas were hyperdispersed (evenly spaced across the planning region). Using these results, we developed three designprinciples for representing unmapped species in protected areas: (1) The fraction of the region placed in protected areas should be substantially larger than the species-level representation target; (2) Individualprotected areas must be at least one to two orders of magnitude smaller than the unmapped species' distribution; and (3) Protected areas should be evenly dispersed over geographic space. We also performedpreliminary investigations of the effects of surrogates and socio-economic cost data on the probability of adequately representing unmapped species, finding that the primary effect of surrogates may simply be topromote hyperdispersion of protected areas across the planning region, and that seeking to minimize opportunity costs gives poorer conservation results than random protected area placement.

Abstract China is one of the most species‐rich countries in the world, harboring many rare gymnosperms. Following recent human‐led loss of forests, China is now experiencing increases in forest cover resulting from efforts of reforestation schemes. As anthropogenic activities have previously been found to interact with topography in shaping forest cover in China and considering the large human population and the ongoing population increase of the country, it is important to understand the role of anthropogenic pressures relative to environmental drivers for shaping species distributions here. Based on the well‐established relationship between human population density and topography, we propose a hypothesis for explaining species distributions in a country dominated by human activities, predicting that species are more likely to occur in areas of steep topography under medium human population densities compared to low and high human population densities. Using species occurrence data from the Chinese Vascular Plant Distribution Database along with a common SDM method (maximum entropy modeling), we tested this hypothesis. Our results show that steep topography has the highest importance for predicting Chinese gymnosperm species occurrences in general, and threatened species specifically, in areas of medium human population densities. Consequently, these species are more often found in areas of steep terrain, supporting the proposed hypothesis. Results from this study highlight the need to include topographically heterogeneous habitats when planning new protected areas for species conservation.

Species niches have been defined in different ways, variably encompassing abiotic and biotic parameters limiting an organism’s spatial distribution. Climate is often the primary component of the abiotic (fundamental) niche, especially among terrestrial plants. In invasion biology, there is an ongoing debate on the prevalence of niche shifts, which may be linked to divergent traits in a species’ native and invaded ranges, as well as dispersal limitations and invasion lag phases. Using a global dataset, we tested whether shifts occurred in the invader Johnsongrass (Sorghum halepense), which has undergone intercontinental spread as well as habitat-switching within its invaded range in North America. Climate space ordination showed that North America is, on average, colder than Afro-Eurasia, and North American Johnsongrass occupies wetter environments than in its native range. Within North America, both agricultural and non-agricultural ecotypes shifted slightly toward colder climates. Notably, while non-agricultural populations far outnumber agricultural ones, the former occupy a narrower niche (DM = 42.7 ± 0.8) than the latter (DM = 59.3 ± 1.1), implying agriculture’s role in providing suitable environments in otherwise suboptimal climates. Maximum entropy models of agricultural and non-agricultural North American populations showed limited ecotypic differences in current suitability and range expansion under climate change through the rest of this century. We also predicted climatic suitability for Johnsongrass to increase most in the Upper Midwest and Great Plains by 2100. Our results help contextualize the empirical evidence for ecotypic differentiation in Johnsongrass, as well predict future range expansion and damage niches.

The changes in forest cover can determine the survival of terrestrial endangered mammal species in the wild. This study assessed the impacts of forest cover changes on endangered mammal species distribution at global scale aiming to understand how the changes in forest cover may have impacted the distributions of 35 endangered small and large-body terrestrial mammals. There were used forest data obtained from time-series analyses of Landsat images between 2000 and 2014, species occurrence records collected by observations between 2000 and 2015 of Global Biodiversity Information Facility and species range data of International Union for Nature Conservation (IUCN) of the year 2015, to test the ‘natural and resource conditions’ hypothesis. Hypothesis on ‘natural and resource conditions’ produced models with high prediction accuracy of above 70 percent for 88 percent of 35 species models. The changes in forest cover explained species occurrences in 10 percent of all species models. In average, 59 percent of species occurrence records overlapped with species range data. The 51 percent of all species had no occurrence records between 2000 and 2015. Species and forest data collection as well as transnational cooperation for conservation of species roaming in the wild in upland forested areas and in cross-border areas may be critical for endangered mammal species conservation.

Abstract Butternut trees dying from a canker disease were first reported in southwestern Wisconsin in 1967. Since then, the disease has caused extensive mortality of butternut throughout its North American range. The objectives of this study were to quantify changes in butternut populations and density across its range and identify habitat characteristics of sites where butternut is surviving in order to locate regions for potential butternut restoration. The natural range of butternut (Juglans cinerea L.) extends over a large region of eastern N. America encompassing New Brunswick south to North Carolina, north to Minnesota, and southwest to Missouri. Despite the species’ large range, it is typically not a common tree, comprising a relatively minor component of several different forest types. We evaluated change in butternut abundance and volume from current and historic data from 21 states in the eastern United States. We related abundance and volume at two time periods to a suite of ecological and site factors in order to characterize site conditions where butternut survived. We also assessed the current level of butternut mortality across its range. Since the 1980s, the number of butternut trees and butternut volume have decreased by 58% and 44%, respectively, across its US range. Substantial relative decreases in tree numbers and volume occurred in most ecoregion sections. Five environmental variables were found to be significant predictors of butternut presence. The potential impacts of butternut canker are particularly acute as the canker pathogen invasion pushes a rare tree species toward extinction, at least at a local scale. Based on the results presented here, large‐diameter maple/beech/birch stands in dry, upland sites in eastern Minnesota, western Wisconsin, and upstate New York appear to offer the most favorable conditions for butternut growth and survival and thus may be the best stands for planting resistant butternut trees.

Most studies on the biological effects of future climatic changes rely on seasonally aggregated, coarse‐resolution data. Such data mask spatial and temporal variability in microclimate driven by terrain, wind and vegetation, and ultimately bear little resemblance to the conditions that organisms experience in the wild. Here, I present the methods for providing fine‐grained, hourly and daily estimates of current and future temperature and soil moisture over decadal timescales. Observed climate data and spatially coherent probabilistic projections of daily future weather were disaggregated to hourly and used to drive empirically calibrated physical models of thermal and hydrological microclimates. Mesoclimatic effects (cold‐air drainage, coastal exposure and elevation) were determined from the coarse‐resolution climate surfaces using thin‐plate spline models with coastal exposure and elevation as predictors. Differences between micro and mesoclimate temperatures were determined from terrain, vegetation and ground properties using energy balance equations. Soil moisture was computed in a thin upper layer and an underlying deeper layer, and the exchange of water between these layers was calculated using the van Genuchten equation. Code for processing the data and running the models is provided as a series of R packages. The methods were applied to the Lizard Peninsula, United Kingdom, to provide hourly estimates of temperature (100 m grid resolution over entire area, 1 m for a selected area) for the periods 1983–2017 and 2041–2049. Results indicated that there is a fine‐resolution variability in climatic changes, driven primarily by interactions between landscape features and decadal trends in weather conditions. High‐temporal resolution extremes in conditions under future climate change were predicted to be considerably less novel than the extremes estimated using seasonally aggregated variables. The study highlights the need to more accurately estimate the future climatic conditions experienced by organisms and equips biologists with the means to do so. [ABSTRACT FROM AUTHOR]

Ecological citizen science initiatives are growing in popularity with the increasing realisation of the potential for occurrence records to contribute information on biodiversity. Citizen science data are, however, justifiably criticised for misidentification, uneven sampling, incomplete detection or selective reporting.Here, we test the accuracy of citizen science data for UK social wasp (Vespinae) species' distributions. We compared data collected over 2 weeks by members of the public setting out baited traps across the UK and sending captured specimens for expert identification [1294 locations; 6680 wasps; three dominant species Vespula vulgaris (44%), Vespula germanica (44%) and Vespa crabro (6%)], with a four‐decade long‐term data set established by the Bees, Wasps and Ants Recording Society (BWARS).The citizen science data were significantly less spatially biased than the long‐term data, but they were more urban‐biased. Species distribution modelling showed that, for Vespa crabro, just 2 weeks of citizen science generated coverage comparable to more than four decades of expert recording.Overall, we show that citizen science can be an extremely powerful and robust method for mapping insect diversity and distributions. We suggest that cautious combination of citizen science data with long‐term expert surveying could be a highly reliable method for monitoring biodiversity. [ABSTRACT FROM AUTHOR]

Plants growing at the edges of their range limits are expected to be particularly sensitive to changes in precipitation and temperature regimes associated with climatic change. However, non-climatic factors are increasingly recognized as important constraints to species’ range expansions. Therefore, we assessed the effects of soil provenance with respect to the alpine treeline on the germination, growth, and survival of Engelmann spruce (Picea engelmannii) seedlings. Seedlings were grown under controlled conditions in a growth chamber and greenhouse for ninety days in soils collected from four treeline ecotones in the Canadian Rocky Mountains. By controlling seed source and climatic conditions, and eliminating competition and predation, we attribute differences in seedling viability to soil properties that differed across elevation zones and individual treeline sites. Overall, alpine soils originating from beyond the species’ current elevational range were least amenable to growth, and there was some indication of reduced germination and survival in high-elevation soils. Forest soils, which were coarser and more nutrient rich, hosted seedlings with greater above- and below-ground biomass. Thus, the physical and chemical characteristics of alpine soils in our study region may constrain future treeline expansion, underscoring the importance of incorporating soil properties when considering species’ distributions under climate change.

Most experimental studies on adaptation to stressful environments are performed under conditions that are rather constant and rarely ecologically relevant. Fluctuations in natural environmental conditions are ubiquitous and include for example variation in intensity and duration of temperature, droughts, parasite loads, and availability of nutrients, predators and competitors. The frequency and amplitude of many of these fluctuations are expected to increase with climate change. Tolerance curves are often used to describe fitness components across environmental gradients. Such curves can be obtained by assessing performance in a range of constant environmental conditions. In this perspective we briefly list theoretical and experimental evidence why results obtained under constant environmental conditions might be misleading for processes in nature and therefore may not be suitable for predicting fitness and future species distribution and abundance. We further suggest experimental avenues that can provide a better foundation for forecasts of the distribution of biota.

Detailed information on species distributions is crucial for answering central questions in biogeography, ecology, evolutionary biology and conservation. Millions of species occurrence records have been mobilized via international data-sharing networks, but inherent biases, gaps and uncertainties hamper broader application. In my PhD thesis, I presented the first comprehensive analyses of global patterns and drivers of these limitations across different taxonomic groups and spatial scales. Integrating 300 million occurrence records for terrestrial vertebrates and plants with comprehensive taxonomic databases, expert range maps and regional checklists, I demonstrated extensive taxonomic, geographical and temporal biases, gaps and uncertainties. I identified key socio-economic drivers of data bias across different taxonomic groups and spatial scales. The results of my dissertation provide an empirical baseline for effectively accounting for data limitations in distribution models, as well as for prioritizing and monitoring efforts to collate additional occurrence information.

The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. Continental-scale climate and microclimate layers are now widely available. However, most terrestrial organisms exist within microclimate spaces that are very small, relative to the spatial resolution of those layers. We examined the effects of multi-resolution, multi-extent topographic and climate inputs on the accuracy of hourly soil temperature predictions for a small island generated at a very high spatial resolution (2) using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continental-scale microclimate layers (within about 2–3°C of observed values) required the use of daily weather data as well as high resolution topographic layers (elevation, slope, aspect, horizon angles), while inclusion of site-specific soil properties did not markedly improve predictions. Our results suggest that large-extent microclimate layers may not provide accurate estimates of microclimate conditions when the spatial extent of a habitat or other area of interest is similar to or smaller than the spatial resolution of the layers themselves. Thus, effort in sourcing model inputs should be focused on obtaining high resolution terrain data, e.g., via LiDAR or photogrammetry, and local weather information rather than in situ sampling of microclimate characteristics.

Plant distributions are expected to shift in response to climate change, and range expansion dynamics will be shaped by the performance of individuals at the colonizing front. These plants will encounter new biotic communities beyond their range edges, and the net outcome of these encounters could profoundly affect colonization success. However, little is known about how biotic interactions vary across range edges and this has hindered efforts to predict changes in species distributions in response to climate change. In contrast, a rich literature documents how biotic interactions within species ranges vary according to distance to and density of conspecific individuals. Here, we test whether this framework can be extended to explain how biotic interactions differ beyond range edges, where conspecific adults are basically absent. To do so, we planted seven species of trees along a 450-km latitudinal gradient that crossed the current distributional range of five of these species and monitored foliar disease and invertebrate herbivory over 5 yr. Foliar disease and herbivory were analyzed as a function of distance to and density of conspecific and congeneric trees at several spatial scales. We found that within species ranges foliar disease was lower for seedlings that were farther from conspecific adults for Acer rubrum, Carya glabra, Quercus alba, and Robinia pseudoacacia. Beyond range edges, there was even less foliar disease for C. glabra, Q. alba, and R. pseudoacacia ( A. rubrum was not planted outside its range). Liriodendron tulipifera did not experience reduced disease within or beyond its range. In contrast, Quercus velutina displayed significant but idiosyncratic patterns in disease at varying distances from conspecifics. Patterns of distance dependent herbivory across spatial scales was generally weak and in some cases negative (i.e., seedlings farther from conspecific adults had more herbivory). Overall, we conclude that differences in biotic interactions across range edges can be thought of as a spatial extension to the concept of distance dependent biotic interactions. This framework also provides the basis for general predictions of how distance dependent biotic interactions will change across range edges in other systems. [ABSTRACT FROM AUTHOR]

Binary similarity indices are widely used in ecology, for example for detecting associations between species occurrence patterns, comparing regional and temporal species assemblages, and assessing beta diversity patterns, including spatial and temporal species loss and turnover. Such indices have widespread applications in biogeography, global change biology and biodiversity conservation., Similarity indices are commonly calculated upon binary presence/absence (or sometimes modelled suitable/unsuitable) data, which are generally incomplete and more categorical than their underlying natural patterns. Probable false absences are disregarded, amplifying the effects of data deficiencies and the scale dependence of the results., Fuzzy occurrence data, with a degree of uncertainty attributed to localities where presence or absence cannot be safely assigned, could better reflect species distributions, compensating for incomplete knowledge and methodological errors. Similarity indices would therefore also benefit from accommodating such fuzzy data directly., This study proposes fuzzy versions of the binary similarity indices most commonly used in ecology, so that they can be directly applied to continuous (fuzzy) rather than binary occurrence values, thus producing more realistic similarity assessments. Fuzzy occurrence can be obtained with several methods, some of which are also provided. The procedure is robust to data source disparities, gaps or other errors in species occurrence records, even for restricted species for which slight inaccuracies can affect substantial parts of their range., The method is implemented in a free and open-source software package, fuzzySim, which is available for the r statistical software and under implementation for the QGIS geographic information system. It is provided with sample data and an illustrated tutorial suitable for non-experienced users. [ABSTRACT FROM AUTHOR]