Modeling Trends from North American Breeding Bird Survey Data: A Spatially Explicit Approach

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.     

Decomposing the heterogeneity of species distributions into multiple scales: a hierarchical framework for large‐scale count surveys

We introduce a novel spatially explicit framework for decomposing species distributions into multiple scales from count data. These kinds of data are usually positively skewed, have non‐normal distributions and are spatially autocorrelated. To analyse such data, we propose a hierarchical model that takes into account the observation process and explicitly deals with spatial autocorrelation. The latent variable is the product of a positive trend representing the non‐constant mean of the species distribution and of a stationary positive spatial field representing the variance of the spatial density of the species distribution. Then, the different scales of emergent structures of the distribution of the population in space are modelled from the latent density of the species distribution using multi‐scale variogram models. Multi‐scale kriging is used to map the spatial patterns previously identified by the multi‐scale models. We show how our framework yields robust and precise estimates of the relevant scales both for spatial count data simulated from well‐defined models, and in a real case‐study based on seabird count data (the common guillemot Uria aalge) provided by large‐scale aerial surveys of the Bay of Biscay (France) performed over a winter. Our stochastic simulation study provides guidelines on the expected uncertainties of the scales estimates. Our results indicate that the spatial structure of the common guillemot can be modelled as a three‐level hierarchical system composed of a very broad‐scale pattern (～ 200 km) with a stable location over time that might be environmentally controlled, a broad‐scale pattern (～ 50 km) with a variable shape and location, that might be related to shifts in prey distribution, and a fine‐scale pattern (～ 10 km) with a rather stable shape and location, that might be controlled by behavioural processes. Our framework enables the development of robust, scale‐dependent hypotheses regarding the potential ecological processes that control species distributions.     

Spatiotemporal variation in mechanisms driving regional‐scale population dynamics of a Threatened grassland bird

To achieve national population targets for migratory birds, landscape‐level conservation approaches are increasingly encouraged. However, knowledge of the mechanisms that drive spatiotemporal patterns in population dynamics are needed to inform scale‐variant policy development. Using hierarchical Bayesian models and variable selection, we determined by which mechanism(s), and to what extent, changes in quantity and quality of surrogate grassland habitats contributed to regional variation in population trends of an obligatory grassland bird, Bobolink (Dolichonyx oryzivorous). We used North American Breeding Bird Survey data to develop spatially explicit models of regional population trends over 25 years across 35 agricultural census divisions in Ontario, Canada. We measured the strength of evidence for effects of land‐use change on population trends over the entire study period and over five subperiods. Over the entire study period, one region (Perth) displayed strong evidence of population decline (95% CI is entirely below 0); four regions displayed strong evidence of population increase (Bruce, Simcoe, Peterborough, and Northumberland). Population trends shifted spatially among subperiods, with more extreme declines later in time (1986–1990: 28% of 35 census divisions, 1991–1995: 46%, 1996–2000: 40%, 2001–2005: 66%, 2006–2010: 82%). Important predictors of spatial patterns in Bobolink population trends over the entire study period were human development and fragmentation. However, factors inferred to drive patterns in population trends were not consistent over space and time. This result underscores that effective threat identification (both spatially and temporally) and implementation of flexible, regionally tailored policies will be critical to realize efficient conservation of Bobolink and similar at‐risk species.     

Spatial autocorrelation as a tool for identifying the geographical patterns of aphid annual abundance

Abstract 1 A spatial autocorrelation analysis was undertaken to investigate the spatial structure of annual abundance for the pest aphid Myzus persicae collected in suction traps distributed across north‐west Europe. 2 The analysis was applied at two different scales. The Moran index was used to estimate the degree of spatial autocorrelation at all sites within the study area (global level). The contributions of each site to the global index were identified by the use of a local indicator of spatial autocorrelation (LISA). A hierarchical cluster analysis was undertaken to highlight differences between groups of resulting correlograms. 3 Similarity between traps was shown to occur over large geographical distances, suggesting an impact of phenomena such as climatic gradients or land use types. 4 The presence of outliers and zones of similarity (hot‐spots) and of dissimilarity (cold‐spots) were identified indicating a strong impact of local effects. 5 Several groups of traps characterized by similarities in their local spatial structure (correlograms, value of Moran's I_i) also had similar values for land use variables (the area occupied by agricultural zones, forest and sea). 6 It is concluded that trap data can provide information about Myzus persicae that is representative of large geographical areas. Thus, trap data can be used to estimate the aerial abundance of this species, even if the suction traps are not regularly and densely distributed.     

Incorporating spatial autocorrelation in rarefaction methods: Implications for ecologists and conservation biologists

Recently, methods for constructing Spatially Explicit Rarefaction (SER) curves have been introduced in the scientific literature to describe the relation between the recorded species richness and sampling effort and taking into account for the spatial autocorrelation in the data. Despite these methodological advances, the use of SERs has not become routine and ecologists continue to use rarefaction methods that are not spatially explicit. Using two study cases from Italian vegetation surveys, we demonstrate that classic rarefaction methods that do not account for spatial structure can produce inaccurate results. Furthermore, our goal in this paper is to demonstrate how SERs can overcome the problem of spatial autocorrelation in the analysis of plant or animal communities. Our analyses demonstrate that using a spatially-explicit method for constructing rarefaction curves can substantially alter estimates of relative species richness. For both analyzed data sets, we found that the rank ordering of standardized species richness estimates was reversed between the two methods. We strongly advise the use of Spatially Explicit Rarefaction methods when analyzing biodiversity: the inclusion of spatial autocorrelation into rarefaction analyses can substantially alter conclusions and change the way we might prioritize or manage nature reserves.     

Long-term primary succession: a comparison of non-spatial and spatially explicit inferential techniques

Trajectories of plant primary succession are commonly inferred from temporal changes in non-spatially explicit metrics that characterise the whole sampling area with a single statistic (e.g. community diversity). However, the derivation of these metrics is affected by the presence of spatial structure (patchiness) in vegetation. The emergence of spatial patchiness during succession is therefore likely to have an impact on attempts to infer the rate and direction of vegetation development. This study examines the impact of patchiness on inferred developmental trajectories by comparing a non-spatial analysis of long-term primary succession with a spatially explicit analysis of the same data. The data used in the analysis were collected from an 850-year-old chronosequence of 7 lava flows in southern Iceland. The non-spatial analysis captured broad developmental trends, including an overall increase in community diversity with time, and a split between early pioneer communities (sites <150-year-old) dominated by cryptogams and later assemblages (sites older than ≈150 years) where vascular plants were more important. However, the non-spatial analysis missed key community processes apparent in the spatially explicit analysis, including divergence in vegetation development related to metre-scale topographic differences. The results of this study emphasise the need for spatially explicit, multi-scale studies of vegetation development, both in the inference of past vegetation dynamics, and in modelling the response of spatially patchy vegetation to future environmental change.     

Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.     

Joint Effects of Habitat Heterogeneity and Species’ Life-History Traits on Population Dynamics in Spatially Structured Landscapes

Both habitat heterogeneity and species’ life-history traits play important roles in driving population dynamics, yet there is little scientific consensus around the combined effect of these two factors on populations in complex landscapes. Using a spatially explicit agent-based model, we explored how interactions between habitat spatial structure (defined here as the scale of spatial autocorrelation in habitat quality) and species life-history strategies (defined here by species environmental tolerance and movement capacity) affect population dynamics in spatially heterogeneous landscapes. We compared the responses of four hypothetical species with different life-history traits to four landscape scenarios differing in the scale of spatial autocorrelation in habitat quality. The results showed that the population size of all hypothetical species exhibited a substantial increase as the scale of spatial autocorrelation in habitat quality increased, yet the pattern of population increase was shaped by species’ movement capacity. The increasing scale of spatial autocorrelation in habitat quality promoted the resource share of individuals, but had little effect on the mean mortality rate of individuals. Species’ movement capacity also determined the proportion of individuals in high-quality cells as well as the proportion of individuals experiencing competition in response to increased spatial autocorrelation in habitat quality. Positive correlations between the resource share of individuals and the proportion of individuals experiencing competition indicate that large-scale spatial autocorrelation in habitat quality may mask the density-dependent effect on populations through increasing the resource share of individuals, especially for species with low mobility. These findings suggest that low-mobility species may be more sensitive to habitat spatial heterogeneity in spatially structured landscapes. In addition, localized movement in combination with spatial autocorrelation may increase the population size, despite increased density effects.     

Analysis of Large-Scale Spatial Heterogeneity in Vegetation Indices among North American Landscapes

We analyzed the spatial heterogeneity in vegetation indices among 13 North American landscapes by using full Landsat Thematic Mapper images. Landscapes varied broadly in the statistical distribution of vegetation indices, but were successfully ordinated by using a measure of central tendency (the mean) and a measure of dispersion (the standard deviation or the coefficient of variation). Differences in heterogeneity among landscapes were explained by their topographic relief and their land cover. Landscape heterogeneity (standard deviation of the Normalized Difference Vegetation Index, NDVI) tended to increase linearly with topographic relief (standard deviation of elevation), but landscapes with low relief were much more heterogeneous than expected from this relationship. The latter were characterized by a large proportion of agricultural land. Percent agriculture, in turn, was inversely related to topographic relief. The strength of these relationships was evaluated against changes in image spatial resolution (grain size). Aggregation of NDVI images to coarser grain size resulted in steady decline of their standard deviation. Although the relationship between landscape heterogeneity and explanatory variables was generally preserved, rates of decrease in heterogeneity with grain size differed among landscapes. A spatial autocorrelation analysis showed that rates of decrease were related to the scale at which pattern is manifested. On one end of the spectrum are agricultural, low-relief landscapes with low spatial autocorrelation and small-scale heterogeneity associated with fields; their heterogeneity decreased sharply as grain size increased. At the other end, desert landscapes were characterized by low small-scale heterogeneity, high spatial autocorrelation, and almost no change in heterogeneity as grain sized was increased—their heterogeneity, associated with land forms, was present at a large scale. Received 1 October 1997; accepted 11 February 1998.     

On the Effects of Scale for Ecosystem Services Mapping

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.     

Zones of influence for soil organic matter dynamics: A conceptual framework for data and models

Soil organic matter (SOM) is an indicator of sustainable land management as stated in the global indicator framework of the United Nations Sustainable Development Goals (SDG Indicator 15.3.1). Improved forecasting of future changes in SOM is needed to support the development of more sustainable land management under a changing climate. Current models fail to reproduce historical trends in SOM both within and during transition between ecosystems. More realistic spatio‐temporal SOM dynamics require inclusion of the recent paradigm shift from SOM recalcitrance as an ‘intrinsic property’ to SOM persistence as an ‘ecosystem interaction’. We present a soil profile, or pedon‐explicit, ecosystem‐scale framework for data and models of SOM distribution and dynamics which can better represent land use transitions. Ecosystem‐scale drivers are integrated with pedon‐scale processes in two zones of influence. In the upper vegetation zone, SOM is affected primarily by plant inputs (above‐ and belowground), climate, microbial activity and physical aggregation and is prone to destabilization. In the lower mineral matrix zone, SOM inputs from the vegetation zone are controlled primarily by mineral phase and chemical interactions, resulting in more favourable conditions for SOM persistence. Vegetation zone boundary conditions vary spatially at landscape scales (vegetation cover) and temporally at decadal scales (climate). Mineral matrix zone boundary conditions vary spatially at landscape scales (geology, topography) but change only slowly. The thicknesses of the two zones and their transport connectivity are dynamic and affected by plant cover, land use practices, climate and feedbacks from current SOM stock in each layer. Using this framework, we identify several areas where greater knowledge is needed to advance the emerging paradigm of SOM dynamics—improved representation of plant‐derived carbon inputs, contributions of soil biota to SOM storage and effect of dynamic soil structure on SOM storage—and how this can be combined with robust and efficient soil monitoring.     

Scale-related patterns in the spatial and environmental components of stream macroinvertebrate assemblage variation

Aim We examined the relative contributions of spatial gradients and local environmental conditions to macroinvertebrate assemblages of boreal headwater streams at three hierarchical extents: bioregion, ecoregion and drainage system. We also aimed to identify the environmental variables most strongly related to assemblage structure at each study scale, and to assess how the importance of these variables is related to regional context and spatial structuring at different scales. Location Northern Finland ( 62 – 68° N, 25–32° E). Methods Variation in macroinvertebrate data was partitioned using partial canonical correspondence analysis into components explained by spatial variables (nine terms from the cubic trend surface regression), local environmental variables (15 variables) and spatially structured environmental variation. Results The strength of the relationship between assemblage structure and local environmental variables increased with decreasing spatial extent, whereas assemblage variation related to spatial variables and spatially structured environmental variation showed the opposite pattern. At the largest extents, spatial variation was related to latitudinal gradients, whereas spatial autocorrelation among neighbouring streams was the likely mechanism creating spatial structure within drainage systems. Only stream size and water acidity were consistently important in explaining assemblage structure at all study scales, while the importance of other environmental variables was more context‐dependent. Main conclusions The importance of local environmental factors in explaining macroinvertebrate assemblage structure increases with decreasing spatial extent. This scale‐related pattern is not caused solely by changes in study extent, however, but also by variable sample sizes at different regional extents. The importance of environmental gradients is context‐dependent and few factors are likely to be universally important correlates of macroinvertebrate assemblage structure. Finally, our results suggest that bioassessment should give due attention to spatial structuring of stream assemblages, because important assemblage gradients may not only be related to local factors but also to biogeographical constraints and neighbourhood dispersal processes.     

Ecosystem service state and trends at the regional to national level: A rapid assessment

The concept of ecosystem services has helped rationalize humanity's dependence on and benefits from nature, pushing the paradigm of environmental sustainability from a charity in the direction of a necessity. However, globally many ecosystem services are declining despite their eminent value for society. A prime cause of this decline is allocated to land use change. While the body of empirical research showing various consequences of land use is growing, and the ecosystem service concept has helped make trade-offs more graspable, a lucid approach that neatly summarizes the extent of land use trade-offs is still lacking.In this paper, we introduce a rapid assessment to analyze both the state and trends of selected ecosystem services associated with given land use categories. Theoretically, the assessment can be performed for any given spatial unit, but the regional to national level appears to be the most appropriate spatial resolution. Each land use-ecosystem service relationship is classified from a strong disservice to a strong service. The results are displayed in adapted flower diagrams, which legibly display information on the ecosystem services in each land use, thus clearly summarizing trade-offs associated with changing land use.We illustrate this rapid ecosystem service assessment method by applying it to three land use categories on the spatial extent of Switzerland. We found that the simple but systematic approach is more flexible than traditional mapping approaches, i.e. it allowed us to combine a variety of spatially non-explicit but highly detailed indicators with spatially explicit indicators. Also, we were able to proceed faster than with a mapping approach, where many known and unknown spatial inaccuracies may arise have allowed. This flexible incorporation of spatially explicit and non-explicit data provides high quality information on the state and trends of ecosystem services at regional to national extents. For that reason, we are convinced that the rapid assessment method has the potential to advance knowledge of ecosystem services and land use trade-offs, especially in areas with low data availability and monitoring activity.     

A Bayesian hierarchical trend analysis finds strong evidence for large-scale temporal declines in stream ecological condition around Melbourne, Australia

Knowledge of temporal change in ecological condition is important for the understanding and management of ecosystems. However, analyses of trends in biological condition have been rare, as there are usually too few data points at any single site to use many trend analysis techniques. We used a Bayesian hierarchical model to analyse temporal trends in stream ecological condition (as measured by the invertebrate-based index SIGNAL) across Melbourne, Australia. The Bayesian hierarchical approach assumes dependency amongst the sampling sites. Results for each site "borrow strength" from the other data because model parameter values are assumed to be drawn from a larger common distribution. This leads to robust inference despite the few data that exist at each site. Utilising the flexibility of the Bayesian approach, we also modelled change over time as a function of catchment urbanisation, allowed for potential temporal and spatial autocorrelation of the data and trend estimates, and used prior information to improve the estimate of data uncertainty. We found strong evidence of a widespread decline in SIGNAL scores for edge habitats (areas of little or no flow). The rate of decline was positively associated with catchment urbanisation. There was no evidence of such declines for riffle habitats (areas with rapid and turbulent flow). Melbourne has experienced a decline in rainfall, indicative of either drought and/or longer-term climate change. The results are consistent with the expected coupled effects of these rainfall changes and increasing urbanisation, but more research is needed to isolate a causal mechanism. More immediately, however, the Bayesian hierarchical approach has allowed us to identify a pattern in a biological monitoring data set that might otherwise have gone un-noticed, and to demonstrate a large-scale temporal decline in biological condition.     

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent.     

Heteroskedasticity as a leading indicator of desertification in spatially explicit data

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale‐free patterning. These models simulate shifts from extensive vegetative cover to bare, desert‐like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data.     

The influence of space in genetic-environmental relationships when environmental heterogeneity and seed dispersal occur at similar scale

We tested the importance of microenvironmental topographic parameters as predictors of emmer wheat genetic variation using three classes of single-locus (or at most several-loci) genetic markers (allozymes, glutenins, and qualitative traits) and two classes of markers of polygenic inheritance (phenological and morphological traits). Canonical correspondence analysis (CCA) and redundancy analysis (RDA) detected a significant effect of spatially structured environmental variation on genetic differences between plants for allozymes, glutenins, and quantitative morphological and phenological traits. However, after removing a spatial component of variation in partial CCA and partial RDA, the relationship of the remaining environmental variation with these genetic markers could be explained by chance alone, allowing us to rule out microniche topographic specialization in emmer wheat. Topographic autocorrelation exhibited a certain degree of similarity with genetic marker autocorrelation, indicating similar scales of environmental heterogeneity and seed flow. The detected population genetic structure agrees with one expected under isolation by distance as a result of limited gene flow. A negative relationship of genetic similarity with the logarithm of distance between plants was detected for both molecular markers and quantitative traits, which differed in the strength but not the pattern of association.     

The trouble with isolation by distance

The genetic population structure of many species is characterised by a pattern of isolation by distance (IBD): due to limited dispersal, individuals that are geographically close tend to be genetically more similar than individuals that are far apart. Despite the ubiquity of IBD in nature, many commonly used statistical tests are based on a null model that is completely non-spatial, the Island model. Here, I argue that patterns of spatial autocorrelation deriving from IBD present a problem for such tests as it can severely bias their outcome. I use simulated data to illustrate this problem for two widely used types of tests: tests of hierarchical population structure and the detection of loci under selection. My results show that for both types of tests the presence of IBD can indeed lead to a large number of false positives. I therefore argue that all analyses in a study should take the spatial dependence in the data into account, unless it can be shown that there is no spatial autocorrelation in the allele frequency distribution that is under investigation. Thus, it is urgent to develop additional statistical approaches that are based on a spatially explicit null model instead of the non-spatial Island model.     

Biodiversity change is scale‐dependent: an example from Dutch and UK hoverflies (Diptera,Syrphidae)

We test whether temporal change in species richness (ΔS [%]) is scale‐dependent, using data on hoverflies from the UK and the Netherlands. We analysed ΔS between pre‐1980 and post‐1980 periods using 5 grid resolutions (10×10, 20×20, 40×40, 80×80 and 160×160 km). We also tested the effect of data quality and of unequal survey periods on ΔS estimates, and checked for spatial autocorrelation of ΔS estimates. Using data from equal survey periods, we found significant increases in hoverfly species richness in the Netherlands at fine scales, but no significant change at coarser scales indicating a decrease in beta diversity. In the UK, ΔS was negative at fine scale, near zero at intermediate scales, and positive at coarse scales, indicating that the degree of spatial beta diversity increased between the time periods. The use of unequal survey periods (using longer periods in the past to compensate for lower survey intensity) tended to inflate past species richness, biasing ΔS estimates downwards. High data quality thresholds sometimes obscured dynamics by reducing sample size, but never reversed trends. There was little spatial autocorrelation of ΔS, implying that local drivers (land use change or environmental noise) are important in dynamics of hoverfly diversity. A second, sample agglomeration approach to measure scaling resulted in greater noise in ΔS, obscuring the NL pattern, while still showing strong evidence of fine‐scale richness loss in the UK. Our results indicate that explicit considerations of spatial (and temporal) scale are essential in studies documenting past biodiversity change, or projecting change into the future.     

Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

Aim We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Location Nova Scotia, eastern Canada. Methods Species distribution data were taken from a recent (1999–2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land‐cover variables at a 10‐km resolution. We then accounted for spatial autocorrelation using an autocovariate or third‐order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. Results The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend‐surface variables. Main conclusions The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land‐cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land‐cover simulations in climate‐induced range‐shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range‐shift projections is merited.