Biogeographic multi‐species occupancy models for large‐scale survey data

Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi‐species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local‐scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. We show how this challenge can be overcome by incorporating preexisting range information into MSOMs, yielding a “biogeographic multi‐species occupancy model” (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey and entire avian communities in forests and pastures of Colombia''s West Andes. Compared with traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species‐specific inference even for never‐observed species. Incorporating preexisting range data enables principled partial pooling of information across species in large‐scale MSOMs. Our biogeographic framework for multi‐species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false absences are modeled in an occupancy framework.     

Multi‐scale oviposition site selection in two mosquito species

1. In organisms characterised by complex life cycles, habitat selection often occurs at multiple spatial scales. For instance, female mosquitoes searching for an appropriate aquatic habitat to oviposit their eggs should also consider the characteristics of the terrestrial landscape in which it is embedded. 2. In this study, a field experiment was conducted to test for multi‐scale oviposition site selection in two mosquito species. Artificial pools were placed in two adjacent landscapes, olive plantations and a citrus orchard, mainly differing in their blooming periods and nectar availability. Pools were organised in three pairs: predatory caged fish were present in both pools, in one pool, or in none. 3. Early during the season, most of the egg rafts were laid by Culiseta longiareolata females in pools located within the blooming citrus orchard. When blooming shifted to the olive plantation, C. longiareolata become opportunistic. Culex pipiens females appeared later on during the season, when egg rafts of C. longiareolata were scarce, and they exhibited a higher selectivity to the olive plantation, although its blooming ended. In addition, the selectivity of C. pipiens to fish‐free pools was stronger than that of C. longiareolata. 4. Culex pipiens was more selective, possibly due to its high dispersal ability, which can lower movement cost and enhance the ability to gather environmental information. A trade‐off among gonotrophic cycles, combined with a shorter breeding season and limited recognition ability of the predatory fish may have reduced C. longiareolata selectivity. These differential oviposition patterns can strongly affect the population and community dynamics of both species.     

Land use drivers of species re‐expansion: inferring colonization dynamics in Eurasian otters

Aim Land use intensity has been recognized as one of the major determinants of native species declines. The re‐expansion of species previously constrained by habitat degradation has been rarely investigated. Here, we use site occupancy models incorporating imperfect detection to identify the land use drivers of the re‐expansion of the Eurasian otter (Lutra lutra). Location Czech Republic. Methods We applied multi‐season occupancy models to otter presence–non‐detection data collected in three national surveys (1992, 2000, 2006) at 552 sites (11.2 × 12 km grid cells). Model parameters included site occupancy, colonization and extinction probabilities, and detection probability at a sub‐site level. We modelled changes in occupancy over time as a function of agricultural, urban and industrial land use and change in the extent of agricultural land use. Results Under the best fitting model, occupancy was estimated to be 34.6% in 1992, 51.3% in 2000 and 83.7% in 2006. Detection probability was neither perfect nor constant. Occupancy probability in 1992 was negatively related to land use gradients. Colonization was more likely to occur where a reduction in agricultural land was larger. Variation in extinction and colonization rates along land use gradients resulted in increased occupancy in industrial and especially urban landscapes. Conversely, occupancy remained almost unchanged along agricultural gradients. Main conclusions Dynamics of otter expansion were strongly associated with the two main patterns of the rapid environmental transition that has taken place in the Czech Republic since the early 1990s. Results show that a reduction in intensive agricultural land use led to an increase in otter distribution, providing evidence of the impact of agricultural land use on stream ecosystems. Moreover, otters recolonized urban and industrial landscapes, probably as a result of extensive reduction in water pollution from point sources. Our results suggest that active conservation of otter populations should focus on restoration of freshwater habitat at large scales, especially in agricultural landscapes.     

Meta‐replication,sampling bias,and multi‐scale model selection: A case study on snow leopard (Panthera uncia) in western China

Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining species distribution and their influences on ecological responses. This study explores multi‐scale habitat relationships of the snow leopard (Panthera uncia) in two study areas on the Qinghai–Tibetan Plateau of western China. Our primary objectives were to evaluate the degree to which snow leopard habitat relationships, expressed by predictors, scales of response, and magnitude of effects, were consistent across study areas or locally landcape‐specific. We coupled univariate scale optimization and the maximum entropy algorithm to produce multivariate SDMs, inferring the relative suitability for the species by ensembling top performing models. We optimized the SDMs based on average omission rate across the top models and ensembles’ overlap with a simulated reference model. Comparison of SDMs in the two study areas highlighted landscape‐specific responses to limiting factors. These were dependent on the effects of the hydrological network, anthropogenic features, topographic complexity, and the heterogeneity of the landcover patch mosaic. Overall, even accounting for specific local differences, we found general landscape attributes associated with snow leopard ecological requirements, consisting of a positive association with uplands and ridges, aggregated low‐contrast landscapes, and large extents of grassy and herbaceous vegetation. As a means to evaluate the performance of two bias correction methods, we explored their effects on three datasets showing a range of bias intensities. The performance of corrections depends on the bias intensity; however, density kernels offered a reliable correction strategy under all circumstances. This study reveals the multi‐scale response of snow leopards to environmental attributes and confirms the role of meta‐replicated study designs for the identification of spatially varying limiting factors. Furthermore, this study makes important contributions to the ongoing discussion about the best approaches for sampling bias correction.     

Species‐specific and temporal scale‐dependent responses of birds to drought

Global climate change is increasing the frequency and intensity of weather extremes, including severe droughts in many regions. Drought can impact organisms by inhibiting reproduction, reducing survival and abundance, and forcing range shifts. For birds, considering temporal scale by averaging drought‐related variables over different time lengths (i.e., temporal grains) captures different hydrologic attributes which may uniquely influence food supplies, vegetation greenness/structure, and other factors affecting populations. However, studies examining drought impacts on birds often assess a single temporal grain without considering that different species have different life histories that likely determine the temporal grain of their drought response. Furthermore, while drought is known to influence bird abundance and drive between‐year range shifts, less understood is whether it causes within‐range changes in species distributions. Our objectives were to (a) determine which temporal grain of drought (if any) is most related to bird presence/absence and whether this response is species specific; and (b) assess whether drought alters bird distributions by quantifying probability of local colonization and extinction as a function of drought intensity. We used North American Breeding Bird Survey data collected over 16 years, generalized linear mixed models, and dynamic occupancy models to meet these objectives. Different bird species responded to drought at different temporal grains, with most showing the strongest signal at annual or near‐annual grains. For all drought‐responsive species, increased drought intensity at any temporal grain always correlated with decreased occupancy. Additionally, colonization/extinction analyses indicated that one species, the dickcissel (Spiza americana), is more likely to colonize novel areas within the southern/core portion of its range during drought. Considering drought at different temporal grains, along with hydrologic attributes captured by each grain, may better reveal mechanisms behind drought impacts on birds and other organisms, and therefore improve understanding of how global climate change impacts species and the landscapes they inhabit.     

Toward community predictions: Multi‐scale modelling of mountain breeding birds' habitat suitability,landscape preferences,and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence‐only points) of bird species to find suitable modelling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi‐scale method of modelling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including “Bio11” (Mean Temperature of Coldest Quarter), and “Bio 4” (Temperature Seasonality), then in the focal variables including “Forest”, “Orchard”, and “Agriculture area” as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment.     

A multi‐scale analysis of fragmentation effects on remnant plant species richness in Phoenix,Arizona

Aim Understanding complex ecological phenomena, such as the determinants of species richness, is best achieved by investigating their properties at different spatial scales. Factors significantly affecting the number of species occurring at one scale may not impact on richness at other scales. While this scale dependence has become increasingly recognized, there still remains a need to elucidate exactly how richness is structured across scales, and which mechanisms are influential for determining this important community property. This study explores how woody plant species richness varies in a fragmented system at multiple scales, and which factors are primarily responsible for these patterns. Location The study area is located in the Sonoran Desert within the bounds of metropolitan Phoenix, Arizona, which is the locus of the Central Arizona–Phoenix Long‐Term Ecological Research (CAP‐LTER) site. Methods Estimates of local and fragment plant species richness were generated from field data collected from 22 sites. Independent variables describing fragment sites were also calculated, including area, habitat heterogeneity, density of individuals, mean elevation, and extent of isolation. Structural equation modelling, multiple regression, and analysis of covariance were used to assess the contribution of independent variables to richness at the fragment and local scales. Results Fragment species richness was significantly influenced by area, though not isolation, habitat heterogeneity, mean elevation, or density of individuals. Local richness was not significantly related to fragment area, but was positively related to fragment richness, plant density, and elevation. Main conclusions The fragment species–area effect resulted from larger remnants supporting higher numbers of individuals at comparable densities, increasing richness through either passive sampling of progressively less common species and/or lower extinction rates among larger populations. Without using multi‐temporal data it is not possible to disentangle these mechanisms. We found that patterns evident at one scale are not necessarily apparent at other scales, as elevation and density of individuals significantly affected richness at the local scale but not at the fragment scale. These results lend support to the concept that mechanisms influencing the species richness of natural communities may be operable only within certain domains and that relevant scales should be specified.     

Discrepancies in occupancy and abundance approaches to identifying and protecting habitat for an at‐risk species

Predicting how environmental factors affect the distribution of species is a fundamental goal of conservation biology. Conservation biologists rely on species distribution and abundance models to identify key habitat characteristics for species. Occupancy modeling is frequently promoted as a practical alternative to use of abundance in identifying habitat quality. While occupancy and abundance are potentially governed by different limiting factors operating at different scales, few studies have directly compared predictive models for these approaches in the same system. We evaluated how much occupancy and abundance are driven by the same environmental factors for a species of conservation concern, the greater short‐horned lizard (Phrynosoma hernandesi). Occupancy was most strongly dictated by precipitation, temperature, and density of ant mounds. While these factors were also in the best‐supported predictive models for lizard abundance, the magnitude of the effects varied, with the sign of the effect changing for temperature and precipitation. These discrepancies show that while occupancy modeling can be an efficient approach for conservation planning, predictors of occupancy probability should not automatically be equated with predictors of population abundance. Understanding the differences in factors that control occupancy versus abundance can help us to identify habitat requirements and mitigate the loss of threatened species.     

Ensemble modeling to predict habitat suitability for a large‐scale disturbance specialist

To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black‐backed Woodpeckers (Picoides arcticus; a burned‐forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D² models) and various combinations of four environmental variables describing burn severity, the north–south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled “ensemble predictions,” consisting of the number of models (0–8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., “no‐analogue”) environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black‐backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide managers attempting to balance salvage logging with habitat conservation in burned‐forest landscapes where black‐backed woodpecker nest location data are not immediately available. Ensemble modeling represents a promising tool for guiding conservation of large‐scale disturbance specialists.     

Habitat characteristics and species interference influence space use and nest‐site occupancy: implications for social variation in two sister species

Nest‐site selection is an important component of species socio‐ecology, being a crucial factor in establishment of group living. Consequently, nest‐site characteristics together with space‐use proxies may reveal the social organization of species, which is critical when direct observation of social interactions is hindered in nature. Importantly, nest‐site choice is expected to be under strong selective pressures and the object of intra‐ and interspecific competition. Although the bulk of research on sociality focuses on its ecological drivers, our study introduces interspecific competition as a potential factor that could influence social evolution. We investigated the influence of habitat and interspecific competition on the social organization of two sister species of the African four striped mouse, Rhabdomys dilectus dilectus and Rhabdomys bechuanae, in a similar macroenvironment. These species diverged in allopatry and occupy distinct environmental niches. We radiotracked 140 adults to identify their nest‐sites, determine nest characteristics and record groups that shared nest‐sites. Group cohesion was estimated from nest‐site fidelity, group association strength, and home range overlap within versus between group members. We compared the two species in sympatry versus parapatry to determine the impact of species interference on sociality. In parapatry, the two species selected distinct nest‐site types, interpreted as different anti‐predator strategies: R. bechuanae selected fewer, spaced, less concealed nest‐sites whereas R. d. dilectus selected clumped and less visible nest‐sites. Rhabdomys bechuanae also showed more cohesive and stable social groups than R. d. dilectus. In sympatry, compared to R. bechuanae, R. d. dilectus occupied similar nest‐sites, however slightly more exposed and clumped, and displayed similar nest‐site fidelity and group association strength. We conclude that although habitat selection may be an important driver of social divergence in Rhabdomys, species interference, by limiting R. d. dilectus movements and forcing nest‐site sharing may induce new ecological pressures that could influence its social evolution.     

Evaluating model transferability for a threatened species to adjacent areas: Implications for rock‐wallaby conservation

When modelling the distribution of a species, it is often not possible to comprehensively sample the whole distribution of the species and managers may have habitat models based on data from one area that they want to apply in other areas. Hence, an important question is: how accurate are models of the distributions of species when applied beyond the areas where they were developed? A first step in measuring model transferability could be testing models in adjacent areas. We predicted the habitat associations of the brush‐tailed rock‐wallaby (Petrogale penicillata) across two spatial scales in two neighbouring study areas in eastern Australia, south‐east Queensland and north‐east New South Wales. We used classification trees for exploratory data analysis of habitat relationships and then applied logistic regression models to predict species occurrence. We assessed the within‐area discriminative ability of the habitat models using cross‐validation and threshold plots, and tested the predictive ability of the models for adjacent areas using the receiver operating characteristic statistic to determine the area under the curve. We found that models performed well within an area and extrapolating them to adjacent areas resulted in good predictive performance at the site scale but substantially poorer predictive performance at the landscape scale. We conclude that distribution models for wildlife species should only be extrapolated to neighbouring areas with caution when using landscape‐scale environmental variables. Alternatively, only key habitat associations predicted by the models at this scale should be transferred across adjacent areas once verified against local knowledge of the ecology of the study species.     

PRIMO/PRIMONA: A coarse‐grained model for proteins and nucleic acids that preserves near‐atomistic accuracy

The new coarse graining model PRIMO/PRIMONA for proteins and nucleic acids is proposed. This model combines one to several heavy atoms into coarse‐grained sites that are chosen to allow an analytical, high‐resolution reconstruction of all‐atom models based on molecular bonding geometry constraints. The accuracy of proposed reconstruction method in terms of structure and energetics is tested and compared with other popular reconstruction methods for a variety of protein and nucleic acid test sets. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.