Ringed seal post-moulting movement tactics and habitat selection

Intra-specific and intra-population variation in movement tactics have been observed in many species, sometimes in association with alternative foraging techniques or large-scale habitat selection. However, whether animals adjust their small-scale habitat selection according to their large-scale tactics has rarely been studied. This study identified two large-scale movement tactics in ringed seals (Phoca hispida) during their non-breeding, post-moulting period. First-passage times (FPT) were used to explore these large-scale patterns. Subsequently, habitat selection was quantified by modelling the FPTs as a function of habitat attributes using Cox proportional hazards models. Some seals moved far offshore into areas preferentially containing 40–80% ice coverage, while other individuals spread along the coasts of Svalbard concentrating their time near glacier fronts. Both tactics resulted in ringed seals being in highly productive areas where they had access to ice-platforms to rest. When offshore, habitat selection was influenced mainly by sea ice concentration and season. Late in the season (autumn), increased risk of leaving an area was identified, even when ice conditions were still favourable, reflecting their need to return to over-wintering/breeding areas before the fjords of the archipelago freeze. For ringed seals that remained inshore, habitat use intensities were influenced mainly by the distance to glacier fronts and season. These animals were already close to their over-wintering habitat and hence their risk of leaving an area decreased as winter approached. This study of ringed seals habitat selection reveals how they fulfil their biological requirements in this dynamic, heterogeneous habitat. Individuals within the same population employed two distinct large-scale movement tactics, adjusting their decisions for small-scale habitat selection accordingly. This flexibility in ringed seal spatial ecology during summer and fall is expected to result in increased population viability in this high Arctic environment. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.     

Assessing the transferability of hydraulic habitat models for juvenile Atlantic salmon

Hydraulic habitat models are logistically and technically challenging and expensive to produce. They are therefore frequently transferred between rivers, often with different environmental conditions, without validation. Although studies have recognised problems with model transfer, few have assessed the consequences for model predictions. This study investigated the local (within sub-catchment) transfer of hydraulic habitat models developed for Atlantic salmon (Salmo salar) fry. Two adjacent reaches were chosen for the study, each containing pool, riffle, glide and run habitats. Detailed topographic surveys were used to develop hydraulic models for the study reaches. Substrate and cover were characterised using transects. Seasonal fish habitat use was characterised by electrofishing. Uniform saturation stocking was employed to ensure standard starting densities across all habitats. Generalised additive models were fitted to fry abundance data. Depth, velocity, dominant substrate and cover were used as predictor variables. The distribution of depth, velocity, dominant substrate and cover differed between the two reaches, but was consistent within reaches across seasons. Substrate exhibited the greatest inter-reach difference. Velocity and depth were the most important individual predictors of fry abundance, with the highest densities observed at moderate velocities (∼0.4 m s⁻¹) and low depths (∼0.1 m) across all seasons. When models were transferred locally between reaches, those that were adjusted for inter-reach differences in mean fry abundance (i.e. those predicting changes in relative abundance) performed better than those transferred without adjustment. Complex models that included substrate and cover typically explained some of the variation in abundance in the transferred reach but performed less well than models containing hydraulic parameters alone. This potentially reflected inter-reach differences in the distribution of substrate and cover. This study suggests that (1) uniform stocking is useful for examining habitat-abundance relationships free from the influence of patchy egg deposition; (2) habitat models should be developed at sites offering maximum environmental complexity at a local level; (3) scientists and managers should avoid transferring models between locations with different environmental characteristics, especially in the absence of model validation; (4) complex models should be avoided, and those containing hydraulic variables alone should be considered, if predictions of habitat quality are to be made at new sites.     

Using regression trees to predict alpha diversity based upon geographical and habitat characteristics

Different environmental factors act as driving forces of diversity at different scales of analysis; and also the effect of one environmental factor changes as the scale of analysis changes. Most studies rely on multiple regression models, and such models tend to mix-up the effect of all factors and assume that factors effects are additive. We believe that the effect of environment on diversity should be characterized by a hierarchical structure with coarse scale factors, like geographical tropics to poles gradients, defining the envelope of possible diversity conditions, and other more local factors, like habitat structure, being responsible for the fine tuning of diversity. This structure is most efficiently modeled with regression trees. We show that for six habitat types in Greek protected areas regression tree models were able to describe plant species richness based upon environmental factors considerably more efficiently than multiple regression models. More importantly when the models were extrapolated to other sites in Greece, outside their domain, the differences between the predictive ability of the two approaches was magnified. The tree models picked up important ecological characteristics, and a hierarchical structure that used coarse scale factors, like latitude and longitude, for the coarse scale estimate of alpha diversity, and finer scale factors like fragmentation, for the fine-tuning of the estimation. Therefore, we advocate that the regression tree methodology is most appropriate for modeling the relationship between diversity and environmental factors, and the use of the classical regression approaches might be misleading.     

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.     

The habitat quality index applied to New Mexico streams

The accuracy of two trout biomass (standing stock) prediction models, developed for Wyoming streams by Binns & Eiserman (1979), was evaluated for New Mexico streams inhabited by brown trout, Salmo trutta L. and rainbow trout, Oncorhynchus mykiss Walbaum. Thirty-two representative sites in 15 different streams were sampled under summer low-flow conditions in 1988 and 1989. The 11 phyiscal, chemical, and biological variables used in original models were used as independent variables for simple and multiple regression analysis designed to predict total trout biomass. Model I of Binns and Eiserman proved to be of limited utility; it explained 53% of the variation in total trout biomass at each of the New Mexico sites (kg ha⁻¹ = 8.916 + 0.830/Model U). Only 9.5% of the biomass variations was explained by Model II. Statistical analysis showed that trout biomass was significantly correlated with nitrate-nitrogen concentration and macroinvertebrate diversity in Model I. Because both variates are time consuming to estimate, Model I may not be any more cost-effective than sampling trout directly. The low predictive power of Model II probably indicates that it is limited to the geographical area of field measurement origin.     

On the validity of time‐dependent AUC estimation in the presence of cure fraction

During the last decades, several approaches have been proposed to estimate the time‐dependent area under the receiver operating characteristic curve (AUC) of risk tools derived from survival data. The validity of these estimators relies on some regularity assumptions among which a survival function being proper. In practice, this assumption is not always satisfied because a fraction of the population may not be susceptible to experience the event of interest even for long follow‐up. Studying the sensitivity of the proposed estimators to the violation of this assumption is of substantial interest. In this paper, we investigate the performance of a nonparametric simple estimator, developed for classical survival data, in the case when the population exhibits a cure fraction. Motivated from the current practice of deriving risk tools in oncology and cardiovascular disease prevention, we also assess the loss, in terms of predictive performance, when deriving risk tools from survival models that do not acknowledge the presence of cure. The simulation results show that the investigated method is valid even under the presence of cure. They also show that risk tools derived from survival models that ignore the presence of cure have smaller AUC compared to those derived from survival models that acknowledge the presence of cure. This was also attested with a real data analysis from a breast cancer study.     

Assessing changes in habitat quality due to land use changes in the spur-thighed tortoise Testudo graeca using hierarchical predictive habitat models

In this study we propose a model-building approach based on the hierarchical integration of the main environmental factors (climate, topography/lithology, and land uses) determining the distribution of the spur-thighed tortoise in south-east Spain. Data on the presence/absence of the species were primarily based on information derived from interviews to shepherds. The hierarchical modelling exercise consisted of three steps. First, we constructed a model for the entire region using climate variables, thus obtaining a potential climatical model. Second, we introduced variables referring to topography and lithology that fall within the climatic distribution range ( potential model). Third, by using this second model as a starting point, we included land use variables to obtain the actual distribution model.
We analysed the changes in the values of probability of the presence of this species for a given cell between the potential and the actual model, assessing areas where habitat quality has decreased, been maintained or increased. The spatial representation of these changes was highly coherent. A discriminant analysis linked areas where habitat quality has dropped with agriculture landscapes, whereas those areas where habitat quality has been maintained or increased were located mainly in shrublands. Twenty-five per cent (479 km²) of the potential distribution of the species became suboptimal when land use was included, which emphasizes the importance of land use changes in both the range dynamics and the conservation of the spur-thighed tortoise in south-east Spain.     

Sensitivity of marine fish thermal habitat models to fishery data sources

Statistical models built using different data sources and methods can exhibit conflicting patterns. We used the northern stock of black sea bass (Centropristis striata) as a case study to assess the impacts of using different fisheries data sources and laboratory‐derived physiological metrics in the development of thermal habitat models for marine fishes. We constructed thermal habitat models using generalized additive models (GAMs) based on various fisheries datasets as input, including the NOAA Northeast Fisheries Science Center (NEFSC) bottom trawl surveys, various inshore fisheries‐independent trawl surveys (state waters), NEFSC fisheries‐dependent observer data, and laboratory‐based physiological metrics. We compared each model''s GAM response curve and coupled them to historical ocean conditions in the U.S. Northeast Shelf using bias‐corrected ocean temperature output from a regional ocean model. Thermal habitat models based on shelf‐wide data (NEFSC fisheries‐dependent observer data and fisheries‐independent spring and fall surveys) explained the most variation in black sea bass presence/absence data at ~15% deviance explained. Models based on a narrower range of sampled thermal habitat from inshore survey data in the Northeast Area Monitoring and Assessment Program (NEAMAP) and the geographically isolated Long Island Sound data performed poorly. All models had similar lower thermal limits around 8.5℃, but thermal optima, when present, ranged from 16.7 to 24.8℃. The GAMs could reliably predict habitat from years excluded from model training, but due to strong seasonal temperature fluctuations in the region, could not be used to predict habitat in seasons excluded from training. We conclude that survey data source can greatly impact development and interpretation of thermal habitat models for marine fishes. We suggest that model development be based on data sources that sample the widest range of ocean temperature and physical habitat throughout multiple seasons when possible, and encourage thorough consideration of how data gaps may influence model uncertainty.     

Tree-augmented Cox proportional hazards models

We study a hybrid model that combines Cox proportional hazards regression with tree-structured modeling. The main idea is to use step functions, provided by a tree structure, to 'augment' Cox (1972) proportional hazards models. The proposed model not only provides a natural assessment of the adequacy of the Cox proportional hazards model but also improves its model fitting without loss of interpretability. Both simulations and an empirical example are provided to illustrate the use of the proposed method.     

Global patterns of fragmentation and connectivity of mammalian carnivore habitat

Although mammalian carnivores are vulnerable to habitat fragmentation and require landscape connectivity, their global patterns of fragmentation and connectivity have not been examined. We use recently developed high-resolution habitat suitability models to conduct comparative analyses and to identify global hotspots of fragmentation and connectivity for the world's terrestrial carnivores. Species with less fragmentation (i.e. more interior high-quality habitat) had larger geographical ranges, a greater proportion of habitat within their range, greater habitat connectivity and a lower risk of extinction. Species with higher connectivity (i.e. less habitat isolation) also had a greater proportion of high-quality habitat, but had smaller, not larger, ranges, probably reflecting shorter distances between habitat patches for species with restricted distributions; such species were also more threatened, as would be expected given the negative relationship between range size and extinction risk. Fragmentation and connectivity did not differ among Carnivora families, and body mass was associated with connectivity but not fragmentation. On average, only 54.3 per cent of a species' geographical range comprised high-quality habitat, and more troubling, only 5.2 per cent of the range comprised such habitat within protected areas. Identification of global hotspots of fragmentation and connectivity will help guide strategic priorities for carnivore conservation.     

A stochastic metapopulation model accounting for habitat dynamics

A stochastic metapopulation model accounting for habitat dynamics is presented. This is the stochastic SIS logistic model with the novel aspect that it incorporates varying carrying capacity. We present results of Kurtz and Barbour, that provide deterministic and diffusion approximations for a wide class of stochastic models, in a form that most easily allows their direct application to population models. These results are used to show that a suitably scaled version of the metapopulation model converges, uniformly in probability over finite time intervals, to a deterministic model previously studied in the ecological literature. Additionally, they allow us to establish a bivariate normal approximation to the quasi-stationary distribution of the process. This allows us to consider the effects of habitat dynamics on metapopulation modelling through a comparison with the stochastic SIS logistic model and provides an effective means for modelling metapopulations inhabiting dynamic landscapes.     

Summer habitat relationships of barbels in south‐west Spain

An analysis was made of the associations with local habitat features of barbels ( Barbus sp.) of a Mediterranean river basin. The analysis was based on the presence data from sampling the upper, middle, and lower reaches of 31 rivers in the middle Guadiana River basin (south‐west Spain). Numerous local habitat variables were determined, including the river's size and substratum, physicochemical variables of water, and the aquatic and riparian vegetation. For each species, a univariate analysis was performed using preference indices, and logistic regression was used to construct a parsimonious multivariate model and Gaussian response models with the most influential variables, quantifying the species' limits of tolerance. Distinct habitat associations for every species were obtained, mainly relating Barbus comiza to the larger habitats and higher water levels, Barbus microcephalus to the maintenance of lotic conditions and Barbus sclateri to more fluctuating rivers. Barbus steindachneri showed a different habitat relationship from that of the genetically almost identical B. comiza . Cover played a significant role in all but B. comiza .     

Species persistence decreases with habitat fragmentation: an analysis in periodic stochastic environments

This paper presents a study of a nonlinear reaction–diffusion population model in fragmented environments. The model is set on , with periodic heterogeneous coefficients obtained using stochastic processes. Using a criterion of species persistence based on the notion of principal eigenvalue of an elliptic operator, we provided a precise numerical analysis of the interactions between habitat fragmentation and species persistence. The obtained results clearly indicated that species persistence strongly tends to decrease with habitat fragmentation. Moreover, comparing two stochastic models of landscape pattern generation, we observed that in addition to local fragmentation, a more global effect of the position of the habitat patches also influenced species persistence.      

Assessing the applicability of fuzzy neural networks for habitat preference evaluation of Japanese medaka (Oryzias latipes)

Species–environment relationships are key information for the development of planning and management strategies for conservation or restoration of ecosystems. Artificial neural networks (ANNs) are one widely applied type of species distribution model (SDM). Fuzzy neural networks (FNNs), that is, fuzzified ANNs, have been introduced to take into account the uncertainties inherent in fish behaviour and errors in input data. Despite their high predictive ability in modelling complex systems, FNNs cannot describe habitat preference curves (HPCs), although these are the basis for habitat quality assessment. The present study therefore aimed to evaluate the applicability of FNNs for modelling habitat preference and spatial distributions of Japanese medaka (Oryzias latipes), one of the most common freshwater fish in Japan. Three independent data sets were collected during a series of field surveys and used for model development and evaluation of FNNs. A weight decay backpropagation algorithm was additionally introduced, and its effects on the FNNs were evaluated on the basis of model performance and habitat preference information retrieved from the field observation data. Modified sensitivity analysis was applied to derive HPCs of the target fish. Application of weight decay backpropagation markedly reduced the variability of the model structures, improved the generalization ability of the FNNs, and resulted in well-converged and consistent HPCs that were similar to those evaluated by fuzzy habitat preference models. These results support the applicability of FNNs to habitat preference modelling, which can provide useful information on the habitat use by the target fish. Further study should focus on the effects of sources of uncertainty, such as zero abundance, on the SDMs and the resulting habitat preference evaluation.     

Bayesian joint models with INLA exploring marine mobile predator–prey and competitor species habitat overlap

Understanding spatial physical habitat selection driven by competition and/or predator–prey interactions of mobile marine species is a fundamental goal of spatial ecology. However, spatial counts or density data for highly mobile animals often (1) include excess zeros, (2) have spatial correlation, and (3) have highly nonlinear relationships with physical habitat variables, which results in the need for complex joint spatial models. In this paper, we test the use of Bayesian hierarchical hurdle and zero‐inflated joint models with integrated nested Laplace approximation (INLA), to fit complex joint models to spatial patterns of eight mobile marine species (grey seal, harbor seal, harbor porpoise, common guillemot, black‐legged kittiwake, northern gannet, herring, and sandeels). For each joint model, we specified nonlinear smoothed effect of physical habitat covariates and selected either competing species or predator–prey interactions. Out of a range of six ecologically important physical and biologic variables that are predicted to change with climate change and large‐scale energy extraction, we identified the most important habitat variables for each species and present the relationships between these bio/physical variables and species distributions. In particular, we found that net primary production played a significant role in determining habitat preferences of all the selected mobile marine species. We have shown that the INLA method is well‐suited for modeling spatially correlated data with excessive zeros and is an efficient approach to fit complex joint spatial models with nonlinear effects of covariates. Our approach has demonstrated its ability to define joint habitat selection for both competing and prey–predator species that can be relevant to numerous issues in the management and conservation of mobile marine species.