Sensitivity of species-distribution models to error,bias, and model design: An application to resource selection functions for woodland caribou

Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.     

Error propagation in spatial modeling of public health data: a simulation approach using pediatric blood lead level data for Syracuse,New York

Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results.     

Spatial repartition and ontogenetic shifts in habitat use by coral reef fishes (Moorea,French Polynesia)

This study explores the extent to which ontogenetic habitat shifts modify spatial patterns of fish established at settlement in the Moorea Island lagoon (French Polynesia). The lagoon of Moorea Island was divided into 12 habitat zones (i.e. coral seascapes), which were distinct in terms of depth, wave exposure, and substratum composition. Eighty-two species of recently settled juveniles were recorded from March to June 2001. Visual censuses documented changes in the distribution of juveniles of each species over time among the 12 habitats. Two patterns of juvenile habitat use were found among species. Firstly, some species settled and remained in the same habitat until the adoption of the adult habitats (i.e. recruitment; e.g. Chaetodon citrinellus, Halichoeres hortulanus, Rhinecanthus aculeatus). Secondly, others settled to several habitats and then disappeared from some habitats through differential mortality and/or post-settlement movement (e.g. 65–70 mm size class for Ctenochaetus striatus, 40–45 mm size class for Epinephelus merra, 50–55 mm size class for Scarus sordidus). A comparison of the spatial distribution of juveniles to that of adults (61 species recorded at both stages) illustrated four patterns of subsequent recruitment in habitat use: (1) an increase in the number of habitats used during the adult stage (e.g. H. hortulanus, Mulloidichthys flavolineatus); (2) a decrease in the number of habitats adults used compared to recently settled juveniles (e.g. Chrysiptera leucopoma, Stethojulis bandanensis); (3) the use of different habitat types (e.g. Acanthurus triostegus, Caranx melampygus); and (4) no change in habitat use (e.g. Naso litturatus, Stegastes nigricans). Of the 20 most abundant species recorded in Moorea lagoon, 12 species modified the spatial patterns established at settlement by an ontogenetic habitat shift.Communicated by T. Ikeda, Hakodate     

The importance of incorporating functional habitats into conservation planning for highly mobile species in dynamic systems

The distribution of mobile species in dynamic systems can vary greatly over time and space. Estimating their population size and geographic range can be problematic and affect the accuracy of conservation assessments. Scarce data on mobile species and the resources they need can also limit the type of analytical approaches available to derive such estimates. We quantified change in availability and use of key ecological resources required for breeding for a critically endangered nomadic habitat specialist, the Swift Parrot (Lathamus discolor). We compared estimates of occupied habitat derived from dynamic presence‐background (i.e., presence‐only data) climatic models with estimates derived from dynamic occupancy models that included a direct measure of food availability. We then compared estimates that incorporate fine‐resolution spatial data on the availability of key ecological resources (i.e., functional habitats) with more common approaches that focus on broader climatic suitability or vegetation cover (due to the absence of fine‐resolution data). The occupancy models produced significantly (P < 0.001) smaller (up to an order of magnitude) and more spatially discrete estimates of the total occupied area than climate‐based models. The spatial location and extent of the total area occupied with the occupancy models was highly variable between years (131 and 1498 km²). Estimates accounting for the area of functional habitats were significantly smaller (2–58% [SD 16]) than estimates based only on the total area occupied. An increase or decrease in the area of one functional habitat (foraging or nesting) did not necessarily correspond to an increase or decrease in the other. Thus, an increase in the extent of occupied area may not equate to improved habitat quality or function. We argue these patterns are typical for mobile resource specialists but often go unnoticed because of limited data over relevant spatial and temporal scales and lack of spatial data on the availability of key resources. Understanding changes in the relative availability of functional habitats is crucial to informing conservation planning and accurately assessing extinction risk for mobile resource specialists.     

Incorporation of Recreational Fishing Effort into Design of Marine Protected Areas

Abstract:  Theoretical models of marine protected areas (MPAs) that explore benefits to fisheries or biodiversity conservation often assume a dynamic pool of fishing effort. For instance, effort is homogenously distributed over areas from which subsets of reserves are chosen. I tested this and other model assumptions with a case study of the multiple-use Jervis Bay Marine Park. Prior to zoning of the park I conducted 166 surveys of the park's recreational fisheries, plotting the location of 16,009 anglers. I converted these plots into diagrams of fishing effort and analyzed correlates between fishing and habitat and the effect of two reserve designs—the draft and final zoning plans of the park—on the 15 fisheries observed. Fisheries were strongly correlated with particular habitats and had negatively skewed and often bimodal spatial distribution. The second mode of intensely fished habitat could be 6 SD greater than the fishery's mean allocation of effort by area. In the draft-zoning plan, sanctuary zone (no-take) area and potential subduction of fishing effort were similar. In the final plan, which was altered in response to public comment, the area of sanctuary zone increased, and the impact on fishing effort decreased. In only one case was a fishery's most intensely targeted location closed to fishing. Because of the discriminating manner with which fishers target habitats, if simple percentage targets are used for planning, sanctuary location can be adjusted to avoid existing fishing effort. According to modeled outcomes, the implication of this may be diminished reserve effectiveness. To address this, reserve area should be implicitly linked to subducted fishing effort when promoting or modeling MPAs.     

Evolution of within-colony distribution patterns of birds in response to habitat structure

It has long been suggested that habitat structure affects how colonial birds are distributed within their nesting aggregations, but this hypothesis has never been formally tested. The aim of this study was to test for a correlated evolution between habitat heterogeneity and within-colony distributions of Ciconiiformes by using Pagel’s general method of comparative analysis for discrete variables. The analysis indicated that central-periphery gradients of distribution (high-quality individuals occupying central nesting locations) prevail in species breeding in homogeneous habitats. These were mainly ground-nesting larids and spheniscids, where clear central-periphery patterns were recorded in ca. 80 % of the taxa. Since homogeneous habitats provide little variation in the physical quality of nest sites, central nesting locations should be largely preferred because they give better protection against predators by means of more efficient predator detection and deterrence. By contrast, central-periphery gradients tended to be disrupted in heterogeneous habitats, where 75 % of colonial Ciconiiform species showed uniform patterns of distribution. Under this model of distribution, edge nest sites of high physical quality confer higher fitness benefits in comparison to low-quality central sites, and thus, high-quality pairs are likely to choose nest sites irrespectively of their within-colony location. Breeding in homogeneous habitats and uniform distribution patterns were identified as probable ancestral states in Ciconiiformes, but there was a significant transition rate from uniform to central-periphery distributions in species occupying homogeneous habitats.     

Modeling resource selection using polytomous logistic regression and kernel density estimates

Wildlife resource selection studies typically compare used to available resources; selection or avoidance occurs when use is disproportionately greater or less than availability. Comparing used to available resources is problematic because results are often greatly influenced by what is considered available to the animal. Moreover, placing relocation points within resource units is often difficult due to radiotelemetry and mapping errors. Given these problems, we suggest that an animal’s resource use be summarized at the scale of the home range (i.e., the spatial distribution of all point locations of an animal) rather than by individual points that are considered used or available. To account for differences in use-intensity throughout an animal’s home range, we model resource selection using kernel density estimates and polytomous logistic regression. We present a case study of elk (Cervus elaphus) resource selection in South Dakota to illustrate the procedure. There are several advantages of our proposed approach. First, resource availability goes undefined by the investigator, which is a difficult and often arbitrary decision. Instead, the technique compares the intensity of animal use throughout the home range. This technique also avoids problems with classifying locations rigidly as used or unused. Second, location coordinates do not need to be placed within mapped resource units, which is problematic given mapping and telemetry error. Finally, resource use is considered at an appropriate scale for management because most wildlife resource decisions are made at the level of the patch. Despite the advantages of this use-intensity procedure, future research should address spatial autocorrelation and develop spatial models for ordered categorical variables.     

Measurement error causes scale-dependent threshold erosion of biological signals in animal movement data.

Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy mu, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of > or = 10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD > or = 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.     

A mechanistic model of GPS collar location data: Implications for analysis and bias mitigation

Global positioning system (GPS) collars have revolutionized the collection of animal location data; however, it is well-recognized that considerable bias can be present in these data due to habitat or behavior-induced obstruction of satellite signals resulting in inaccurate or missing locations. To date, no explicit theoretical framework of GPS fix acquisition specific to animal telemetry has been presented, and studies make differing assumptions regarding factors influencing GPS fix acquisition and how these data should be analyzed. Inappropriate statistical models have been used, interaction effects have been misunderstood, and the implementation of bias mitigation techniques has been problematic. Herein we outline current conceptual and analytical problems in the GPS animal telemetry literature, and subsequently present a theoretical model-based framework for GPS fix acquisition that clarifies the single and interactive effects of habitat and behavioral obstruction, fix interval, and collar model on GPS collar performance. By recognizing that GPS fix acquisition is a Bernoulli process, it becomes apparent that all forms of obstruction inherently interact with each other, making generalizations across study areas, study species, and collar models problematic. Stationary collar tests to determine the probability of fix acquisition (PFA), location accuracy, and the response to sources of obstruction are thus of limited applicability to animal-deployed collars. Bias mitigation techniques that extrapolate PFA models across samples, especially those using stationary collar tests to correct animal-deployed collars, are theoretically unsound. It is also demonstrated that nonlinearities in the relationships between sources of obstruction and PFA complicate PFA modeling with limited data and that even slight model misspecification can lead to considerable errors in correction factors, especially when using inverse weighting to mitigate bias. By emphasizing the importance of GPS collar sensitivity and ephemeris retention, the theoretical framework predicts that newer, more sensitive GPS collars will be less severely biased by sources of obstruction than reported for the older, less sensitive collars that have been used in the majority of GPS performance studies to date and we expect this trend to continue. This heuristic modeling exercise should be of value to researchers planning and analyzing studies using GPS collars and it also establishes a starting point for future theoretical investigations into GPS collar performance and bias mitigation.     

A Framework for Conceptualizing Human Effects on Landscapes and Its Relevance to Management and Research Models

Abstract: The concept of habitat fragmentation is limited in its ability to describe the range of possible landscape configurations created by a variety of disturbances. This limitation is especially problematic in landscapes where human use of the habitat matrix occurs at multiple levels and where habitat modification may be a more important consideration than a simple binary classification of habitat versus nonhabitat. We propose a synthesizing scheme that places intact, variegated, fragmented, and relictual landscape states on a continuum, depending on the degree of habitat destruction. At a second level, the scheme considers the patterns of habitat modification that are imposed on remaining habitats. Management for conservation involves halting and sometimes reversing the trends of habitat destruction and modification. Conservation strategies will differ according to the state of alteration of the landscape, but all strategies include some consideration of the degree of modification of the matrix in determining habitat viability. It is convenient for biologists to assess landscape alteration state in terms of the persistence of large structural elements such as trees. Because animal species use habitats differently, however, they also experience the landscape differently. A landscape considered structurally fragmented by humans may be functionally variegated to other species. Therefore, it is necessary to consider the extent to which the entire landscape, including the matrix, is accessible and utilized by organisms with different spatial scales of resource use.     

Distribution patterns of sea urchins and barrens in shallow Mediterranean rocky reefs impacted by the illegal fishery of the rock-boring mollusc<Emphasis Type="Italic"> Lithophaga lithophaga</Emphasis>

Shallow rocky habitats in SW Apulia (SE Italy, Mediterranean Sea) were surveyed in late spring 2002 to assess distribution patterns of sea urchins (Paracentrotus lividus and Arbacia lixula) and barren habitats (coralline barrens and bare substrates) in rocky reefs impacted by the destructive fishery of the rock-boring date-mussel Lithophaga lithophaga. Sea urchin density, test size-structure and biomass, and the percent cover of barrens were evaluated at four locations (5–6 km apart from each other), two heavily impacted by the date-mussel fishery and two controls. Sea urchin density and barren habitat cover were assessed at two and three sites (100–300 m apart), respectively, within each location. Sea urchin biomass was evaluated only at the scale of locations. Average density of P. lividus did not significantly change between impacted locations and controls, whereas A. lixula showed a greater density at the impacted locations. Distribution patterns of A. lixula, in addition, differed at the spatial scale of a few metres between impacted locations and controls, being generally more aggregated at the controls. The size-frequency distribution (test diameter) of P. lividus showed a mode at 3–4 cm at the impacted locations compared to a mode at 2–3 cm in the controls. The size-frequency of A. lixula was bimodal at the damaged locations (with modes at 1–2 and 4–5 cm, respectively) and unimodal (with the mode at 4–5 cm) at the controls. Average biomass of both sea urchins (P. lividus and A. lixula) was two- to fourfold greater at the impacted locations (~600 g wet wt m^–2) than at the controls (150–250 g wet wt m^–2). Barren habitats had a far greater average cover (mainly of macroalgae) at the impacted locations (from 79% to 96%) than at control locations (from 7% to 21%). These results show that the date-mussel fishery may have the potential to affect distribution patterns of sea urchins and to greatly enhance the percent cover of barren grounds in shallow Mediterranean rocky reefs.Communicated by R. Cattaneo-Vietti, Genova     

Covariates affecting spatial variability in bison travel behavior in Yellowstone National Park.

Understanding mechanisms influencing the movement paths of animals is essential for comprehending behavior and accurately predicting use of travel corridors. In Yellowstone National Park (USA), the effects of roads and winter road grooming on bison (Bison bison) travel routes and spatial dynamics have been debated for more than a decade. However, no rigorous studies have been conducted on bison spatial movement patterns. We collected 121 380 locations from 14 female bison with GPS collars in central Yellowstone to examine how topography, habitat type, roads, and elevation affected the probability of bison travel year-round. We also conducted daily winter bison road use surveys (2003-2005) to quantify how topography and habitat type influenced spatial variability in the amount of bison road travel. Using model comparison techniques, we found the probability of bison travel and spatial distribution of travel locations were affected by multiple topographic and habitat type attributes including slope, landscape roughness, habitat type, elevation, and distances to streams, foraging areas, forested habitats, and roads. Streams were the most influential natural landscape feature affecting bison travel, and results suggest the bison travel network throughout central Yellowstone is spatially defined largely by the presence of streams that connect foraging areas. Also, the probability of bison travel was higher in regions of variable topography that constrain movements, such as in canyons. Pronounced travel corridors existed both in close association with roads and distant from any roads, and results indicate that roads may facilitate bison travel in certain areas. However, our findings suggest that many road segments used as travel corridors are overlaid upon natural travel pathways because road segments receiving high amounts of bison travel had similar landscape features as natural travel corridors. We suggest that most spatial patterns in bison road travel are a manifestation of general spatial travel trends. Our research offers novel insights into bison spatial dynamics and provides conceptual and analytical frameworks for examining movement patterns of other species.     

An evaluation of three statistical methods used to model resource selection

The performance of statistical methods for modeling resource selection by animals is difficult to evaluate with field data because true selection patterns are unknown. Simulated data based on a known probability distribution, though, can be used to evaluate statistical methods. Models should estimate true selection patterns if they are to be useful in analyzing and interpreting field data. We used simulation techniques to evaluate the effectiveness of three statistical methods used in modeling resource selection. We generated 25 use locations per animal and included 10, 20, 40, or 80 animals in samples of use locations. To simulate species of different mobility, we generated use locations at four levels according to a known probability distribution across DeSoto National Wildlife Refuge (DNWR) in eastern Nebraska and western Iowa, USA. We either generated 5 random locations per use location or 10,000 random locations (total) within 4 predetermined areas around use locations to determine how the definition of availability and the number of random locations affected results. We analyzed simulated data using discrete choice, logistic-regression, and a maximum entropy method (Maxent). We used a simple linear regression of estimated and known probability distributions and area under receiver operating characteristic curves (AUC) to evaluate the performance of each method. Each statistical method was affected differently by number of animals and random locations used in analyses, level at which selection of resources occurred, and area considered available. Discrete-choice modeling resulted in precise and accurate estimates of the true probability distribution when the area in which use locations were generated was ≥ the area defined to be available. Logistic-regression models were unbiased and precise when the area in which use locations were generated and the area defined to be available were the same size; the fit of these models improved with increased numbers of random locations. Maxent resulted in unbiased and precise estimates of the known probability distribution when the area in which use locations were generated was small (home-range level) and the area defined to be available was large (study area). Based on AUC analyses, all models estimated the selection distribution better than random chance. Results from AUC analyses, however, often contradicted results of the linear regression method used to evaluate model performance. Discrete-choice modeling was best able to estimate the known selection distribution in our study area regardless of sample size or number of random locations used in the analyses, but we recommend further studies using simulated data over different landscapes and different resource metrics to confirm our results. Our study offers an approach and guidance for others interested in assessing the utility of techniques for modeling resource selection in their study area.     

Reproduction in the diadematoid sea urchin Centrostephanus rodgersii in contrasting habitats along the coast of New South Wales, Australia

Reproduction in the sea urchin Centrostephanus rodgersii was examined in two types of habitats (“barrens”, i.e. habitats characterised by the high crustose coralline algal cover typical of urchin-barren grounds, and by the absence of macroalgae; and “fringe”, i.e. habitats characterised by a high macroalgal biomass and few C. rodgersii) at four locations in New South Wales. The four locations: the Solitary Islands, Sydney, Ulladulla and Eden, span the distribution of C.␣rodgersii from the subtropics at its northern limit to temperate waters near its southern limit. Histology and estimates of gonad retrieval rate (GRR) from January 1994 to October 1995 indicated that reproduction was synchronous at all locations. An increase in the tempo of gametogenesis in May and onset of spawning in June at all locations is consistent with entrainment in response to exogenous factors. Over the range studied, C. rodgersii experienced relatively similar daylength cycles and contrasting sea-temperature cycles. Short days and lunar conditions coinciding with the solstice appear likely proximate cues for the onset of spawning. The major difference in reproduction among locations was in the duration of spawning. In the southern parts of its range breeding occurred over a 5 to 6 mo period, whereas at the Solitary Islands it lasted ≃1 mo. At most locations the GRRs were significantly higher in the fringe habitat than in the barrens habitat. The lower reproductive output of urchins in the barrens habitat was attributed to the food-poor conditions typical of this habitat. The developing fishery for C. rodgersii is likely to be most effective from March to early May. Urchins from barrens areas may not provide sufficient yield to warrant harvesting. Received: 29 October 1997 / Accepted: 18 May 1998     

Influence of habitat on abundance and size structure of a large temperate-reef fish, Achoerodus viridis (Pisces: Labridae)

The relationship between densities of Achoerodus viridis (Pisces: Labridae) and reef habitats at various localities within New South Wales (NSW), Australia was examined. Types of habitats were quantified at inner, mid and outer estuarine locations in each of two estuaries (Botany Bay and Port Jackson) to determine whether purported patterns of movement from estuaries could be related to differences in habitat. Although the same types of habitat were generally found at all locations, differences in the proportion of habitat types were found between shallow and deep reefs and among inner, mid and outer estuarine locations for both estuaries. Shallow habitats were usually dominated by Ecklonia radiata, turf and/or fringe habitat in Botany Bay, whereas deep sites were generally dominated by urchin-grazed barrens habitat and, sometimes, sponge- and ascidian-dominated deep reef. Shallow sites in Port Jackson were dominated by a mixture of habitats, as were deep reefs at mid-estuarine locations. Other deep reefs in Port Jackson were dominated by E. radiata (inner estuarine) or barrens (outer estuarine) habitat. Thus, patterns of habitat cover were not consistent between estuaries and numbers of fish could not be related to proportional representation of habitat on reefs along estuarine gradients. Univariate and multivariate analyses showed that there was little evidence that any size class of fish was correlated with the proportional representation of a particular habitat or group of habitats. Counts of fish that focused on barrens and E. radiata forest habitats over a period of 10 yr showed that similar numbers and all sizes of fish were found in the two types of habitat. Greater numbers of small fish were, however, found in the E. radiata forest habitat than in the barrens habitat. Estimates of abundance along the coast of NSW (100s to 1000 km) in a range of habitats (e.g. ascidian-dominated reef, kelp forest, urchin-grazed barrens) showed that there was no indication that a particular habitat consistently had greater numbers of A. viridis than other habitats. Therefore, A. viridis of a range of sizes appears to be flexible in its use of habitats on reefs. Received: 24 December 1997 / Accepted: 23 June 1998     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.     

Estimated home ranges can misrepresent habitat relationships on patchy landscapes

Home ranges of animals are generally structured by the selective use of resource-bearing patches that comprise habitat. Based on this concept, home ranges of animals estimated from location data are commonly used to infer habitat relationships. Because home ranges estimated from animal locations are largely continuous in space, the resource-bearing patches selected by an animal from a fragmented distribution of patches would be difficult to discern; unselected patches included in the home range estimate would bias an understanding of important habitat relationships. To evaluate potential for this bias, we generated simulated home ranges based on optimal selection of resource-bearing patches across a series of simulated resource distributions that varied in the spatial continuity of resources. For simulated home ranges where selected patches were spatially disjunct, we included interstitial, unselected cells most likely to be traveled by an animal moving among selected patches. We compared characteristics of the simulated home ranges with and without interstitial patches to evaluate how insights derived from field estimates can differ from actual characteristics of home ranges, depending on patchiness of landscapes. Our results showed that contiguous home range estimates could lead to misleading insights on the quality, size, resource content, and efficiency of home ranges, proportional to the spatial discontinuity of resource-bearing patches. We conclude the potential bias of including unselected, largely irrelevant patches in the field estimates of home ranges of animals can be high, particularly for home range estimators that assume uniform use of space within home range boundaries. Thus, inferences about the habitat relationships that ultimately define an animal's home range can be misleading where animals occupy landscapes with patchily distributed resources.     

A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

Reefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250 m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250 m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints.     

Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis

Predicting species distribution and habitat suitability (HS) modelling, across broad spatial scales, is now a major challenge in marine ecology. The resulting knowledge is of considerable use in supporting the implementation of environmental legislation, integrated coastal zone management and ecosystem-based fisheries management. This contribution considers the identification of seafloor morphological characteristics, together with wave energy conditions, that determine the presence of European lobster (Homarus gammarus); and it predicts suitable habitats over the Basque continental shelf (Bay of Biscay), in summer. The results obtained, by applying Ecological-Niche Factor Analysis (ENFA), indicate that lobster habitat differs considerably from the mean environmental condition over the study area; likewise, that it is restrictive in terms of the range of conditions in which they dwell. The best of the environmental predictors found to be: distance to the rock substrate; Benthic Position Index; wave flux over the seafloor; and the underlying bathymetry. A habitat suitability map was produced, with a high model quality (Boyce index: 0.98 ± 0.06). The most suitable habitat for European lobster are locations at the boundary between sedimentary- and rocky-bottoms, coincident with seafloor depressions with a steep slope, with medium to high wave energy conditions, and located within a range of water depths of 35–40 m. This approach demonstrates the applicability of the method in case studies where only presence data are available, together with the inclusion of environmental variables obtained from different sources.