Wildlife Habitat Studies Research Articles

A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models

<p>Se ha probado que los mapas que muestran explícitamente las relaciones especie-hábitat constituyen herramientas valiosas en aplicaciones de conservación y gestión, incluyendo la evaluación sobre qué especies y de qué forma se pueden ver afectadas por el cambio climático a gran escala, la fragmentación progresiva del hábitat y los usos del suelo a nivel local. Diversos estudios se han centrado en utilizar la teledetección como herramienta que permite caracterizar la vegetación para el análisis de la selección del hábitat y para cartografiar las relaciones con el entorno natural. Uno de los tipos de hábitats más difíciles de caracterizar mediante teledetección son los sistemas forestales verticales y horizontales complejos. Su caracterización es necesaria para estudiar los aspectos determinantes y/o limitantes para las especies. El uso de la teledetección activa mediante sensores LiDAR y RADAR ha suscitado gran interés en el ámbito de la investigación de especies de fauna silvestre en áreas forestales así como su gestión, dado el potencial de esta tecnología para representar características tridimensionales de estos hábitats. El objetivo de este artículo de revisión es analizar las aplicaciones de teledetección activa en los estudios de hábitat de fauna silvestre en zonas forestales a través de búsquedas de palabras claves en la <em>Web of Science</em>. Se presentan las métricas y métodos comúnmente utilizados, los avances recientes en la caracterización de hábitats forestales y se recomiendan líneas futuras de investigación en el área de teledetección que podrían beneficiar estudios sobre fauna silvestre en ámbitos forestales que actualmente o no existen o están infrautilizados. También se destaca el valor potencial de la fusión de datos de sensores activos y pasivos para la representación de múltiples dimensiones y escalas del hábitat forestal. Si bien el uso de la teledetección en estudios de hábitat de fauna silvestre se ha incrementado en los últimos años, la comunicación fluida entre las comunidades científicas relacionadas con la teledetección, la gestión forestal y la ecología es vital para garantizar el uso y comprensión adecuados de los datos, permitiendo un mejor conocimiento de las necesidades de los usuarios.</p><p> </p>

Integrating optical satellite data and airborne laser scanning in habitat classification for wildlife management

Wildlife habitat selection is determined by a wide range of factors including food availability, shelter, security and landscape heterogeneity all of which are closely related to the more readily mapped landcover types and disturbance regimes. Regional wildlife habitat studies often used moderate resolution multispectral satellite imagery for wall to wall mapping, because it offers a favourable mix of availability, cost and resolution. However, certain habitat characteristics such as canopy structure and topographic factors are not well discriminated with these passive, optical datasets. Airborne laser scanning (ALS) provides highly accurate three dimensional data on canopy structure and the underlying terrain, thereby offers significant enhancements to wildlife habitat mapping. In this paper, we introduce an approach to integrate ALS data and multispectral images to develop a new heuristic wildlife habitat classifier for western Alberta. Our method combines ALS direct measures of canopy height, and cover with optical estimates of species (conifer vs. deciduous) composition into a decision tree classifier for habitat – or landcover types. We believe this new approach is highly versatile and transferable, because class rules can be easily adapted for other species or functional groups. We discuss the implications of increased ALS availability for habitat mapping and wildlife management and provide recommendations for integrating multispectral and ALS data into wildlife management.

Hibernation site requirements of bats in man‐made hibernacula in a spatial context

Bat hibernacula selection depends on various spatial and nonspatial variables that differ widely between sites. However, previous studies have focused mainly on nonspatial variables. This research investigated factors that determined the abundance and species richness of hibernating bats in hibernation objects of the New Dutch Waterline, The Netherlands, and determined the relevant scales over which spatial factors operate using regression techniques and ecological-niche factor analyses. The effects of 32 predictor variables on several response variables, i.e., the total bat abundance, species richness, and abundance and presence of bat species, were investigated. Predictor variables were classified as internal variables (e.g., building size, climatic conditions, and human access) or external variables (e.g., ground and vegetation cover and land cover type) that were measured at different spatial scales to study the influence of the spatial context. The internal building variables (mainly the size of hibernacula and the number of hiding possibilities) affected the hibernating bat abundance and species richness. Climatic variables, such as changes in temperature and humidity, were less important. The hibernation site suitability was also influenced by spatial variables at a variety of scales, thereby indicating the importance of scale-dependent species-environment relationships. The absence of human use and public access enhanced hibernation site suitability, but the internal size-related variables had the greatest positive effect on hibernation site suitability. These results demonstrate the importance of considering the different spatial scales of the surrounding landscape to better understand habitat selection, and they offer directives to managers to optimize objects for hibernating bats and to improve management and bat conservation. The analyses have wider applications to other wildlife-habitat studies.

The habitat sampling and analysis paradigm has limited value in animal conservation: A prequel

AbstractIn this essay, I make the case that our studies of wildlife and habitat are largely decoupled from any meaningful relationship to the distribution of the study species. The field that we broadly classify as wildlife–habitat relationships is characterized by an increasing number of studies that gather additional data on phenomena that are already well studied. I offer that unless we make changes to the fundamental aspect of study design, our studies will fail to advance conservation of species. The current habitat sampling and analysis paradigm involves identification of a convenient study area, drawing samples from the usual list of parameters, conducting a series of statistical analyses, comparing findings to other studies, and justifying publication by extrapolating findings to some unspecified larger area. Recommendations for management are usually vague and are seldom tested for efficacy. Most of our habitat studies have little relevance to the target species with regard to viability. Attempts to translate the “best scientific information” into a set of management guidelines for a species produce one size fits all documents. I describe how we usually compromise our studies well before data collection by failing to establish a cogent framework for sampling from an ecologically meaningful unit of a population, but rather sample based on funding priorities and convenience. Specifying the sampling universe for a species sets the stage for properly establishing the sampling frame. Although we always have a target population, that target is often the result of personal, political, or administrative interest, but has little to do with biological reality. I review various intraspecies levels that could be a focus for study, including subspecies and especially ecotypes. Although making assumptions about our study species and habitat parameters is a necessary step, carrying forward untested assumptions from previous studies and failing to test new ones substantially negates the application of research results to meaningful management actions. I include recommendations for enhancing studies of wildlife and habitat with the intent of altering the current norm of wildlife–habitat studies. © 2012 The Wildlife Society.

Using multitemporal satellite imagery to characterize forest wildlife habitat: The case of ruffed grouse

Like many similar forest species, ruffed grouse (Bonasa umbellus; hereafter grouse) populations in the central and southern Appalachians (CSA) are strongly affected by forest composition at the landscape scale. Because these populations are in decline, managers require accurate forest maps to understand how stand level characteristics affect the survival and reproductive potentials of individual birds to design management strategies that improve grouse abundance. However, traditional mapping techniques are often labor-intensive and cost-prohibitive. We used a normalized difference vegetation index (NDVI) from each of 8 Landsat images and the digital elevation model (DEM)-derived variables of elevation and aspect in discriminant analyses to classify 7 study areas to 3 overstory classes (evergreen, hardwoods, and oak) and distinguish evergreen and deciduous understories in the CSA, 2000–2002. Overall accuracy was 82.08%, varying from 83.59% for oak to 79.79% for hardwoods overstories. Periods with large phenological differences among classes, particularly early and late spring, were most useful for discriminating overstory vegetation types. Alternatively, winter NDVI in combination with elevation was critical for differentiating evergreen and deciduous understories. Multitemporal image sets used in concert with DEMs provided a cost-effective alternative to hyperspectral sensors for improving wildlife habitat classification accuracy with Landsat imagery. This allowed for enhanced understanding of grouse-habitat relationships and habitat affects on grouse populations that allowed for improved management. With the incorporation of simple adjustments for local forest plant species phenology into the model, it may be used to better classify wildlife habitat of similar species in areas with comparable forest communities and topography. Multitemporal images can also be used to differentiate grassland communities, monitor wetlands, and serve as baseline data for detecting changes in land use over longer temporal scales, making their use in forest wildlife habitat studies cost-justifiable.

A comparison of forest resource inventory, provincial land cover maps and field surveys for wildlife habitat analysis in the Great Lakes – St. Lawrence forest

In a portion of central Ontario, Canada we assessed the classification agreement between field-based estimates of forest stand composition and each of two mapped data sources used in wildlife habitat studies, the Forest Resource Inventory (FRI) and satellite-image derived Provincial Land Cover (PLC). At two study areas, Algonquin Provincial Park (APP) and Wildlife Management Unit 49 (WMU49), we surveyed 119 forest stands and 40 water and wetland stands. Correspondence levels between FRI and field classifications were 48% in APP and 44% in WMU49 when assessing six forest cover types. With only four simplified forest cover types, levels improved to 77% in APP and 63% in WMU49. Correspondence between PLC and field classifications for three forested stand types was approximately 63% in APP and 55% in WMU49. Because of the poor to moderate level of correspondence we detected between map and field classifications, we recommend that care be exercised when FRI or PLC maps are used in forest and wildlife research and management planning. Key words: forest resource inventory, FRI, provincial land cover, PLC, Landsat Thematic Mapper, map accuracy, map correspondence, map agreement, Ontario, wildlife habitat

Incorporating remotely sensed tree canopy cover data into broad scale assessments of wildlife habitat distribution and conservation

Remote sensing provides critical information for broad scale assessments of wildlife habitat distribution and conservation. However, such efforts have been typically unable to incorporate information about vegetation structure, a variable important for explaining the distribution of many wildlife species. We evaluated the consequences of incorporating remotely sensed information about horizontal vegetation structure into current assessments of wildlife habitat distribution and conservation. For this, we integrated the new NLCD tree canopy cover product into the US GAP Analysis database, using avian species and the finished Idaho GAP Analysis as a case study. We found: (1) a 15-68% decrease in the extent of the predicted habitat for avian species associated with specific tree canopy conditions, (2) a marked decrease in the species richness values predicted at the Landsat pixel scale, but not at coarser scales, (3) a modified distribution of biodiversity hotspots, and (4) surprising results in conservation assessment: despite the strong changes in the species predicted habitats, their distribution in relation to the reserves network remained the same. This study highlights the value of area wide vegetation structure data for refined biodiversity and conservation analyses. We discuss further opportunities and limitations for the use of the NLCD data in wildlife habitat studies.

Remote sensing and forest inventory for wildlife habitat assessment

Researchers and managers undertaking wildlife habitat assessments commonly require spatially explicit environmental map layers such as those derived from forest inventory and remote sensing. However, end users of geospatial products must often make choices regarding the source and level of detail required for characterizing habitat elements, with few published resources available for guidance. We appraised three environmental data sources that represent options often available to researchers and managers in wildlife ecological studies: (i) a pre-existing forest inventory; (ii) a general-purpose, single-attribute remote sensing land cover map; and (iii) a specific-purpose, multi-attribute remote sensing database. The three information sources were evaluated with two complementary analyses: the first designed to appraise levels of map quality (assessed on the basis of accuracy, vagueness, completion, consistency, level of measurement, and detail) and the second designed to assess their relative capacity to explain patterns of grizzly bear ( Ursus arctos) telemetry locations across a 100,000-km 2 study area in west-central Alberta, Canada. We found the forest inventory database to be reasonably functional in its ability to support resource selection analysis in regions where coverage was available, but overall, the data suffered from quality issues related to completeness accuracy, and consistency. The general-purpose remote sensing land cover product ranked higher in terms of overall map quality, but demonstrated a lower capacity for explaining observed patterns of grizzly bear habitat use. We found the best results using the specific-purpose, multi-attribute remote sensing database, and recommend that similar information sources be used as the foundation for wildlife habitat studies whenever possible, particularly those involving large areas that span jurisdictional boundaries.

A Simple Solar Radiation Index for Wildlife Habitat Studies

Abstract: Solar radiation is a potentially important covariate in many wildlife habitat studies, but it is typically addressed only indirectly, using problematic surrogates like aspect or hillshade. We devised a simple solar radiation index (SRI) that combines readily available information about aspect, slope, and latitude. Our SRI is proportional to the amount of extraterrestrial solar radiation theoretically striking an arbitrarily oriented surface during the hour surrounding solar noon on the equinox. Because it derives from first geometric principles and is linearly distributed, SRI offers clear advantages over aspect‐based surrogates. The SRI also is superior to hillshade, which we found to be sometimes imprecise and ill‐behaved. To illustrate application of our SRI, we assessed niche separation among 3 ungulate species along a single environmental axis, solar radiation, on the northern Yellowstone winter range. We detected no difference between the niches occupied by bighorn sheep (Ovis canadensis) and elk (Cervus elaphus; P = 0.104), but found that mule deer (Odocoileus hemionus) tended to use areas receiving more solar radiation than either of the other species (P < 0.001). Overall, our SRI provides a useful metric that can reduce noise, improve interpretability, and increase parsimony in wildlife habitat models containing a solar radiation component.

Scale for resource selection functions

ABSTRACTResource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information‐theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k‐fold cross validation.

Resource Selection Functions Based on Use–Availability Data: Theoretical Motivation and Evaluation Methods

Applications of logistic regression in a used–unused design in wildlife habitat studies often suffer from asymmetry of errors: used resource units (landscape locations) are known with certainty, whereas unused resource units might be observed to be used with greater sampling intensity. More appropriate might be to use logistic regression to estimate a resource selection function (RSF) tied to a use–availability design based on independent samples drawn from used and available resource units. We review the theoretical motivation for RSFs and show that sample “contamination” and the exponential form commonly assumed for the RSF are not concerns, contrary to recent statements by Keating and Cherry (2004; Use and interpretation of logistic regression in habitat-selection studies. Journal of Wildlife Management 68:774–789). To do this, we re-derive the use–availability likelihood and show that it can be maximized by logistic regression software. We then consider 2 case studies that illustrate our findings. For our first case study, we fit both RSFs and resource selection probability functions (RSPF) to point count data for 4 bird species with varying levels of occurrence among sample blocks. Drawing on our new derivation of the likelihood, we sample available resource units with replacement and assume overlapping distributions of used and available resource units. Irrespective of overlap, we observed approximate proportionality between predictions of a RSF and RSPF. For our second case study, we evaluate the classic use-availability design suggested by Manly et al. (2002), where availability is sampled without replacement, and we systematically introduce contamination to a sample of available units applied to RSFs for woodland caribou (Rangifer tarandus caribou). Although contamination appeared to reduce the magnitude of one RSF beta coefficient, change in magnitude exceeded sampling variation only when >20% of the available units were confirmed caribou use locations (i.e., contaminated). These empirically based simulations suggest that previously recommended sampling designs are robust to contamination. We conclude with a new validation method for evaluating predictive performance of a RSF and for assessing if the model deviates from being proportional to the probability of use of a resource unit.

K-select analysis: a new method to analyse habitat selection in radio-tracking studies

Two kinds of wildlife habitat studies can be distinguished in the literature: hindcasting and forecasting studies. Hindcasting studies aim to emphasize among a large set of habitat variables those that are of interest for the focus species, whereas forecasting studies are intended to predict habitat selection according to a small number of habitat variables for a given area. We provide here a new analytical tool which relies on the concept of ecological niche, the K-select analysis, for hindcasting studies of habitat selection by animals using radio-tracking data. Each habitat variable defines one dimension in the ecological space. For each animal, the difference between the vector of average available habitat conditions and the vector of average used conditions defines the marginality vector. Its size is proportional to the importance of habitat selection, and its direction indicates which variables are selected. By performing a non-centered principal component analysis of the table containing the coordinates of the marginality vectors of each animal (row) on the habitat variables (column), the K-select analysis returns a linear combination of habitat variables for which the average marginality is greatest. It is a synthesis of variables which contributes the most to the habitat selection. As with principal component analysis, the biological significance of the factorial axes is deduced from the loading of variables. An example is provided: habitat selection by wild boar is studied in a Mediterranean habitat using the K-select analysis. The numerous advantages of the analysis (a large number of variables that can be included, individual variability in habitat selection taken into account, a lack of too strict underlying hypotheses) make it a powerful approach in radio-tracking studies designed to identify habitat variables that are selected by animals.

Forest classification in an Amazonian rainforest landscape using pteridophytes as indicator species

Habitat classification systems are poorly developed for tropical rainforests, where extremely high plant species richness causes numerous methodological difficulties. We used an indicator species approach to classify primary rainforest vegetation for purposes of comparative wildlife habitat studies. We documented species composition of pteridophytes (ferns and fern allies) in 635 plots (2×100 m) along 8 transects within a continuous rainforest landscape in northeastern Peruvian Amazonia. Considerable floristic variation was found when the data were analyzed using multivariate methods. The obtained forest classification was interpreted with the help of indicator value analysis and known soil preferences of the pteridophyte species. The final classification included four forest types: 1) inundated forests, 2) terrace forests, 3) intermediate tierra firme forests and 4) Pebas Formation forests. This rapid and relatively simple vegetation classification technique offers a practical, quantitative method for large‐scale vegetation inventory in complex rainforest landscapes.

A Neural Network Method for Mixture Estimation for Vegetation Mapping

While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife habitat studies, can benefit from knowledge of mixes. This article examines various algorithms that use data from the Landsat Thematic Mapper (TM) satellite to estimate mixtures of vegetation types within forest stands. Included in the study are maximum likelihood classification and linear mixture models as well as a new methodology based on the ARTMAP neural network. Two paradigms are considered: classification methods, which describe stand-level vegetation mixtures as mosaics of pixels, each identified with its primary vegetation class; and mixture methods, which treat samples as blends of vegetation, even at the pixel level. Comparative analysis of these mixture estimation methods, tested on data from the Plumas National Forest, yields the following conclusions: 1) Accurate estimates of proportions of hardwood and conifer cover within stands can be obtained, particularly when brush is not present in the understory; 2) ARTMAP outperforms statistical methods and linear mixture models in both the classification and the mixture paradigms; 3) topographic correction fails to improve mapping accuracy; and 4) the new ARTMAP mixture system produces the most accurate overall results. The Plumas data set has been made available to other researchers for further development of new mapping methods and comparison with the quantitative studies presented here, which establish initial benchmark standards.