An investigation of uncertainty in field habitat mapping and the implications for detecting land cover change

Land cover data for landscape ecological studies are frequently obtained by field survey. In the United Kingdom, temporally separated field surveys have been used to identify the locations and magnitudes of recent changes in land cover. However, such map data contain errors which may seriously hinder the identification of land cover change and the extent and locations of rare landscape features. This paper investigates the extent of the differences between two sets of maps derived from field surveys within the Northumberland National Park in 1991 and 1992. The method used in each survey was the Phase 1 approach of the Nature Conservancy Council of Great Britain. Differences between maps were greatest for the land cover types with the smallest areas. Overall spatial correspondence between maps was found to be only 44.4%. A maximum of 14.4% of the total area surveyed was found to have undergone genuine land cover change. The remaining discrepancies, equivalent to 41.2% of the total survey area, were attributed primarily to differences of land cover interpretation between surveyors (classification error). Differences in boundary locations (positional error) were also noted, but were found to be a relatively minor source of error. The implications for the detection of land cover change and habitat mapping are discussed.     

Observing succession on aspen-dominated landscapes using a remote sensing-ecosystem approach

In the North American upper Great Lakes region, forests dominated by the aspens (Populus grandidentata Michx. – bigtooth aspen, and P. tremuloides Michx. – trembling aspen), which established after late 19th and early 20th century logging, are maturing and succession will create a new forest composition at landscape to regional scales. This study analyzed the capabilities of Landsat ETM+ remote sensing data combined with existing ecological land unit classifications to discriminate and quantify patterns of succession at the landscape scale over the 4200 ha University of Michigan Biological Station (UMBS) in northern Lower Michigan. In a hierarchical approach first multi-temporal Landsat ETM+ was used with a landscape ecosystem classification to map upland forest cover types (overall accuracy 91.7%). Next the aspen cover type was subset and successional pathways were mapped within that type (overall accuracy 89.8%). Results demonstrated that Landsat ETM+ may be useful for these purposes; stratification of upland from wetland types using an ecological land unit classification eliminated confounding issues; multi-temporal methods discriminated evergreen conifer versus deciduous understories. The Landsat ETM+ classifications were then used to quantify succession and its relationship to landform-level ecological land units. Forests on moraine and ice contact landforms are succeeding distinctly to northern hardwoods (95% and 88% respectively); those on outwash and other landforms show greater diversity of successional pathways.     

Modelling Habitat Suitability for Deciduous Forest Focal Species – A Sensitivity Analysis using Different Satellite Land Cover Data

We explored the usefulness of three satellite land cover data sets available to land managers in south-central Sweden for conservation planning using four deciduous forest focal resident bird species with different habitat requirements. Habitat suitability models using empirical species-specific habitat parameters and a Geographic Information System were applied to evaluate and compare the degree of consistency among three different land cover data sets. The study area encompassed 10,000 km² in a landscape mosaic of managed boreal forests and is within the distribution range of all four focal species. Although the three land cover data sets indicated similar total amounts of deciduous forest, the habitat suitability models showed that different land cover data yielded inconsistent results regarding the amount and distribution of suitable habitat within 5×5 km grid cells. Given this sensitivity to the choice of land cover data sets, the habitat suitability models showed positive relationships among the selected focal species for each land cover data set separately. As expected, decreasing amounts of suitable habitat were identified for species with higher specialisation. Thus, because habitat suitability models are an appropriate way to gain insight into the functionality and connectivity of habitat networks, land cover data must be carefully evaluated and if necessary combined with other landscape information for effective conservation planning.     

Accessibility as a determinant of landscape transformation in western Honduras: linking pattern and process

This study evaluates the relationship between landscape accessibility and land cover change in Western Honduras, and demonstrates how these relationships are influenced by social and economic processes of land use change in the region. The study area presents a complex mosaic of land cover change processes that involve approximately equal amounts of reforestation and deforestation. Landsat Thematic Mapper (TM) satellite imagery of 1987, 1991 and 1996 was used to create three single date classifications and a land cover change image depicting the sequence of changes in land cover between 1987–1991–1996. An accessibility analysis examined land cover change and landscape fragmentation relative to elevation and distance from roads. Between 1987 and 1991, results follow ‘expected’ trends, with more accessible areas experiencing greater deforestation and fragmentation. Between 1991 and 1996 this trend reverses. Increased deforestation is found in areas distant from roads, and at higher elevations; a result of government policies promoting expansion of mountain coffee production for export. A ban on logging, and abandonment of marginally productive agricultural fields due to agricultural intensification in other parts of the landscape, has led to increased regrowth in accessible regions of the landscape. Roads and elevation also present different obstacles in terms of their accessibility, with the smallest patches of cyclical clearing and regrowth, relating mostly to the agricultural fallow cycle, found at the highest elevations but located close to roads. This research highlights the need to locate analyses of land cover change within the context of local socio-economic policies and land use processes. This revised version was published online in August 2006 with corrections to the Cover Date.     

Effects of changing landscape pattern and U. S. G. S. land cover data variability on ecoregion discrimination across a forest-agriculture gradient

We examined the use of coarse resolution land cover data (USGS LUDA) to accurately discriminate ecoregions and landscape-scale features important to biodiversity monitoring and management. We used land cover composition and landscape indices, correlation and principal components analysis, and comparison with finer-grained Landsat TM data, to assess how well LUDA data discriminate changing patterns across an agriculture-forest gradient in Minnesota, U.S.A. We found LUDA data to be most accurate at general class levels of agriculture and forest dominance (Anderson Level I), but in consistent and limited in ecotonal areas of the gradient and within forested portions of the study region at finer classes (Anderson Level II). We expected LUDA to over-represent major (matrix) cover types and under-represent minor types, but this was not consistent with all classes. 1) Land cover types respond individualistically across the gradient, changing landscape grain as well as their spatial distribution and abundance. 2) Agriculture is not over-represented where it is the dominant land cover type, but forest is over-represented where it is dominant. 3) Individual forest types are under-represented in an open land matrix. 4) Within forested areas, mixed deciduous-coniferous forest is over-represented by several orders of magnitude and the separate conifer and hardwood types under-represented. Across gradual, transitional agriculture-forest areas, LUDA cover class dominance changes abruptly in a stair-step fashion. In general, rare cover types that are discrete, such as forest in agriculture or wetlands or water in forest, are more accurately represented than cover classes having lower contrast with the matrix. Northward across the gradient, important changes in the proportions of conifer and deciduous forest mixtures occur at scales not discriminated by LUDA data. Results suggest that finer-grained data are needed to map within-state ecoregions and discriminate important landscape characteristics. LUDA data, or similar coarse resolution data sources, should be used with caution and the biases fully understood before being applied in regional landscape management. This revised version was published online in July 2006 with corrections to the Cover Date.     

Landscape changes and ecological studies in agricultural regions,Québec,Canada

Most landscape definitions in the western world are based on soil, climatic, or physiographic features and do not integrate humans as an integral part of the landscape. We present an approach where landscape types have been delineated in southern Québec, Canada based on current land use where anthropogenic and agricultural activities are concentrated as a practical application of the holistic approach in landscape definition. Landsat-TM satellite images were classified and the 27 habitat classes were regrouped into 5 general land cover classes (cash crop, dairy farming, forest, anthropogenic, wetlands) and overlaid onto soil landscape polygons to characterize natural boundary units. Cluster analyses were used to aggregate these polygons into seven agricultural types of land scape forming a gradient from urban and high-intensity cash crop farming activities to landscapes dominated by a mosaic of agriculture and forested areas. Multivariate analyses of raw data and of socio-economic and farming practices variables were used to describe the defined types of landscape and these were projected over three established land classification systems of southern Québec (Canadian ecoregions, North American Bird Conservation Initiative regions and Corn Heat Unit regions) to compare their similarity in terms of land cover and for planning of future ecological studies. Because agricultural landscapes are highly dynamic, they are bound to undergo changes in the near future. Our landscape delineation may serve as an experimental setup where land scape dynamics and wildlife populations and community structures could be monitored. Because the information we used to delineate and characterize agricultural landscape types is readily available in other countries, our approach could easily be adapted to similar data sources under and a wide variety of landscape types. This revised version was published online in July 2006 with corrections to the Cover Date.     

Generating confidence intervals for composition-based landscape indexes

Many landscape indexes with ecological relevance have been proposed, including diversity indexes, dominance, fractal dimension, and patch size distribution. Classified land cover data in a geographic information system (GIS) are frequently used to calculate these indexes. However, a lack of methods for quantifying uncertainty in these measures makes it difficult to test hypothesized relations among landscape indexes and ecological processes. One source of uncertainty in landscape indexes is classification error in land cover data, which can be reported in the form of an error matrix. Some researchers have used error matrices to adjust extent estimates derived from classified land cover data. Because landscape diversity indexes depend only on landscape composition – the extent of each cover in a landscape – adjusted extent estimates may be used to calculate diversity indexes. We used a bootstrap procedure to extend this approach and generate confidence intervals for diversity indexes. Bootstrapping is a technique that allows one to estimate sample variability by resampling from the empirical probability distribution defined by a single sample. Using the empirical distribution defined by an error matrix, we generated a bootstrap sample of error matrixes. The sample of error matrixes was used to generate a sample of adjusted diversity indexes from which estimated confidence intervals for the diversity indexes were calculated. We also note that present methods for accuracy assessment are not sufficient for quantifying the uncertainty in landscape indexes that are sensitive to the size, shape, and spatial arrangement of patches. More information about the spatial structure of error is needed to calculate uncertainty for these indexes. Alternative approaches should be considered, including combining traditional accuracy assessments with other probability data generated during the classification procedure.     

A new approach for rescaling land cover data

The resolution of satellite imagery must often be increased or decreased to fill data gaps or match preexisting project requirements. It is well known that a change in resolution introduces systematic errors of size, shape, location and amount of contiguous land cover types. Nevertheless, robust methods for rescaling landscape data are frequently required to assess patterns of landscape change through time and over large areas. We developed a new method for rescaling spatial data that allows map resolution (grain size) to be either increased or decreased while holding the total proportion of land cover types constant. The method uses a weighted sampling net of variable resolution to sample an existing map and then randomly selects from the frequency of cover types derived from this sample to assign the cover type for the corresponding location in the rescaled map. The properties of the sampling net had a variable effect on measures of landscape pattern with the characteristic patch size (S) the most robust metric and the number of clusters (A) the most variable. A comparison of up-scaled and down-scaled maps showed that this process is not symmetrical, producing different errors for increases versus decreases in grain size. Rescaling Landsat (30 m) imagery to the 10 m resolution of SPOT imagery for four National Park units within Maryland and Virginia resulted in errors due to rescaling that were small (1–2%) relative to the total error (∼11%) associated with these images. The new rescaling method is general because it provides a single method for increasing or decreasing resolution, can be applied to maps with multiple land cover types, allows grid geometry to be transformed (i.e., square to hexagonal grids), and provide a more consistent basis for landscape comparisons when maps must be derived from multiple sources of classified imagery.     

Presettlement landscape heterogeneity: Evaluating grain of resolution using General Land Office Survey data

General Land Office Survey (GLOS) records from the A.D. 1840s provide data for quantitative characterization of presettlement vegetation across western Mackinac County, Michigan, located within the mixed conifer-northern hardwoods forest region. We analyzed data from land survey plat maps and 1958 bearing, witness, and line trees from 162 surveyed section and quarter-section corners in order to map vegatation cover types at a level of spatial resolution appropriate for characterizing landscape heterogeneity using standard landscape ecological metrics. As also demonstrated by a number of both classic and contemporary plant-ecological studies, the distribution of landforms, soils properties, hydrology, and location of fire breaks all contribute to the heterogeneity in vegetation observed at a landscape scale in the region. Through a series of spatial landscape analyses with differing grain of resolution, in this study we determine that a grid cell size of 65 ha (0.5 mi×0.5 mi or 0.25 mi²) to 259 ha (1 mi²) gives a conservative characterization of landscape heterogeneity using standard metrics and is therefore appropriate for use of GLOS data to study historical landscape changes.     

Impact of data integration technique on historical land-use/land-cover change: Comparing historical maps with remote sensing data in the Belgian Ardennes

Historical reconstructions of land-use/cover change often require comparing maps derived from different sources. The objective of this study was to measure land-use/cover changes over the last 225 years at the scale of a Belgian landscape, Lierneux in Ardennes, on the basis of a heterogeneous time series of land cover data. The comparability between the land-cover maps was increased following a method of data integration by map generalisation. Two types of time series were built by integrating the maps either by reference to the initial map of the time series or by pair of successive maps. Land-cover change detection was performed on the initial time series without data integration and on the two types of integrated time series. Results reveal that land cover and landscape structure have been subject to profound changes in Lierneux since 1775, with an annual rate of change at the landscape level of up to 1.40%. The major land-cover change processes observed are expansion of grasslands-croplands and reforestation with coniferous species, leading to amore fragmented landscape structure. The annual rates of land-cover change estimated from integrated data are significantly different from the annual rates of change estimated without a prior integration of the data. There is a trade-off between going as far back in time as possibleversus performing change detection as accurately as possible. This revised version was published online in July 2006 with corrections to the Cover Date.     

What multiscale environmental drivers can best be discriminated from a habitat index derived from a remotely sensed vegetation time series?

Understanding which environmental conditions are critical for species survival is a critical, ongoing question in ecology. These conditions can range from climate, at the broadest scale, through to elevation and other local landscape conditions, to fine scale landscape patterns of land cover and use. Remote sensing is an ideal technology to monitor and assess changes in these environmental conditions at a variety of spatial and temporal scales, with many studies focusing on the physiological state of vegetation derived from time series of satellite measurements. As vegetation occurs within specific climatic zones, over certain soil, terrain, and land cover types, it can be difficult to decipher the influence of the underlying role of climate, topography, soil, and land cover on the observed vegetation signal. In this article, we specifically addressed this problem by asking the question: what is the relative impact and importance of these different scales of environmental drivers on the temporal and spatial patterns observed on a habitat index derived from remotely sensed data? To find the solution, we utilized a SPOT VEGETATION-normalized difference vegetation index time series of Europe to create a remote-sensing-derived habitat index, which incorporates aspects of productivity, seasonality, and cover. We then compared the observed temporal and spatial variations in the index to a pan-Europe terrestrial classification system, which explicitly incorporates variations in climate, terrain, soil parent material, land cover, and use. Results indicated that the most accurate level of discrimination from the habitat index was at the broadest level of the hierarchy, climate, while the poorest degree of discrimination was associated with elevation. In terms of similarity on the index across time and space, we found that arable and forest cover classes were more similar across elevation and parent materials than across other land cover types within them. Analyzing the remote-sensing index, at multiple scales, provides significant insights into the drivers of satellite-derived greenness indices, as well as highlights the benefit and cautions associated with linking satellite-derived indirect indicators to species distribution modeling and biodiversity.     

Land use, land cover changes and coastal lagoon surface reduction associated with urban growth in northwest Mexico

Coastal land use and land cover changes, emphasizing the alterations of coastal lagoons, were assessed in northwest Mexico using satellite imagery processing. Supervised classifications of a Landsat series (1973–1997) and the coefficients Kappa (K) and Tau (τ), were used to assess the area and verify the accuracy of the classification of six informational classes (urban area, aquatic systems, mangrove, agriculture, natural vegetation, and aquaculture). Pixel-by-pixel change detection among dates was evaluated using the Kappa Index of Agreement (KIA). Besides the overall estimation of the aquatic systems class, variations in the three lagoons present in the study area were analyzed individually. Measures of agreement between the classification and reference data indicate that the accuracy for the classification ranked from moderate to high (K = 0.76 ± 0.07; τ = 0.77 ± 0.06). From 1973 to 1997 urban area has doubled, growing to the north and the northeast, extending mainly over natural vegetation and agricultural land. La Escopama and El Sabalo, two of the lagoons studied, reduced their size to less than half that estimated in 1973, but the main estuarine system in the study area, Estero de Urias - El Infiernillo, has maintained its area without noticeable changes. However, the surrounding landscape in Estero de Urias - Infiernillo is changing from natural vegetation and agriculture to urban land use. Consequently, to limit as much as possible changes in the area to natural causes, some management measures must be considered to design urban development plans and to recover and preserve the natural areas, on a broad scale rather than a local spatial scale. This revised version was published online in August 2006 with corrections to the Cover Date.     

Effects of changing spatial scale on the analysis of landscape pattern

The purpose of this study was to observe the effects of changing the grain (the first level of spatial resolution possible with a given data set) and extent (the total area of the study) of landscape data on observed spatial patterns and to identify some general rules for comparing measures obtained at different scales. Simple random maps, maps with contagion (i.e., clusters of the same land cover type), and actual landscape data from USGS land use (LUDA) data maps were used in the analyses. Landscape patterns were compared using indices measuring diversity (H), dominance (D) and contagion (C). Rare land cover types were lost as grain became coarser. This loss could be predicted analytically for random maps with two land cover types, and it was observed in actual landscapes as grain was increased experimentally. However, the rate of loss was influenced by the spatial pattern. Land cover types that were clumped disappeared slowly or were retained with increasing grain, whereas cover types that were dispersed were lost rapidly. The diversity index decreased linearly with increasing grain size, but dominance and contagion did not show a linear relationship. The indices D and C increased with increasing extent, but H exhibited a variable response. The indices were sensitive to the number (m) of cover types observed in the data set and the fraction of the landscape occupied by each cover type (P _k); both m and P _kvaried with grain and extent. Qualitative and quantitative changes in measurements across spatial scales will differ depending on how scale is defined. Characterizing the relationships between ecological measurements and the grain or extent of the data may make it possible to predict or correct for the loss of information with changes in spatial scale.     

Land Use – Land Cover Conversion, Regeneration and Degradation in the High Elevation Bolivian Andes

Regional land-cover change affects biodiversity, hydrology, and biogeochemical cycles at local, watershed, and landscape scales. Developing countries are experiencing rapid land cover change, but assessment is often restricted by limited financial resources, accessibility, and historical data. The assessment of regional land cover patterns is often the first step in developing conservation and management plans. This study used remotely sensed land cover and topographic data (Landsat and Shuttle Radar Topography Mission), supervised classification techniques, and spectral mixture analysis to characterize current landscape patterns and quantify land cover change from 1985 to 2003 in the Altiplano (2535–4671 m) and Intermediate Valley (Mountain) (1491–4623 m) physiographic zones in the Southeastern Bolivian Andes. Current land cover was mapped into six classes with an overall accuracy of 88% using traditional classification techniques and limited field data. The land cover change analysis showed that extensive deforestation, desertification, and agricultural expansion at a regional scale occurred in the last 20 years (17.3% of the Mountain Zone and 7.2% of the Altiplano). Spectral mixture analysis (SMA) indicated that communal rangeland degradation has also occurred, with increases in soil and non-photosynthetic vegetation fractions in most cover classes. SMA also identified local areas with intensive management activities that are changing differently from the overall region (e.g., localized areas of increased green vegetation). This indicates that actions of local communities, governments, and environmental managers can moderate the potentially severe future changes implied by the results of this study.     

Historical landscape ecology of an urbanized California valley: wetlands and woodlands in the Santa Clara Valley

Historical records provide information to land managers and landscape ecologists attempting to understand current trajectories in altered landscapes. In this study, we synthesized a heterogeneous array of historical sources to reconstruct historical land cover in California’s Santa Clara Valley (a.k.a. “Silicon Valley”). To increase and assess accuracy, we used the triangulation of overlapping, independent data sources and the application of certainty level standards. The region has been subject to extensive urbanization, so we also evaluated the applicability of historical landscape reconstructions to the altered landscape. We found evidence for five major land cover types prior to significant Euro–American modification. Valley freshwater marsh, wet meadow, alkali meadow, willow grove, and valley oak savanna have all experienced extreme decline (85–100%) since Euro–American settlement. However, comparison of historical land cover patterns to contemporary land use suggested several new strategies for environmental recovery, despite the limitations of surrounding urbanization. We also observed a temporal shift in riparian habitat along the mainstem of Coyote Creek, from a relatively open mixture of riparian scrub, sycamore woodland, and unvegetated gravel bars to dense riparian forest, likely resulting from stream flow regulation. By identifying former land cover patterns we provide a basis for evaluating local landscape change and setting restoration targets, including the identification of residual features and under-recognized land cover types. These findings suggest that reliable historical landscape reconstructions can be developed in the absence of standardized historical data sources and can be of value even in highly modified regions.     

Bush cover and range condition assessments in relation to landscape and grazing in southern Ethiopia

Progressive growth of bush cover in dry savannahs is responsible for declines in range conditions. In southern Ethiopia, the Booran pastoralists assisted our understanding of spatial patterns of bush cover and range conditions in 54 landscape patch types grouped into six landscape units within an area of 30000 km². The size of landscape patches sampled was 625 m². We assessed the relationships between bush cover, grass cover and bare soil and grazing pressure and soil erosion and changes in range condition. Externally, political conflicts and internally, break down of land use, and official bans on the use of fire promoted bush cover and the decline in range conditions. Bush cover was negatively correlated with grass cover, and positively correlated with bare soil. Grass cover was negatively correlated with bare soil and grazing pressure in most landscape patch types. Grazing pressure was not significantly correlated with bush cover or bare soil, while soil erosion was directly related to bare soil. Soil erosion was absent in 64% of the landscape patch types, and seemingly not a threat to the rangelands. The relationship between bush cover, grass cover, bare soil and soil erosion is complex and related to climate, landscape geology, and patterns of land use. Main threats to range conditions are bush climax, loss of grass cover and unpalatable forbs. Currently, >70% of the landscape patch types are in poor to fair range conditions. Decline in range conditions, unless reversed, will jeopardise the pastoral production system in southern Ethiopia.     

Mapping and analyzing landscape patterns

Landscapes were mapped as clusters of 2 or 3 land cover^** types, based on their pattern within the clusters and tendency for a single type to dominate. These landscapes, called Landscape Pattern Types (LPTs), were combined with other earth surface feature data in a Geographic Information System (GIS) to test their utility as analysis units. Road segment density increased significantly as residential and urbanized land cover components increased from absent, to present as patch, to present as matrix (i.e., the dominant land cover type). Stream segment density was significantly lower in LPTs with an urbanized or residential matrix than in LPTs with either a forest or agriculture matrix, suggesting an inverse relationship between stream network density and the prevalence of human development other than agriculture in the landscape. The ratio of average forest patch size to total forest in the LPT unit decreased as agriculture replaced forest, then increased as residential and urban components dominated. Wetland fractal dimension increased as agriculture and residential land cover components of LPTs increased. Comparison of LPT and LUDA land cover area statistics in ecoregions suggested that land cover data alone does not provide information as to its spatial arrangement.     

Rates and patterns of landscape change in the Central Sikhote-alin Mountains,Russian Far East

We used Landsat imagery and GIS to quantify the rates and patterns of landscape change between 1972 and 1992 for a 734,126 ha forested study area in the central Sikhote-alin Mountains of the Russian Far East. The study area includes a portion of the Sikhote-alinskiy Biosphere Reserve which is a part of the United Nations international Man and the Biosphere (MAB) reserve network. Wildfire is a major disturbance agent throughout the area and timber harvesting outside the reserve is also important. Maximum likelihood classification of the satellite imagery identified four broad cover types (hardwood, conifer, mixed and non-forest) in 1992 and changes among them between 1972 and 1992. We used multi-temporal principal components analysis to describe the magnitude and direction of landscape change for six watersheds that represent a range of ecological histories and disturbance regimes. Overall, forest cover declined from 90.4% in 1972 to 77.2% in 1992. The disturbance rate was more than twice as high in conifer than in hardwood forests. The rate of disturbance outside the reserve was three times that inside. While the rates of disturbance are not markedly higher than those recorded from other temperate forests, there has recently been a large alteration in the disturbance regime which will lead to a general transformation of forest composition and structure in the study area if the trend continues.     

Predicting carnivore occurrence with noninvasive surveys and occupancy modeling

Terrestrial carnivores typically have large home ranges and exist at low population densities, thus presenting challenges to wildlife researchers. We employed multiple, noninvasive survey methods—scat detection dogs, remote cameras, and hair snares—to collect detection–nondetection data for elusive American black bears (Ursus americanus), fishers (Martes pennanti), and bobcats (Lynx rufus) throughout the rugged Vermont landscape. We analyzed these data using occupancy modeling that explicitly incorporated detectability as well as habitat and landscape variables. For black bears, percentage of forested land within 5 km of survey sites was an important positive predictor of occupancy, and percentage of human developed land within 5 km was a negative predictor. Although the relationship was less clear for bobcats, occupancy appeared positively related to the percentage of both mixed forest and forested wetland habitat within 1 km of survey sites. The relationship between specific covariates and fisher occupancy was unclear, with no specific habitat or landscape variables directly related to occupancy. For all species, we used model averaging to predict occurrence across the study area. Receiver operating characteristic (ROC) analyses of our black bear and fisher models suggested that occupancy modeling efforts with data from noninvasive surveys could be useful for carnivore conservation and management, as they provide insights into habitat use at the regional and landscape scale without requiring capture or direct observation of study species.