Gary J. Roloff

Validation tests of a spatially explicit habitat effectiveness model for Rocky Mountain elk

We tested the validity of a spatially explicit habitat effectiveness model for Rocky Mountain elk (Cervus elaphus nelsoni). The model scored habitat effectiveness based on seasonal changes in the quality, quantity, and availability of forage. Seasonal forage potential scores were derived by integrating information on existing vegetation, site potential, historic disturbances, topography, and roads. The model generated maps of seasonal habitat effectiveness that were used to create utilization distributions (UD; i.e. 3-dimensional density estimates). We tested the elk habitat model using telemetry data collected on 5 cow elk sub-herds from 1993 to 1997 in Custer State Park (CSP), South Dakota, USA. We computed fixed kernel UD from elk telemetry data and simulated random UD within the confines of each sub-herd boundary. The degree of fit between elk UD and model predicted UD (elk-model UD) and random UD and model predicted UD (random-model UD) was represented by sub-herd, season, and year using the Volume of Intersection test statistic (V.I. Index). There were no differences in V.I. Indices by year for elk-model (1993 = 0.59, 1994 = 0.54, 1995 = 0.60, 1996 = 0.57, 1997 = 0.57; F 4,70 = 0.93, P = 0.45) or random-model (1993 = 0.59, 1994 = 0.55, 1995 = 0.59, 1996 = 0.58, 1997 = 0.59; F 4,70 = 1.49, P = 0.21) UD; thus, V.I. Indices were pooled across years. Two-way analysis of variance indicated that elk-model V.I. Indices did not differ by sub-herd (B = 0.50, Y = 0.58, A = 0.56, S = 0.58, R = 0.63; F 4,12 = 2.68, P = 0.08), season (Spring = 0.55, Summer = 0.55, Fall = 0.60, Winter = 0.58; F 3,12 = 0.80, P = 0.52), or the interaction terms (B Spring = 0.48, B Summer = 0.52, B Fall = 0.52, B Winter = 0.49, Y Spring = 0.60, Y Summer = 0.52, Y Fall = 0.58, Y Winter = 0.62, A Spring = 0.47, A Summer = 0.58, A Fall = 0.64, A Winter = 0.54, S Spring = 0.54, S Summer = 0.49, S Fall = 0.64, S Winter = 0.65, R Spring = 0.70, R Summer 0.64, R Fall = 0.60, R Winter = 0.59; F 12,55 = 1.68, P = 0.10). V.I. Indices for random-model UD did not differ by season (Spring = 0.58, Summer = 0.57, Fall = 0.58, Winter = 0.59; F 3,12 = 0.56, P = 0.65) or interaction term (B Spring = 0.55, B Summer = 0.58, B Fall = 0.56, B Winter = 0.54, Y Spring = 0.57, Y Summer = 0.53, Y Fall = 0.53, Y Winter = 0.57, A Spring = 0.61, A Summer = 0.63, A Fall = 0.63, A Winter = 0.64, S Spring = 0.60, S Summer = 0.49, S Fall = 0.57, S Winter = 0.59, R Spring = 0.59, R Summer 0.60, R Fall = 0.60, R Winter = 0.59; F 12,55 = 1.44, P = 0.17); however, differences were noted among sub-herds (B = 0.56, Y = 0.55, A = 0.63, S = 0.56, R = 0.60; F 4,12 = 4.48, P = 0.02). V.I. Indices for elk-model UD differed from random-model UD (F 4,12 = 4.71, P = 0.02); model performance was worse than random (i.e., lower V.I. Indices) for 2 sub-herds (elk-model sub-herd B = 0.50 vs. random-mode sub-herd B = 0.56 and elk-model sub-herd A = 0.56 vs. random-model sub-herd A = 0.63). Lower V.I. Indices were observed for 2 sub-herds that occupied areas recently subjected to large-scale wildfires. For sub-herds not subjected to fire effects (i.e., greater loss of vegetation security cover), the model portrayed elk habitat use less consistently, as represented by greater variability (27-42% larger standard errors) in V.I. Indices, during summer. Conversely, the model portrayed elk habitat use most consistent for the same 3 sub-herds during fall.

Poaching Risks in Community-Based Natural Resource Management

Poaching can disrupt wildlife-management efforts in community-based natural resource management systems. Monitoring, estimating, and acquiring data on poaching is difficult. We used local-stakeholder knowledge and poaching records to rank and map the risk of poaching incidents in 2 areas where natural resources are managed by community members in Caprivi, Namibia. We mapped local stakeholder perceptions of the risk of poaching, risk of wildlife damage to livelihoods, and wildlife distribution and compared these maps with spatially explicit records of poaching events. Recorded poaching events and stakeholder perceptions of where poaching occurred were not spatially correlated. However, the locations of documented poaching events were spatially correlated with areas that stakeholders perceived wildlife as a threat to their livelihoods. This result suggests poaching occurred in response to wildlife damage occurred. Local stakeholders thought that wildlife populations were at high risk of being poached and that poaching occurred where there was abundant wildlife. These findings suggest stakeholders were concerned about wildlife resources in their community and indicate a need for integrated and continued monitoring of poaching activities and further interventions at the wildlife-agricultural interface. Involving stakeholders in the assessment of poaching risks promotes their participation in local conservation efforts, a central tenet of community-based management. We considered stakeholders poaching informants, rather than suspects, and our technique was spatially explicit. Different strategies to reduce poaching are likely needed in different areas. For example, interventions that reduce human-wildlife conflict may be required in residential areas, and increased and targeted patrolling may be required in more remote areas. Stakeholder-generated maps of human-wildlife interactions may be a valuable enforcement and intervention support tool.

Implications of Ignoring Telemetry Error on Inference in Wildlife Resource use Models

ABSTRACT Global Positioning System (GPS) and very high frequency (VHF) telemetry data redefined the examination of wildlife resource use. Researchers collar animals, relocate those animals over time, and utilize the estimated locations to infer resource use and build predictive models. Precision of these estimated wildlife locations, however, influences the reliability of point-based models with accuracy depending on the interaction between mean telemetry error and how habitat characteristics are mapped (categorical raster resolution and patch size). Telemetry data often foster the assumption that locational error can be ignored without biasing study results. We evaluated the effects of mean telemetry error and categorical raster resolution on the correct characterization of patch use when locational error is ignored. We found that our ability to accurately attribute patch type to an estimated telemetry location improved nonlinearly as patch size increased and mean telemetry error decreased. Furthermore, the exact shape of these relationships was directly influenced by categorical raster resolution. Accuracy ranged from 100% (200-ha patch size, 1- to 5-m telemetry error) to 46% (0.5-ha patch size, 56- to 60-m telemetry error) for 10 m resolution rasters. Accuracy ranged from 99% (200-ha patch size, 1- to 5-m telemetry error) to 57% (0.5-ha patch size, 56- to 60-m telemetry error) for 30-m resolution rasters. When covariate rasters were less resolute (30 m vs. 10 m) estimates for the ignore technique were more accurate at smaller patch sizes. Hence, both fine resolution (10 m) covariate rasters and small patch sizes increased probability of patch misidentification. Our results help frame the scope of ecological inference made from point-based wildlife resource use models. For instance, to make ecological inferences with 90% accuracy at small patch sizes (≤5 ha) mean telemetry error ≤5 m is required for 10-m resolution categorical rasters. To achieve the same inference on 30-m resolution categorical rasters, mean telemetry error ≤10 m is required. We encourage wildlife professionals creating point-based models to assess whether reasonable estimates of resource use can be expected given their telemetry error, covariate raster resolution, and range of patch sizes.

Importance of visibility when evaluating animal response to roads

Roads increase risk to animals via direct and indirect mechanisms yet, both positive and negative effects of animal space use in relation to roads have been reported. These contrasting reports may not actually represent animal ecology, but could be a product of the primary variable used to test the relationship between animals and roads. Animal-road associations are often evaluated using Euclidean distance. Euclidean, or straight-line, distance fails to account for the screening effects of vegetation and topography and may document spurious relationships. We evaluated the influence of Euclidean distance, visibility from road and forage quality on summer space use for male elk Cervus elaphus and female elk subherds in Custer State Park, South Dakota, USA. Models that included interactions with visibility from road metrics outperformed models that included only Euclidean distance to road as main effects. Elk response to roads varied by sex and road type, which functioned as an index for vehicle use. Male elk selected habitat away from roads with the greatest vehicle use, an effect that was greater if habitat was visible from those roads. Female elk tended to select habitat with high forage quality in areas visible from roads closed to vehicle use. Interestingly, both male and female elk selected habitat away from roads with secondary vehicle use and near to roads devoid of traffic, regardless of visibility. Our analysis highlights the importance of including both Euclidean distance and visibility from road metrics. Road effects research may be incomplete without consideration of visibility from roads, particularly for large mammals in landscapes with intense road use.

Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise

Abstract Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error  =  174 m, SD  =  130 m) typical of field-based studies. We tested 5 techniques in 10 virtual environments and in one real-world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate-weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point-based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small-patch habitat relationships.

The influence of winter severity, predation and senescence on moose habitat use

Habitat use is widely known to be influenced by abiotic and biotic factors, such as climate, population density, foraging opportunity and predation risk. The influence of the life-history state of an individual organism on habitat use is less well understood, especially for terrestrial mammals. There is good reason to expect that life-history state would affect habitat use. For example, organisms exhibiting poor condition associated with senescence have an increased vulnerability to predation and that vulnerability is known to alter habitat use strategies. We assessed the influence of life-history stage on habitat use for 732 moose (Alces alces) killed by wolves (Canis lupus) over a 50-year period in Isle Royale National Park, an island ecosystem in Lake Superior, USA. We developed regression models to assess how location of death was associated with a mooses life-history stage (prime-aged or senescent), presence or absence of senescent-associated pathology (osteoarthritis and jaw necrosis), and annual variation in winter severity, moose density and ratio of moose to wolves, which is an index of predation risk. Compared to senescent moose, prime-aged moose tend to make greater use of habitat farther from the shoreline of Isle Royale. That result is ecologically relevant because shoreline habitat on Isle Royale tends to provide better foraging opportunities for moose but is also associated with increased predation risk. During severe winters prime-aged moose tend to make greater use of habitat that is closer to shore in relation to senescent-aged moose. Furthermore, moose of both age classes were more likely to die in riskier, shoreline habitat during years when predation risk was lower in the preceding year. Our results highlight a complicated connection between life history, age-structured population dynamics and habitat-related behaviour. Our analysis also illustrates why intraspecific competition should not be the presumed mechanism underlying density-dependent habitat use, if predation risk is related to density, as it is expected to be in many systems.

A synthesis of giant panda habitat selection

Abstract The giant panda (Ailuropoda melanoleuca) is a global conservation icon, but its habitat selection patterns are poorly understood. We synthesized previous studies on giant panda habitat selection. We confirmed that pandas generally selected forests with moderate to high bamboo densities, mid-elevations, both primary and secondary forests, and areas more distant from human activities. Pandas did not select steep slopes. We also highlighted the interactive effects among different habitat components, such as weaker selection for gentle slope and large patch size in disturbed secondary forests compared with primary forests. Pandas selected for land cover and disturbance at the level of the geographic range and selected for variables such as slope and bamboo density at the level of the home range. Furthermore, selection for higher bamboo cover did not change with bamboo availability, but selection against secondary forest declined as availability of this forest type increased. Our results have implications for the conservation of pandas, particularly the need for inclusion of areas previously seen as less suitable (e.g., moderate slopes and secondary forest) in protected area and habitat restoration planning.

Using multiple methods to assess detection probabilities of forest-floor wildlife†

ABSTRACT Many previous comparisons of multiple sampling methods have assumed that detection probabilities for each method are either constant or equal to one. We used 4 sampling methods to estimate detection probabilities for forest-floor dwelling amphibians, reptiles, and small mammals. We investigated associations between seasonality and precipitation on species detection and explored sample design tradeoffs for future studies. Although we captured 25 species, we could reliably detect (detection probability >0.15) only northern short-tailed shrews (Blarina brevicauda) and pygmy and masked shrews (Sorex spp.) using drift fences and red-backed salamanders (Plethodon cinereus) using visual encounter surveys (VES). The use of multiple sampling methods improved detection probabilities for only red-backed salamanders ( VES = 0.32, 95% CI: 0.24–0.38, allmethods = 0.38, 95% CI: 0.32–0.44). Parameter estimates indicated detection of both shrew species was positively related to increased precipitation. Detection probabilities for pygmy and masked shrews and red-backed salamanders were positively and negatively associated with date, respectively. Our power analysis revealed that sampling during rain events increased the power of detecting a change in sorid occupancy by ≥40% (&agr; = 0.05). Our results demonstrate the need to incorporate species detectability when comparing the effectiveness of different trapping methodologies. Furthermore, our study highlights the utility of power analyses for exploring study design tradeoffs for research and monitoring programs.

INVESTIGATING IMPACTS OF LAND-USE LAND COVER CHANGE ON WETLANDS IN THE MUSKEGON RIVER WATERSHED, MICHIGAN, USA

Estimates indicate that nearly half of the world’s wetlands have been destroyed or altered as a result of human activities. However, studies investigating the impacts of land-use land cover change on wetlands have shown varied results. Furthermore, when assessing wetlands at the site level, landscapescale wetland health conditions can go largely unnoticed. The objective of this paper was to investigate wetland quality changes resulting from land-use land cover alterations at a watershed scale. Landscapepattern metrics were generated to examine changes in wetlands characteristics between 1978 and 2000 in the Muskegon River Watershed, Michigan, USA. Metrics quantifying composition, configuration, and fractal indication were generated and correlated with land-use land cover patterns and alterations. Results show ranging wetland stress with the mid-river portion of the watershed undergoing the highest fragmentation indicated by wetland patch dynamics. Landscape Shape Index, Fractal Dimension Index, and Interspersion and Juxtaposition Index metrics reflect similar spatial trends of decreasing wetlands quality. Agriculture and urban land-use composition had weak to moderate strength correlations with the Interspersion and Juxtaposition Index, respectively. The method was useful for identifying the spatial variability of wetland changes across watersheds and possible regions to focus management and monitoring efforts.

Where wolves kill moose: the influence of prey life history dynamics on the landscape ecology of predation.

The landscape ecology of predation is well studied and known to be influenced by habitat heterogeneity. Little attention has been given to how the influence of habitat heterogeneity on the landscape ecology of predation might be modulated by life history dynamics of prey in mammalian systems. We demonstrate how life history dynamics of moose (Alces alces) contribute to landscape patterns in predation by wolves (Canis lupus) in Isle Royale National Park, Lake Superior, USA. We use pattern analysis and kernel density estimates of moose kill sites to demonstrate that moose in senescent condition and moose in prime condition tend to be wolf-killed in different regions of Isle Royale in winter. Predation on senescent moose was clustered in one kill zone in the northeast portion of the island, whereas predation on prime moose was clustered in 13 separate kill zones distributed throughout the full extent of the island. Moreover, the probability of kill occurrence for senescent moose, in comparison to prime moose, increased in high elevation habitat with patches of dense coniferous trees. These differences can be attributed, at least in part, to senescent moose being more vulnerable to predation and making different risk-sensitive habitat decisions than prime moose. Landscape patterns emerging from prey life history dynamics and habitat heterogeneity have been observed in the predation ecology of fish and insects, but this is the first mammalian system for which such observations have been made.