Tammy L. Wilson

Deriving Snow Cover Metrics for Alaska from MODIS

Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates for Alaska, portions of western Canada and the Russian Far East derived from Terra MODIS snow cover daily 500 m grid data (MOD10A1) and evaluates our method for filling data gaps caused by clouds or polar darkness. Pixels classified as cloud or no data were reclassified by: spatial filtering using neighboring pixel values; temporal filtering using pixel values for days before/after cloud cover; and snow-cycle filtering based on a time series assessment of a pixel’s position within snow accumulation, cover or melt periods. During the 2012 snow year, these gap-filling methods reduced cloud pixels from 27.7% to 3.1%. A total of 12 metrics (e.g., date of first and last snow, date of persistent snow cover and periods of intermittence) for each pixel were calculated by snow year. A comparison of MODIS-derived snow onset and melt dates with in situ observations from 244 weather stations generally showed an early bias in MODIS-derived dates and an effect of increasing cloudiness exacerbating bias. Our results show that mean regional duration of seasonal snow cover is 179–311 days/year and that snow cover is often intermittent, with 41% of the area experiencing ≥2 snow-covered periods during a snow season. Other regional-scale patterns in the timing of snow onset and melt are evident in the yearly 500 m gridded products publically available at http://static.gina.alaska.edu/NPS_products/MODIS_snow/.

Effects of Sagebrush Treatments on Multi-Scale Resource Selection by Pygmy Rabbits

ABSTRACT The effects of widespread sagebrush removal treatments on pygmy rabbits (Brachylagus idahoensis) are not well understood. Due to reliance on sagebrush, pygmy rabbits are among the species for which these treatments may be detrimental. Our objectives were to evaluate the effects of experimental sagebrush treatment on 8 radio-collared pygmy rabbits between and within home range habitat selection using Monte Carlo simulation from null models. Pygmy rabbits were not extirpated from plots containing habitat treatments, and we found no evidence that treatments affected home range placement. The mean treatment distance of observed home range centers did not differ from repeated trials of random points. However, we found evidence of within home range selection against treatments from 2 of 8 rabbits located close to the treatments. The mean treatment distance of all observed locations for these 2 rabbits was greater than expected based on a null model. We also used snow tracking to show that pygmy rabbits entered treatments in 4 out of 21 trials, which was less often than expected by chance (G 2 = 8.662, P = 0.003). Conservatively, sagebrush removal treatments should not be conducted on active or recently active pygmy rabbit burrows. Elsewhere near known pygmy rabbit sites, treated patches should be small and connected by untreated corridors to prevent potentially limiting movement of rabbits among the untreated habitat.

Spatial ecology of refuge selection by an herbivore under risk of predation

Prey species use structures such as burrows to minimize predation risk. The spatial arrangement of these resources can have important implications for individual and population fitness. For example, there is evidence that clustered resources can benefit individuals by reducing predation risk and increasing foraging opportunity concurrently, which leads to higher population density. However, the scale of clustering that is important in these processes has been ignored during theoretical and empirical development of resource models. Ecological understanding of refuge exploitation by prey can be improved by spatial analysis of refuge use and availability that incorporates the effect of scale. We measured the spatial distribution of pygmy rabbit (Brachylagus idahoensis) refugia (burrows) through censuses in four 6-ha sites. Point pattern analyses were used to evaluate burrow selection by comparing the spatial distribution of used and available burrows. The presence of food resources and additional overstory cover resources was further examined using logistic regression. Burrows were spatially clustered at scales up to approximately 25 m, and then regularly spaced at distances beyond ∼40 m. Pygmy rabbit exploitation of burrows did not match availability. Burrows used by pygmy rabbits were likely to be located in areas with high overall burrow density (resource clusters) and high overstory cover, which together minimized predation risk. However, in some cases we observed an interaction between either overstory cover (safety) or understory cover (forage) and burrow density. The interactions show that pygmy rabbits will use burrows in areas with low relative burrow density (high relative predation risk) if understory food resources are high. This points to a potential trade-off whereby rabbits must sacrifice some safety afforded by additional nearby burrows to obtain ample forage resources. Observed patterns of clustered burrows and non-random burrow use improve understanding of the importance of spatial distribution of refugia for burrowing herbivores. The analyses used allowed for the estimation of the spatial scale where subtle trade-offs between predation avoidance and foraging opportunity are likely to occur in a natural system.

Scale dependence in occupancy models: implications for estimating bear den distribution and abundance

Monitoring programs are typically designed to identify long-term trends in animal abundance, however estimating abundance at a relevant scale can be logistically prohibitive. This is particularly true for species that occur at low densities or those with large home ranges. In such cases, occupancy surveys are often employed in place of more expensive abundance estimation techniques such as mark-recapture because precise estimation of occupancy probability generally requires fewer data. Although choice of plot size is a critical design element of occupancy monitoring, relatively little effort has been expended to develop or test plot size recommendations. Animal movement between surveys can complicate efforts to obtain an optimal plot size, but surveys of fixed objects, such as nests, dens, or burrows can provide insight about scale effects because the population exposed to sampling does not change during the duration of the survey. We used repeated aerial occupancy surveys to obtain estimates of brown bear (Ursus arctos) den distribution and abundance in a portion of Katmai National Park and Preserve in Alaska. We then used these data to assess the importance of plot size selection and highlight the effects of spatial grain on the resulting inference and utility for monitoring. Scale effects in estimates of mean den-based site occupancy, but not total den abundance demonstrated that careful selection of sample unit size is important if estimating occupancy probability is a primary monitoring objective. We expect occupancy surveys based on important structures such as nests or dens could have wide applicability for many species.

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity

Abstract Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.

Nest use dynamics of an undisturbed population of bald eagles

Abstract Management or conservation targets based on demographic rates should be evaluated within the context of expected population dynamics of the species of interest. Wild populations can experience stable, cyclical, or complex dynamics, therefore undisturbed populations can provide background needed to evaluate programmatic success. Many raptor species have recovered from large declines caused by environmental contaminants, making them strong candidates for ongoing efforts to understand population dynamics and ecosystem processes in response to human‐caused stressors. Dynamic multistate occupancy models are a useful tool for analyzing species dynamics because they leverage the autocorrelation inherent in long‐term monitoring datasets to obtain useful information about the dynamic properties of population or reproductive states. We analyzed a 23‐year bald eagle monitoring dataset in a dynamic multistate occupancy modeling framework to assess long‐term nest occupancy and reproduction in Lake Clark National Park and Preserve, Alaska. We also used a hierarchical generalized linear model to understand changes in nest productivity in relation to environmental factors. Nests were most likely to remain in the same nesting state between years. Most notably, successful nests were likely to remain in use (either occupied or successful) and had a very low probability of transitioning to an unoccupied state in the following year. There was no apparent trend in the proportion of nests used by eagles through time, and the probability that nests transitioned into or out of the successful state was not influenced by temperature or salmon availability. Productivity was constant over the course of the study, although warm April minimum temperatures were associated with increased chick production. Overall our results demonstrate the expected nesting dynamics of a healthy bald eagle population that is largely free of human disturbance and can be used as a baseline for the expected dynamics for recovering bald eagle populations in the contiguous 48 states.