Timothy R Johnson

In natural resource management, reliable monitoring data is necessary to make informed management decisions. Multiple observers collecting data for a monitoring program can introduce variance due to between-observer differences which reduces data reliability. We introduce an application of mixed-effects models with relaxed homogeneous variance assumptions (called heterogeneous-variance mixed-effects models) to quantify between-observer variance in natural resource monitoring programs with large spatial and temporal extents. We used monitoring data from the Bureau of Land Management's Assessment, Inventory, and Monitoring program to describe and demonstrate the use of the method. In two example analyses, we identified differences in observer variance across regions and years and associated the differences in these examples with features of the AIM monitoring program. These examples illustrate several potential uses of the models which include calculating the magnitude of observer variance to guide indicator selection and appropriate indicator estimate calculations, determining appropriate data aggregation and comparison, understanding the influences of changes to a monitoring program on observer variance, identifying previously unknown influences on observer variance, and suggesting practices or further changes to monitoring programs to reduce observer variance in future data collection efforts. This statistical technique requires a dataset with a large number of samples and observers and is therefore best suited to large monitoring programs. Reliable metadata about observers and the program history is also required to build the model and interpret the results. The model could also be expanded to answer additional questions about the influence of observers on monitoring data.
•Observer variance can reduce the quality of natural resource monitoring data.•We propose heterogeneous-variance mixed-effects models to quantify this variance.•An important model application is identifying sources of observer variance.•These results can help direct variance reduction strategies in monitoring programs.•The models can also guide appropriate data use and parameter estimation.

Animals at risk of predation select habitat that enhances security from predators. Two properties of cover related to security are concealment (i.e., habitat structure that blocks an individual from detection by others) and visibility (i.e., visual information accessible relative to habitat structure). Although these properties are often negatively correlated, they are not always inverse; animals in habitat with heterogeneous structure may be able to select for both. We investigated habitat use by pygmy rabbits (Brachylagus idahoensis) at 2 scales (patch and microsite) to evaluate the influence of both structural properties of cover and visual properties (concealment and visibility) on habitat use by prey. We contrasted vegetation structure at paired used and unused patches. At each patch, we measured concealment and viewshed (i.e., visibility) in 3 orientations (i.e., aerial, terrestrial, and overall) and structural density using lidar. We also measured heights of the 3 tallest shrubs. Additionally, within used patches, we assessed the density of fecal pellets as an index of intensity of use and also measured distance to nearest burrow. At the patch scale, rabbits selected for structural properties of cover (dense vegetation and tall shrubs), but not visual properties of cover. Pygmy rabbits more intensively used microsites associated with high terrestrial concealment and in proximity to burrows. Our results suggest that pygmy rabbits may perceive greater threat from terrestrial as opposed to aerial predators at both scales, and they also indicate a nuanced relationship between properties of cover and habitat use.

Objective: The potential influence (i.e., impact rate) of catch-and-release fisheries on wild steelhead Oncorhynchus mykiss is poorly understood and is a function of the abundance of wild fish, how many fish are encountered by anglers (i.e., encounter rate), and the mortality of fish that are caught and released. In Idaho, estimates of wild steelhead encounter rates have been derived using the number of wild and hatchery steelhead passing Lower Granite Dam, the number of hatchery steelhead harvested, and the number of hatchery steelhead caught and released. The method includes assumptions that hatchery and wild steelhead have equal encounter rates and catch-and-release mortality is 5% for wild steelhead. Here, we investigated wild and hatchery steelhead encounter rates by anglers, estimated catch-and-release mortality, and concatenated both aspects to examine how existing recreational steelhead fisheries influence wild steelhead mortality.Methods: We sampled, tagged, and released 1,251 spawn-year 2020 (SY2020) and 1,956 spawn-year 2021 (SY2021) adult steelhead at Lower Granite Dam with T-bar anchor tags and passive integrated transponder (PIT) tags to estimate steelhead encounter rates and catch-and-release mortality. Differences in survival of caught steelhead and those not reported as caught were evaluated using detections at various locations (e.g., PIT arrays, weirs).Result: Estimated encounter rates were 43.7% (95% credible interval; 28.2%, 100.0%) for wild fish and 46.7% (29.6%, 100.0%) for adipose-clipped fish in SY2020. In SY2021, encounter rates were 47.2% (32.4%, 100.0%) for wild fish and 52.3% (37.1%, 100.0%) for adipose-clipped fish. Based on detections of caught fish and those not reported as caught, catch-and-release mortality of wild steelhead was estimated to be 1.6% (0.0%, 5.2%). Wild steelhead impact rates were 0.7% (0.0%, 2.7%) in SY2020 and 0.7% (0.0%, 2.8%) in SY2021.Conclusion: Estimated rates of impact on wild steelhead were consistent and low across years despite major differences in the structure of the fisheries. Our results suggest assuming that encounter rates are equal between hatchery and wild steelhead, and that steelhead catch-and-release mortality is 5%, will likely lead to a conservative estimate of the wild steelhead impact occurring from catch-and-release fisheries.

Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear. In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.