Hierarchical Bayesian Spatial Models for Multispecies Conservation Planning and Monitoring

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data’s spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church’s sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence–absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatialmodels outperformed analogousmodels developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatialmodels built from presence–absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km2 hexagons), can increase the relevance of habitat models to multispecies conservation planning

Place-Based Learning Communities on a Rural Campus: Turning Challenges into Assets

At Humboldt State University (HSU), location is everything. Students are as drawn to our spectacular natural setting as they are to the unique majors in the natural resource sciences that the university has to offer. However, the isolation that nurtures the pristine natural beauty of the area presents a difficult reality for students who are accustomed to more densely populated environments. With the large majority of our incoming students coming from distant cities, we set out to cultivate a “home away from home” by connecting first-year students majoring in science, technology, engineering and math (STEM) to the communities and local environment of Humboldt County. To achieve this, we designed first-year place-based learning communities (PBLCs) that integrate unique aspects and interdisciplinary themes of our location throughout multiple high impact practices, including a summer experience, blocked-enrolled courses, and a first-year experience course entitled Science 100: Becoming a STEM Professional in the 21st Century. Native American culture, traditional ways of knowing, and contemporary issues faced by tribal communities are central features of our place-based curriculum because HSU is located on the ancestral land of the Wiyot people and the university services nine federally recognized American Indian tribes. Our intention is that by providing a cross-cultural, validating environment, students will: feel and be better supported in their academic pursuits; cultivate values of personal, professional and social responsibility; and increase the likelihood that they will complete their HSU degree. As we complete the fourth year of implementation, we aim to harness our experience and reflection to improve our programming and enable promising early results to be sustained

Appendix E. Mesoscale predictive models rankings based on Akaike's Information Criterion for models with Akaike weights > 0.05.

Mesoscale predictive models rankings based on Akaike's Information Criterion for models with Akaike weights > 0.05

Appendix B. A table showing means (±1 SD) of micro scale variables that entered at least one of the "best" models for any of the five mollusk species with an Akaike weight > 0.05.

A table showing means (±1 SD) of micro scale variables that entered at least one of the "best" models for any of the five mollusk species with an Akaike weight > 0.05

Appendix A. A table of candidate explanatory variables used to model mollusk presence–absence in northern California National Forests, 1999–2000.

A table of candidate explanatory variables used to model mollusk presence–absence in northern California National Forests, 1999–2000

Appendix D. A table showing means (±1 SD) of mesoscale variables at the scale for which we found the best model for each mollusk species.

A table showing means (±1 SD) of mesoscale variables at the scale for which we found the best model for each mollusk species

Modeling spatial variation in density of golden eagle nest sites in the western United States.

In order to contribute to conservation planning efforts for golden eagles (Aquila chrysaetos) in the western U.S., we developed nest site models using >6,500 nest site locations throughout a >3,483,000 km2 area of the western U.S. We developed models for twelve discrete modeling regions, and estimated relative density of nest sites for each region. Cross-validation showed that, in general, models accurately estimated relative nest site densities within regions and sub-regions. Areas estimated to have the highest densities of breeding golden eagles had from 132-2,660 times greater densities compared to the lowest density areas. Observed nest site densities were very similar to those reported from published studies. Large extents of each modeling region consisted of low predicted nest site density, while a small percentage of each modeling region contained disproportionately high nest site density. For example, we estimated that areas with relative nest density values 0.5 represented from 1.0-12.8% ([Formula: see text] = 6.3%) of modeling areas, and those areas contained from 47.7-66.9% ([Formula: see text] = 57.3%) of the nest sites. Our findings have direct application to: 1) large-scale conservation planning efforts, 2) risk analyses for land-use proposals such as recreational trails or wind power development, and 3) identifying mitigation areas to offset the impacts of human disturbance