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

When to be discrete: the importance of time formulation in understanding animal movement

Animal movement is essential to our understanding of population dynamics, animal behavior, and the impacts of global change. Coupled with high-resolution biotelemetry data, exciting new inferences about animal movement have been facilitated by various specifications of contemporary models. These approaches differ, but most share common themes. One key distinction is whether the underlying movement process is conceptualized in discrete or continuous time. This is perhaps the greatest source of confusion among practitioners, both in terms of implementation and biological interpretation. In general, animal movement occurs in continuous time but we observe it at fixed discrete-time intervals. Thus, continuous time is conceptually and theoretically appealing, but in practice it is perhaps more intuitive to interpret movement in discrete intervals. With an emphasis on state-space models, we explore the differences and similarities between continuous and discrete versions of mechanistic movement models, establish some common terminology, and indicate under which circumstances one form might be preferred over another. Counter to the overly simplistic view that discrete- and continuous-time conceptualizations are merely different means to the same end, we present novel mathematical results revealing hitherto unappreciated consequences of model formulation on inferences about animal movement. Notably, the speed and direction of movement are intrinsically linked in current continuous-time random walk formulations, and this can have important implications when interpreting animal behavior. We illustrate these concepts in the context of state-space models with multiple movement behavior states using northern fur seal (Callorhinus ursinus) biotelemetry data.Fil: McClintock, Brett T.. National Marine Mammal Laboratory; Estados UnidosFil: Johnson, Devin S.. National Marine Mammal Laboratory; Estados UnidosFil: Hooten, Mevin B.. State University Of Colorado - Fort Collins; Estados UnidosFil: Ver Hoef, Jay M.. National Marine Mammal Laboratory; Estados UnidosFil: Morales, Juan Manuel. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentin

Effect of Localized Vibration Using Massage Gun at 40hz and 50hz on Blood Flow

Data has shown that whole body vibration can positively affect blood flow, however, there are very few studies on the effect of localized therapeutic vibration on arterial blood flow. Occupational studies looking at localized vibration effects on skin blood flow normally include high frequency settings. In the last few years, massage guns have become popular, but they operate at lower frequencies. Currently, there is no data on the effects of localized vibration from massage guns on arterial blood flow. PURPOSE: To compare the effects of two different frequencies of localized vibration on blood flow in the popliteal artery. METHODS: 12 subjects participated in this study (8 males and 4 females). Mean age was 22.7±1.6 years; mean height was 181.1±11.8 cm; mean weight was 78.2±16.2 kg. Participants wore shorts to give access to the popliteal artery. Participants were hooked to ECG leads to control measurement of artery diameter and then laid on a treatment table in a prone position with a foam roller under their ankles. Once at resting heart rate, baseline blood flow readings were taken using ultrasound, which measured TA Mean and Volume Flow. The participants were then randomly given a 5-minute treatment of control with no vibration or vibration at 40hz or 50hz. Blood flow readings were taken immediately post-treatment and then every minute for 5 minutes after. RESULTS: A two-factor repeated measures analysis was performed. Each subject was measured under all levels of condition (Control 5 min, 40hz 5 min, and 50hz 5 min) and time (baseline, post, post1-5). TA Mean and Volume Flow for both 40hz and 50hz were significantly greater than control (p=0.0020 and p=0.0110 respectively). The effect of time was significant (

Bayesian Multimodel Inference for Geostatistical Regression Models

The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance