Extending the Latent Multinomial Model with Complex Error Processes and Dynamic Markov Bases

The latent multinomial model (LMM) model of Link et al. (2010) provided a general framework for modelling mark-recapture data with potential errors in identification. Key to this approach was a Markov chain Monte Carlo (MCMC) scheme for sampling possible configurations of the counts true capture histories that could have generated the observed data. This MCMC algorithm used vectors from a basis for the kernel of the linear map between the true and observed counts to move between the possible configurations of the true data. Schofield and Bonner (2015) showed that a strict basis was sufficient for some models of the errors, including the model presented by Link et al. (2010), but a larger set called a Markov basis may be required for more complex models. We address two further challenges with this approach: 1) that models with more complex error mechanisms do not fit easily within the LMM and 2) that the Markov basis can be difficult or impossible to compute for even moderate sized studies. We address these issues by extending the LMM to separately model the capture/demographic process and the error process and by developing a new MCMC sampling scheme using dynamic Markov bases. Our work is motivated by a study of Queen snakes (Regina septemvittata) in Kentucky, USA, and we use simulation to compare the use of PIT tags, with perfect identification, and brands, which are prone to error, when estimating survival rates

Invasion dynamics of the European Collared-Dove in North America are explained by combined effects of habitat and climate

Global biodiversity is increasingly threatened by the spread of invasive species. Understanding the mechanisms influencing the initial colonization and persistence of invaders is therefore needed if conservation actions are to prevent new invasions or strive to slow their spread. The Eurasian Collared-Dove (Streptopelia decaocto, EUCO) is one of the most successful avian invasive species in North America; however, to our knowledge, no study has simultaneously examined the role that climate-matching, human activity, directional propagation, and local density have in this invasion process. Our research expands upon a cellular-automata-based hierarchical model developed to assess directional invasion dynamics to further quantify the impacts of climate, elevation, and land cover type on the spread of EUCO in North America. Our results suggest that EUCO’s dispersal patterns can largely be explained by the effects of habitat, climate, and environmental conditions at different stages of the invasion process rather than some innate preferred north-westerly spread. Specifically, EUCO initially colonized warm and wet grassland habitats and tended to persist in urban areas. We also found that while EUCO were more likely to spread to the northeast of existing habitats, directional preference did not drive persistence and recolonization events. These findings highlight the importance of incorporating both neighborhood effects and environmental factors in the modelling of range-expanding species, adding to the toolset available to researchers to model invasive species spread. Further, our research demonstrates that historical records of invasive species occurrences can provide the data resources needed to disentangle the characteristics driving species invasion and enable predictions that are of critical importance to resource managers

dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble

Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many biological studies seek to understand non-deterministic patterns in the variance or dispersion of a phenotypic or ecological response variable. We describe a new R package, dalmatian, that provides methods for fitting double hierarchical generalized linear models incorporating fixed and random predictors of both the mean and variance. Models are fit via Markov chain Monte Carlo sampling implemented in either JAGS or nimble and the package provides simple functions for monitoring the sampler and summarizing the results. We illustrate these functions through an application to data on food delivery by breeding pied flycatchers (Ficedula hypoleuca). Our intent is that this package makes it easier for practitioners to implement these models without having to learn the intricacies of Markov chain Monte Carlo methods

Occupancy and Abundance of Stream Salamanders along a Specific Conductance Gradient

In the Central Appalachians (USA), mountaintop-removal mining accompanied by valley fills often leads to streams with elevated specific conductivity (SC). Thus, the ionic composition of freshwaters in this region is hypothesized to be a driver of the distribution and abundance of freshwater taxa, including stream salamanders. We examined the association between SC and stream salamander populations by conducting salamander counts in 30 southeastern Kentucky streams across a continuous gradient of SC that ranged from 30 to 1966 μS/cm. We counted 2319 salamanders across 5 species and, using a hierarchical Bayesian version of the N-mixture model, found a negative association between SC and salamander occupancy rates. This finding was consistent across adults and larvae of the 5 species we examined. Furthermore, we found that most salamander species and life stages showed reduced abundances given occupancy at greater SC levels. For example, estimated mean abundance given occupancy of larval Southern Two-lined Salamanders (Eurycea cirrigera) was 67.69 (95% credible interval 48.31–98.25) ind/10 m at 250 μS/cm and 2.30 (95% credible interval 1.46–3.93) ind/10 m at 2000 μS/cm. The consistent negative association across all species and life stages supports the hypothesis that salamander distributions and abundances are negatively associated with elevated SC of streams in southeastern Kentucky, even though physical and chemical environmental attributes, such as forest cover within stream catchments, were correlated with SC. Restoration of streams affected by mountaintop-removal mining should focus on restoring the ionic compositions that naturally occur in this region

On the identifiability of the trinomial model for mark‐recapture‐recovery studies

Continuous predictors of survival present a challenge in the analysis of data from studies of marked individuals if they vary over time and can only be observed when individuals are captured. Existing methods to study the effects of such variables have followed one of two approaches. The first is to model the joint distribution of the predictor and the observed capture histories, and the second is to draw inference from the likelihood conditional on events that depend only on observed predictor values, called the trinomial model. Previous comparison of these approaches found that joint modelling provided more precise inference about the effect of the covariate while the trinomial model was less prone to issues of model mis-specification. However, we believe that an important issue was missed. We show through mathematical analysis and numerical simulation that the trinomial model is not identifiable when the predictor has no effect on the survival probability. This also causes inferences from the trinomial model to be imprecise when the effect of the covariate on the survival probability is small. We also analyse data on the effect of body mass on the survival of meadow voles to demonstrate the importance of this issue in real applications

Aboriginal Status is a Prognostic Factor for Mortality among Antiretroviral Naive HIV-Positive Individuals First Initiating HAART

Background: Although the impact of Aboriginal status on HIV incidence, HIV disease progression, and accessto treatment has been investigated previously, little is known about the relationship between Aboriginal ethnicityand outcomes associated with highly active antiretroviral therapy (HAART). We undertook the present analysisto determine if Aboriginal and non-Aboriginal persons respond differently to HAART by measuring HIV plasmaviral load response, CD4 cell response and time to all-cause mortality.Methods: A population-based analysis of a cohort of antiretroviral therapy naïve HIV-positive Aboriginal menand women 18 years or older in British Columbia, Canada. Participants were antiretroviral therapy naïve, initiatedtriple combination therapy between August 1, 1996 and September 30, 1999. Participants had to complete abaseline questionnaire as well as have at least two follow-up CD4 and HIV plasma viral load measures. Theprimary endpoints were CD4 and HIV plasma viral load response and all cause mortality. Cox proportionalhazards models were used to determine the association between Aboriginal status and CD4 cell response, HIVplasma viral load response and all-cause mortality while controlling for several confounder variables.Results: A total of 622 participants met the study criteria. Aboriginal status was significantly associated with noAIDS diagnosis at baseline (p = 0.0296), having protease inhibitor in the first therapy (p = 0.0209), lower baselineHIV plasma viral load (p < 0.001), less experienced HIV physicians (P = 0.0133), history of IDU (p < 0.001), notcompleting high school (p = 0.0046), and an income of less than $10,000 per year (p = 0.0115). Cox proportionalhazards models controlling for clinical characteristics found that Aboriginal status had an increased hazard ofmortality (HR = 3.12, 95% CI: 1.77–5.48) but did not with HIV plasma viral load response (HR = 1.15, 95% CI:0.89–1.48) or CD4 cell response (HR = 0.95, 95% CI: 0.73–1.23).Conclusion: Our study demonstrates that HIV-infected Aboriginal persons accessing HAART had similar HIVtreatment response as non-Aboriginal persons but have a shorter survival. This study highlights the need forcontinued research on medical interventions and behavioural changes among HIV-infected Aboriginal and othermarginalized populations

Crossing scales, crossing disciplines: collective motion and collective action in the Global Commons†

Two conflicting tendencies can be seen throughout the biological world: individuality and collective behaviour. Natural selection operates on differences among individuals, rewarding those who perform better. Nonetheless, even within this milieu, cooperation arises, and the repeated emergence of multicellularity is the most striking example. The same tendencies are played out at higher levels, as individuals cooperate in groups, which compete with other such groups. Many of our environmental and other global problems can be traced to such conflicts, and to the unwillingness of individual agents to take account of the greater good. One of the great challenges in achieving sustainability will be in understanding the basis of cooperation, and in taking multicellularity to yet a higher level, finding the pathways to the level of cooperation that is the only hope for the preservation of the planet