Assessing the effects of disease and bleaching on Florida Keys corals by fitting population models to data

Coral diseases have increased in frequency over the past few decades and have important influences on the structure and composition of coral reef communities. However, there is limited information on the etiologies of many coral diseases, and pathways through which coral diseases are acquired and transmitted are still in question. Furthermore, it is difficult to assess the impacts of disease on coral populations because outbreaks often co-occur with temperature-induced bleaching and anthropogenic stressors. We developed spatially explicit population models of coral disease and bleaching dynamics to quantify the impact of six common diseases on Florida Keys corals, including aspergillosis, dark spots, white band, white plague, white patch, and Caribbean yellow band. Models were fit to an 8-year data set of coral abundance, disease prevalence, and bleaching prevalence. Model selection was used to assess alternative pathways for disease transmission, and the influence of environmental stressors, including sea temperature and human population density, on disease prevalence and coral mortality. Classic disease transmission from contagious to susceptible colonies provided the best-fit model only for aspergillosis. For other diseases, external disease forcing, such as through a vector or directly from pathogens in the environment, provided the best fit to observed data. Estimates of disease reproductive ratio values (R₀) were less than one for each disease, indicating coral colonies were below densities required for diseases to become established through contagious spread alone. Incidences of white band and white patch disease were associated with greater susceptibility or slower recovery of bleached colonies, and no disease outbreaks were associated with periods of elevated sea temperatures alone. Projections of best-fit models indicated that, atleast during the period of this study, disease and bleaching did not have substantial impacts on populations and impaired rates of population growth appeared to be attributable to other stressors. By applying epidemiological models to field data, our study gives qualitative insights into the dynamics of coral diseases, relative stressor impacts, and directions for future research.     

The role of agent-based models in wildlife ecology and management

Conservation planning of critical habitats for wildlife species at risk is a priority topic that requires the knowledge of how animals select and use their habitat, and how they respond to future developmental changes in their environment. This paper explores the role of a habitat-modeling methodological approach, agent-based modeling, which we advocate as a promising approach for ecological research. Agent-based models (ABMs) are capable of simultaneously distinguishing animal densities from habitat quality, can explicitly represent the environment and its dynamism, can accommodate spatial patterns of inter- and intra-species mechanisms, and can explore feedbacks and adaptations inherent in these systems. ABMs comprise autonomous, individual entities; each with dynamic, adaptive behaviors and heterogeneous characteristics that interact with each other and with their environment. These interactions result in emergent outcomes that can be used to quantitatively examine critical habitats from the individual- to population-level. ABMs can also explore how wildlife will respond to potential changes in environmental conditions, since they can readily incorporate adaptive animal-movement ecology in a changing landscape. This paper describes the necessary elements of an ABM developed specifically for understanding wildlife habitat selection, reviews the current empirical literature on ABMs in wildlife ecology and management, and evaluates the current and future roles these ABMs can play, specifically with regards to scenario planning of designated critical habitats.     

Toward an integrative molecular approach to wildlife disease

Pathogens pose serious threats to human health, agricultural investment, and biodiversity conservation through the emergence of zoonoses, spillover to domestic livestock, and epizootic outbreaks. As such, wildlife managers are often tasked with mitigating the negative effects of disease. Yet, parasites form a major component of biodiversity that often persist. This is due to logistical challenges of implementing management strategies and to insufficient understanding of host–parasite dynamics. We advocate for an inclusive understanding of molecular diversity in driving parasite infection and variable host disease states in wildlife systems. More specifically, we examine the roles of genetic, epigenetic, and commensal microbial variation in disease pathogenesis. These include mechanisms underlying parasite virulence and host resistance and tolerance, and the development, regulation, and parasite subversion of immune pathways, among other processes. Case studies of devil facial tumor disease in Tasmanian devils (Sarcophilus harrisii) and chytridiomycosis in globally distributed amphibians exemplify the broad range of questions that can be addressed by examining different facets of molecular diversity. For particularly complex systems, integrative molecular analyses present a promising frontier that can provide critical insights necessary to elucidate disease dynamics operating across scales. These insights enable more accurate risk assessment, reconstruction of transmission pathways, discernment of optimal intervention strategies, and development of more effective and ecologically sound treatments that minimize damage to the host population and environment. Such measures are crucial when mitigating threats posed by wildlife disease to humans, domestic animals, and species of conservation concern.     

Using culturomics and social media data to characterize wildlife consumption

Wildlife provides food, medicine, clothing, and other necessities for humans, but overexploitation can disrupt the sustainability of wildlife resources and severely threaten global biodiversity. Understanding the characteristics of consumer behavior is helpful for wildlife managers and policy makers, but the traditional survey methods are laborious and time-consuming. In contrast, culturomics may more efficiently identify the features of wildlife consumption. As a case study of the culturomics approach, we examined tiger bone wine consumption in China based on social media and Baidu search engine data. Tiger bone wine is one of the most purchased tiger products; its consumption is closely related to tiger poaching, which greatly threatens wild tiger survival. We searched a popular social media website for the term “tiger bone wine” and focused on posts that were originally created from 1 January 2012 to 31 December 2018. We filtered and classified posts related to the purchase, sale, or consumption of tiger bone wine and extracted information on providers, consumption motivations, year of production, and place of origin of the tiger bone wines based on the texts and photos of these posts. We found 756 posts related to tiger bone wine consumption, 113 of which mentioned providers of tiger bone wine, including friends (53%), elder relatives (37%), peer relatives (7%), and others (3%). Out of the 756 posts, 266 indicated the motivations of tiger bone wine consumption. Tiger bone wines were consumed as a tonic (34%), medicine (23%), game product (30%), and a symbol of wealth (28%). Some posts indicated ≥2 consumption motivations. These findings were consistent with the search queries from Baidu index. Such information could help develop targeted strategies for tiger conservation. The culturomics approach illustrated by our study is a rapid and cost-efficient way to characterize wildlife consumption.     

Fitting probability models to population dynamics data

Methods for modeling population dynamics in probability using the generalized point process approach are developed. The life history of these populations is such that seasonal reproduction occurs during a short time. Several models are developed and analyzed. Data about two species: colonial spiders (Stegodyphus dumicola) and a migratory bird (wood thrush, Hylocichla mustelina) are used to estimate model parameters with appropriate log maximum likelihood functions. For the spiders, the model is fitted to provide evolutionary feasible colony size based on maximum likelihood estimates of fecundity and survival data. For the migratory bird species, a maximum likelihood estimates are derived for the fecundity and survival rates of young and adult birds and immigration rate. The presented approach allows computation of quantities of interest such as probability of extinction and average time to extinction.     

Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models.

Bayesian methods incorporate prior knowledge into a statistical analysis. This prior knowledge is usually restricted to assumptions regarding the form of probability distributions of the parameters of interest, leaving their values to be determined mainly through the data. Here we show how a Bayesian approach can be applied to the problem of drawing inference regarding species abundance distributions and comparing diversity indices between sites. The classic log series and the lognormal models of relative- abundance distribution are apparently quite different in form. The first is a sampling distribution while the other is a model of abundance of the underlying population. Bayesian methods help unite these two models in a common framework. Markov chain Monte Carlo simulation can be used to fit both distributions as small hierarchical models with shared common assumptions. Sampling error can be assumed to follow a Poisson distribution. Species not found in a sample, but suspected to be present in the region or community of interest, can be given zero abundance. This not only simplifies the process of model fitting, but also provides a convenient way of calculating confidence intervals for diversity indices. The method is especially useful when a comparison of species diversity between sites with different sample sizes is the key motivation behind the research. We illustrate the potential of the approach using data on fruit-feeding butterflies in southern Mexico. We conclude that, once all assumptions have been made transparent, a single data set may provide support for the belief that diversity is negatively affected by anthropogenic forest disturbance. Bayesian methods help to apply theory regarding the distribution of abundance in ecological communities to applied conservation.     

Zero-inflated models with application to spatial count data

Count data arises in many contexts. Here our concern is with spatial count data which exhibit an excessive number of zeros. Using the class of zero-inflated count models provides a flexible way to address this problem. Available covariate information suggests formulation of such modeling within a regression framework. We employ zero-inflated Poisson regression models. Spatial association is introduced through suitable random effects yielding a hierarchical model. We propose fitting this model within a Bayesian framework considering issues of posterior propriety, informative prior specification and well-behaved simulation based model fitting. Finally, we illustrate the model fitting with a data set involving counts of isopod nest burrows for 1649 pixels over a portion of the Negev desert in Israel.     

The changing sociocultural context of wildlife conservation

We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017–2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves (Canis lupus) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.     

From data and theory to environmental models and indices formation

A prerequisite for environmental indices is that they represent environmental pressure, and the state of, and impact on environmental conditions. In other words, they should capture as much as possible of the cause-effect chains they represent and relate pressure and effect to criteria of environmental quality. The approach proposed in the article attempts to link the pressure–state–impact–response framework of indicators to the integrated environmental model, based on the method of response function (MRF). The MRF allows to construct purposeful, credible models from data and prior knowledge or information. The data are usually time series observations of system inputs and outputs, and sometimes of internal states. The output of such models is presented with highly aggregated environmental indices, reflecting the main pressure–state–impact–response cause-effect chains. The proposed approach is illustrated with the example of soil erosion indices.     

Asymmetric circular-linear multivariate regression models with applications to environmental data

We propose asymmetric angular-linear multivariate regression models, which were motivated by the need to predict some environmental characteristics based on some circular and linear predictors. A measure of fit is provided through the residual analysis. Some applications using data from solar energy radiation experiment and wind energy are given. Received: September 2003 / Revised: February 2005     

Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Crop and livestock depredation by wildlife is a primary driver of human–wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08–0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human–wildlife interactions.     

The new approaches on sustainable wildlife of Amik Lake

Turkey has been on a crucial routes of the migrating birds. These birds have been coming from Istanbul-Camlica hills and through Artvin-Borcka to the homeland. And the migrating birds turn to the Africa continent over Syria by passing Hatay-Belen. During the mmigration, the birds use the location of Amik Lake as a watery land for temporary settlement. The Amik Lake which should have been protected as a watery land for birds according to Ramsar Agreement that Turkey signed, was caused to dry ten years ago. There weren't only negative effects on the wildlife by drying but also soil degraded and productivity decreased though the aim was to improve agriculture and contributions to local developments on the Amik Lake dry land. During the last two decades, it was known that one of the birds, which is called Anhinga rufa extincted as a cause of drying ot Amik Lake. In this study, some results that emerged as a cause of drying of Amik Lake were presented.     

The perils of flawed science in wildlife trade literature

Despite broad scientific consensus that sustainable use of wildlife can enhance conservation efforts, ethical concerns have led some community groups to oppose use of wild animals. Voicing those concerns is legitimate, but underlying philosophical bias should not influence science-based analysis and interpretation. We argue that philosophical biases are common in the scientific literature on trade in wildlife. The critically important case of bias surrounding the use of reptile leathers for luxury fashion illustrates the problem. Based on analysis of official seizures of fashion products made from wildlife, a recent study inferred that criminal activity (as inferred by noncompliance with regulations) was common and increasing and, hence, that authorities needed to adopt more stringent restrictions on the trade. In fact, the conclusions of that study are artifacts of pseudoreplication (e.g., multiple counts of single violations) and biased sampling (e.g., focus on companies with high rates of error) and run directly opposite to actual patterns in the data. As a proportion of overall trade, rates of noncompliance are exceptionally low (<0.4%), are declining, and result primarily from paper-work errors rather than criminal intent (e.g., such errors are more frequent for goods shipped by government authorities than by the commercial fashion industry). The recommendation by the study authors to prohibit the international trade in wildlife-based fashion products is imperiling a sustainable trade that can benefit biodiversity and people's livelihoods by providing financial incentives for conservation of species and habitats. This example offers a warning of the dangers of basing research on the wildlife trade on ethical or philosophical positions rather than objective evaluations of evidence.     

Habitat selection models to account for seasonal persistence in radio telemetry data

Models for the analysis of habitat selection data incorporate covariates in an independent multinomial selections model (McCracken et al. 1998) Ramsey and Usner 2003 and an extension of that model to include a persistence parameter (2003). In both cases, all parameters are assumed to be fixed through time. Radio telemetry data collected for habitat selection studies typically consist of animal relocations through time, suggesting the need for an extension to these models. We use a Bayesian approach that allows for the habitat selection probabilities, persistence parameter, or both, to change with season. These extensions are particularly important when movement patterns are expected to differ seasonally and/or when availabilities of habitats change throughout the study period due to weather or migration. We implement and compare the models using radio telemetry data for westslope cutthroat trout in two streams in eastern Oregon.     

Multivariate Bayesian analysis of atmosphere–ocean general circulation models

Numerical experiments based on atmosphere–ocean general circulation models (AOGCMs) are one of the primary tools in deriving projections for future climate change. Although each AOGCM has the same underlying partial differential equations modeling large scale effects, they have different small scale parameterizations and different discretizations to solve the equations, resulting in different climate projections. This motivates climate projections synthesized from results of several AOGCMs’ output. We combine present day observations, present day and future climate projections in a single highdimensional hierarchical Bayes model. The challenging aspect is the modeling of the spatial processes on the sphere, the number of parameters and the amount of data involved. We pursue a Bayesian hierarchical model that separates the spatial response into a large scale climate change signal and an isotropic process representing small scale variability among AOGCMs. Samples from the posterior distributions are obtained with computer-intensive MCMC simulations. The novelty of our approach is that we use gridded, high resolution data covering the entire sphere within a spatial hierarchical framework. The primary data source is provided by the Coupled Model Intercomparison Project (CMIP) and consists of 9 AOGCMs on a 2.8 by 2.8 degree grid under several different emission scenarios. In this article we consider mean seasonal surface temperature and precipitation as climate variables. Extensions to our model are also discussed.     

Understanding complex drivers of wildlife crime to design effective conservation interventions

In conservation understanding the drivers of behavior and developing robust interventions to promote behavioral change is challenging and requires a multifaceted approach. This is particularly true for efforts to address illegal wildlife use, where pervasive—and sometimes simplistic—narratives often obscure complex realities. We used an indirect questioning approach, the unmatched count technique, to investigate the drivers and prevalence of wildlife crime in communities surrounding 2 national parks in Uganda and combined scenario interviews and a choice experiment to predict the performance of potential interventions designed to tackle these crimes. Although poverty is often assumed to be a key driver of wildlife crime, we found that better-off households and those subject to human–wildlife conflict and those that do not receive any benefits from the parks’ tourism revenue sharing were more likely to be involved in certain types of wildlife crime, especially illegal hunting. The interventions predicted to have the greatest impact on reducing local participation in wildlife crime were those that directly addressed the drivers including, mitigating damage caused by wildlife and generating financial benefits for park-adjacent households. Our triangulated approach provided insights into complex and hard-to-access behaviors and highlighted the importance of going beyond single-driver narratives.     

Age-dependent survival analyzed with Bayesian models of mark-recapture data

We describe a Bayesian random effects model of mark-recapture data that accounts for age-dependence in survival and individual heterogeneity in capture probabilities and survival. The model is applied to data on the Glanville fritillary butterfly (Melitaea cinxia) collected from a population enclosed in a large cage in the field. The cage population consisted of a mixture of butterflies originating from newly established and old populations in a large metapopulation in the Aland Islands in Finland. The explanatory variables in the model included the effects of temperature, sex, and population type (new vs. old) on capture probabilities, and the effects of age, sex, population type, and day vs. night on survival. We found that mortality rate increased with age, that mortality rate was much higher during the day than during the night, and that the life span of females originating from newly established populations was shorter than the life span of females from old populations. Capture probability decreased with increasing temperature and decreased with increasing mobility of individuals.     

Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data

The mean of a balanced ranked set sample is more efficient than the mean of a simple random sample of equal size and the precision of ranked set sampling may be increased by using an unbalanced allocation when the population distribution is highly skewed. The aim of this paper is to show the practical benefits of the unequal allocation in estimating simultaneously the means of more skewed variables through real data. In particular, the allocation rule suggested in the literature for a single skewed distribution may be easily applied when more than one skewed variable are of interest and an auxiliary variable correlated with them is available. This method can lead to substantial gains in precision for all the study variables with respect to the simple random sampling, and to the balanced ranked set sampling too.     

Modified Weibull multi-state models for the analysis of animal carcinogenicity data

The Peto test is the standard method of analysis used in carcinogenicity studies to compare tumor incidence in groups of animals. It assumes that tumors are either instantly fatal or have no effect on mortality and requires a judgement of the lethality of each tumor. To avoid this requirement, parametric multi-state models have been proposed. In addition these allow estimation of tumor onset and mortality rates. This paper considers two such models and presents a modification. It is shown that the modified models provide a better fit to carcinogenicity data and simulated data are used to show that the modified models provide a modest increase in test power relative to the Peto test.