A Bayesian state-space formulation of dynamic occupancy models

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.     

Taxes versus emissions trading system: evaluating environmental policies that affect multiple types of pollution

Environmental Economics and Policy Studies - This paper examines the interaction of different policies used to control two types of agricultural pollution. Pollution control policy is efficient...     

Do you only have yourself to blame? A meta-analytic test of the victim precipitation model

The current meta-analysis sought to evaluate the empirical evidence for the victim precipitation model, which has become an increasingly popular yet controversial theory in the organizational sciences. We did so by testing the prediction that some victim dispositional traits contribute to or provoke experiences of mistreatment. We additionally provided preliminary examinations of two distinct conceptual explanations underlying the empirical relationships between victim personality and mistreatment. Finally, we examined the support for the situational antecedents of experienced mistreatment to compare the relative evidence for each of these dominant theoretical explanations. Results for the tests of the victim precipitation model showed that only victim negative affectivity was consistently related to experienced mistreatment. Examinations of the explanations for the relationships between victim personality and victimization showed relatively weak support for the notion that certain employees are more likely to perceive mistreatment and for the proposition that certain employees are mistreated because they are also more likely to engage in mistreatment. Finally, the situational predictors of mistreatment were all supported, and a test of relative importance revealed that the situational antecedents accounted for more variance in mistreatment than the victim dispositional traits. Implications for the theoretical understanding of the origins of mistreatment are discussed.     

The role of partisan conflict in environmental sustainability targets of the United States

The prevailing political atmosphere and partisanship in the United States depict the degree of polarization between the two major political parties of the country. Evidently, the polarization between the Democratic Party (DP) and the Republican Party will expectedly drive the partisan conflict to the higher levels. Considering this motivation, this paper examined the role of partisan conflict in the pollutant emissions in the case of the United States. For sound empirical analysis, the impacts of other environmental quality determinants are being examined over the period 1960–2015. In order to present a decent argument that is viable for policy implementation, the study adopts the combined methodologies of Johansen cointegration; the autoregressive distributed lag (ARDL) of Pesaran et al. J Am Stat Assoc 94(446):621–634 (1999); and the Toda and Yamamoto J Econ 66(1–2):225–250 (1995) Granger causality. Empirical outcomes show (i) the kg oil equivalent per capita energy consumed exercise positive and significant impacts on metric tons of per capita CO2 emissions, and it is the principal determinant of environmental degradation in both the short-run and the long-run (ii) renewable energy consumption and economic growth also exercise negative and significant impacts on metric tons of per capita CO2 emissions. Based on our empirical findings, we conclude that partisan conflict indirectly plays a significant role in environmental sustainability targets of the United States. Thus, we are of the opinion that the government should avoid heightened partisan conflict among the political parties in order to promote sustainable environmental policies that would enhance sound and clean environment for both the immediate and the future generation.     

Evaluation of an explicit NOx chemistry method in AERMOD

An explicit NO_x chemistry method has been implemented in AERMOD version 15181, ADMSM. The scheme has been evaluated by comparison with the methodologies currently recommended by the U.S. EPA for Tier 3 NO₂ calculations, that is, OLM and PVMRM2. Four data sets have been used for NO₂ chemistry method evaluation. Overall, ADMSM-modeled NO₂ concentrations show the most consistency with the AERMOD calculations of NO_x and the highest Index of Agreement; they are also on average lower than those of both OLM and PVMRM2. OLM shows little consistency with modeled NO_x concentrations and markedly overpredicts NO₂. PVMRM2 shows performance closer to that of ADMSM than OLM; however, its behavior is inconsistent with modeled NO_x in some cases and it has less good statistics for NO₂. The trend in model performance can be explained by examining the features particular to each chemistry method: OLM can be considered as a screening model as it calculates the upper bound of conversion from NO to NO₂ possible with the background O₃ concentration; PVMRM2 includes a much-improved estimate of in-plume O₃ but is otherwise similar to OLM, assuming instantaneous reaction of NO with O₃; and ADMSM allows for the rate of this reaction and also the photolysis of NO₂. Evaluation with additional data sets is needed to further clarify the relative performance of ADMSM and PVMRM2.

Implications: Extensive evaluation of the current AERMOD Tier 3 chemistry methods OLM and PVMRM2, alongside a new scheme that explicitly calculates the oxidation of NO by O₃ and the reverse photolytic reaction, shows that OLM consistently overpredicts NO₂ concentrations. PVMRM2 performs well in general, but there are some cases where this method overpredicts NO₂. The new explicit NO_x chemistry scheme, ADMSM, predicts NO₂ concentrations that are more consistent with both the modeled NO_x concentrations and the observations.     

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.     

Using a novel climate–water conflict vulnerability index to capture double exposures in Lake Chad

Climate variability is amongst an array of threats facing agricultural livelihoods, with its effects unevenly distributed. With resource conflict being increasingly recognised as one significant outcome of climate variability and change, understanding the underlying drivers that shape differential vulnerabilities in areas that are double-exposed to climate and conflict has great significance. Climate change vulnerability frameworks are rarely applied in water conflict research. This article presents a composite climate–water conflict vulnerability index based on a double exposure framework developed from advances in vulnerability and livelihood assessments. We apply the index to assess how the determinants of vulnerability can be useful in understanding climate variability and water conflict interactions and to establish how knowledge of the climate–conflict linked context can shape interventions to reduce vulnerability. We surveyed 240 resource users (farmers, fishermen and pastoralists) in seven villages on the south-eastern shores of Lake Chad in the Republic of Chad to collect data on a range of exposure, sensitivity and adaptive capacity variables. Results suggest that pastoralists are more vulnerable in terms of climate-structured aggressive behaviour within a lake-based livelihoods context where all resource user groups show similar levels of exposure to climate variability. Our approach can be used to understand the human and environmental security components of vulnerability to climate change and to explore ways in which conflict-structured climate adaptation and climate-sensitive conflict management strategies can be integrated to reduce the vulnerability of populations in high-risk, conflict-prone environments.     

Exploring future agricultural development and biodiversity in Uganda,Rwanda and Burundi: a spatially explicit scenario-based assessment

Regional Environmental Change - Competition for land is increasing as a consequence of the growing demands for food and other commodities and the need to conserve biodiversity and ecosystem...     

A science of integration: frameworks,processes, and products in a place-based,integrative study

Integrative research is increasingly a priority within the scientific community and is a central goal for the evolving field of sustainability science. While it is conceptually attractive, its successful implementation has been challenging and recent work suggests that the move towards interdisciplinarity and transdisciplinarity in sustainability science is being only partially realized. To address this from the perspective of social-ecological systems (SES) research, we examine the process of conducting a science of integration within the Southcentral Alaska Test Case (SCTC) of Alaska-EPSCoR as a test-bed for this approach. The SCTC is part of a large, 5 year, interdisciplinary study investigating changing environments and adaptations to those changes in Alaska. In this paper, we review progress toward a science of integration and present our efforts to confront the practical issues of applying proposed integration frameworks. We: (1) define our integration framework; (2) describe the collaborative processes, including the co-development of science through stakeholder engagement and partnerships; and (3) illustrate potential products of integrative, social-ecological systems research. The approaches we use can also be applied outside of this particular framework. We highlight challenges and propose improvements for integration in sustainability science by addressing the need for common frameworks and improved contextual understanding. These insights may be useful for capacity-building for interdisciplinary projects that address complex real-world social and environmental problems.