Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.     

The importance of considering multiple interacting species for conservation of species at risk

Conservation of species at risk of extinction is complex and multifaceted. However, mitigation strategies are typically narrow in scope, an artifact of conservation research that is often limited to a single species or stressor. Knowledge of an entire community of strongly interacting species would greatly enhance the comprehensiveness and effectiveness of conservation decisions. We investigated how camera trapping and spatial count models, an extension of spatial-recapture models for unmarked populations, can accomplish this through a case study of threatened boreal woodland caribou (Rangifer tarandus caribou). Population declines in caribou are precipitous and well documented, but recovery strategies focus heavily on control of wolves (Canis lupus) and pay less attention to other known predators and apparent competitors. Obtaining necessary data on multispecies densities has been difficult. We used spatial count models to concurrently estimate densities of caribou, their predators (wolf, black bear [Ursus americanus], and coyote [Canis latrans]), and alternative prey (moose [Alces alces] and white-tailed deer [Odocoileus virginianus]) from a camera-trap array in a highly disturbed landscape within northern Alberta's Oil Sands Region. Median densities were 0.22 caribous (95% Bayesian credible interval [BCI] = 0.08–0.65), 0.77 wolves (95% BCI = 0.26–2.67), 2.39 moose (95% BCI = 0.56–7.00), 2.64 coyotes (95% BCI = 0.45–6.68), and 3.63 black bears (95% BCI = 1.25–8.52) per 100 km². (The white-tailed deer model did not converge.) Although wolf densities were higher than densities recommended for caribou conservation, we suggest the markedly higher black bear and coyote densities may be of greater concern, especially if government wolf control further releases these species. Caribou conservation with a singular focus on wolf control may leave caribou vulnerable to other predators. We recommend a broader focus on the interacting species within a community when conserving species.     

Reexamining the Minimum Viable Population Concept for Long‐Lived Species

For decades conservation biologists have proposed general rules of thumb for minimum viable population size (MVP); typically, they range from hundreds to thousands of individuals. These rules have shifted conservation resources away from small and fragmented populations. We examined whether iteroparous, long‐lived species might constitute an exception to general MVP guidelines. On the basis of results from a 10‐year capture‐recapture study in eastern New York (U.S.A.), we developed a comprehensive demographic model for the globally threatened bog turtle (Glyptemys muhlenbergii), which is designated as endangered by the IUCN in 2011. We assessed population viability across a wide range of initial abundances and carrying capacities. Not accounting for inbreeding, our results suggest that bog turtle colonies with as few as 15 breeding females have >90% probability of persisting for >100 years, provided vital rates and environmental variance remain at currently estimated levels. On the basis of our results, we suggest that MVP thresholds may be 1–2 orders of magnitude too high for many long‐lived organisms. Consequently, protection of small and fragmented populations may constitute a viable conservation option for such species, especially in a regional or metapopulation context. Reexaminando el Concepto de Población Mínima Viable para Especies Longevas Resumen     

Unignited High-Pressure Methane Jet Impinging a Pipe Rack: Practical Tools for Risk Assessment

Although the diffusion of its storage and transport under liquefied conditions, nowadays it is common to have methane in gaseous form in several industrial applications. This leads to safety implications to be considered: hazards are linked to both the high-pressure at which the gas is kept and to its flammability. Scenarios where flammable jets impact an obstacle are of paramount importance because of their possible occurrence. Following a numerical approach, literature shows up that their assessment can be reliably performed by means of only Computational Fluid Dynamics tools. However, despite the improvements of computing power, Computational Fluid Dynamics costs still limit its use in daily risk analysts’ activities. Therefore, considering an accidental jet-obstacle scenario of industrial interest, the present work investigates how a pipe rack can influence the development of a high-pressure methane jet. Based on a Computational Fluid Dynamics analysis, main achievements of this work are a simple criterion able to identify the situations where the pipe rack does not influence the high-pressure methane jet behavior, therefore allowing to identify the scenarios where simpler models can be used (i.e., analytical correlations known for the free jet situation), and, if present, a simple analytical relationship that roughly predicts the influence of the pipe rack without the need of performing complex Computational Fluid Dynamics simulations.     

Prospects for stakeholder coordination by protected‐area managers in Europe

Growing resource demands by humans, invasive species, natural hazards, and a changing climate have created broad‐scale impacts and the need for broader‐extent conservation activities that span ownerships and even political borders. Implementing regional‐scale conservation brings great challenges, and learning how to overcome these challenges is essential for maintaining biodiversity (i.e., richness and evenness of biological communities) and ecosystem functions and services across scales and borders in the face of system change. We administered an online survey to examine factors potentially driving perspectives of protected‐area (PA) managers regarding coordination with neighboring PAs and other stakeholders (i.e., stakeholder coordination) for conserving biodiversity and ecosystem services during the next decade within diverse regions across Europe. Although >70% (n = 58) of responding PA managers indicated that climate change and invasive species are relevant for their PAs, they gave <50% probability that these threats could be mitigated through stakeholder coordination. They thought there was a >60% probability (n = 85) that stakeholder coordination would take place with the aim to improve conservation outcomes. Consistent with the foundation on which many European PAs were established, managers viewed maintaining or enhancing biodiversity as the most important (>70%; n = 61) expected benefit. Other benefits included maintaining or enhancing human resources and environmental education (range of Bayesian credibility intervals [CIs] 57–93%). They thought the main barriers to stakeholder coordination were the lack of human and economic resources (CI 59–67% chance of hindering; n = 64) followed by communication and interstakeholder differences in political structures and laws (CI 51–64% probability of hindering). European policies and strategies that address these hindering factors could be particularly effective means of enabling implementation of green infrastructure networks in which PAs are the nodes.     

Influence of influent on anaerobic ammonium oxidation in an Expanded Granular Sludge Bed-Biological Aerated Filter integrated system

Shortcut nitrification-denitrification, anaerobic ammonium oxidation (ANAMMOX), and methanogenesis have been successfully coupled in an Expanded Granular Sludge Bed-Biological Aerated Filter (EGSB-BAF) integrated system. As fed different synthetic wastewater with chemical oxygen demand (COD) of 300–1200 mg·L^-1 and NH4+-N of 30–120 mg·L^-1 at the outer recycle ratio of 200%, the influence of influent on ANAMMOX in the integrated system was investigated in this paper. The experimental results showed that higher COD concentration caused an increase in denitrification and methanogenesis but a decrease in ANAMMOX; however, when an influent with the low concentration of COD was used, the opposite changes could be observed. Higher influent NH4+-N concentration favored ANAMMOX when the COD concentration of influent was fixed. Therefore, low COD/NH4+-N ratio would decrease competition for nitrite between ANAMMOX and denitrification, which was favorable for reducing the negative effect of organic COD on ANAMMOX. The good performance of the integrated system indicated that the bacterial community of denitrification, ANAMMOX, and methanogenesis could be dynamically maintained in the sludge of EGSB reactor for a certain range of influent.     

A Hierarchical Bayesian model of wildfire in a Mediterranean biodiversity hotspot: Implications of weather variability and global circulation

In this study we combined an extensive database of observed wildfires with high-resolution meteorological data to build a novel spatially and temporally varying survival model to analyze fire regimes in the Mediterranean ecosystem in the Cape Floristic Region (CFR) of South Africa during the period 1980-2000. The model revealed an important influence of seasonally anomalous weather on fire probability, with increased probability of fire in seasons that are warmer and drier than average. In addition to these local-scale influences, the Antarctic Ocean Oscillation (AAO) was identified as an important large-scale influence or teleconnection to global circulation patterns. Fire probability increased in seasons during positive AAO phases, when the subtropical jet moves northward and low level moisture transport decreases. These results confirm that fire occurrence in the CFR is strongly affected by climatic variability at both local and global scales, and thus likely to respond sensitively to future climate change. Comparison of the modelled fire probability between two periods (1951-1975 and 1976-2000) revealed a 4-year decrease in an average fire return time. If, as currently forecasted, climate change in the region continues to produce higher temperatures, more frequent heat waves, and/or lower rainfall, our model thus indicates that fire frequency is likely to increase substantially. The regional implications of shorter fire return times include shifting community structure and composition, favoring species that tolerate more frequent fires.     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

Hierarchical Bayesian Spatial Models for Multispecies Conservation Planning and Monitoring

Abstract: 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 spatial models outperformed analogous models 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. Spatial models 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.     

水文频率线型选择与综合过程中贝叶斯因子求解新方法

为克服水文频率线型选择和综合过程中,贝叶斯因子求解时参数先验分布确定和线型边缘分布数值积分这两个难点问题,联合应用了贝叶斯采样方法和最大熵原理(POME)求解参数后验分布表达式,然后应用参数采样结果中逐个样本近似求和方法代替线型边缘分布积分过程,进而建立了贝叶斯因子求解新方法。实例分析和Monte-Carlo统计试验验证了该方法的准确性和有效性。分析结果显示:序列长度和参数取值大小等因素对贝叶斯因子和水文线型后验概率求解结果影响较大;由于BIC准则的实质是通过寻求一组最优参数值进行贝叶斯因子求解,因此在受到不利因素影响时参数估计结果往往存在较大误差,使得BIC准则的分析结果也存在较大偏差。贝叶斯因子求解新方法能够克服上述不利因素的影响,可以合理地分析和描述参数不确定性,使得分析计算结果明显改善,因此所提新方法具有更好的实用性和可靠性。