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.     

Forecasting population trend from the scaling pattern of occupancy

Forecasting the temporal trend of a focal species, its range expansion or retraction, provides crucial information regarding population viability. To this end, we require the accumulation of temporal records which is evidently time consuming. Progress in spatial data capturing has enabled rapid and accurate assessment of species distribution across large scales. Therefore, it would be appealing to infer the temporal trends of populations from the spatial structure of their distributions. Based on a combination of models from the fields of range dynamics, occupancy scaling and spatial autocorrelation, here I present a model for forecasting the population trend solely from its spatial distribution. Numerical tests using cellular automata confirm a positive correlation, as inferred from the model, between the temporal change in species range sizes and the exponent of the power-law scaling pattern of occupancy. The model is thus recommended for rapid estimation of species range dynamics from a single snapshot of its current distribution. Further applications in biodiversity conservation could provide a swift risk assessment, especially, for endangered and invasive species.     

Habitat-specific impacts of multiple consumers on plant population dynamics

Multiple consumers often attack seeds, seedlings, and adult plants, but their population-level consequences remain uncertain. We examined how insect and small mammal consumers influenced the demography and abundance of the perennial shrub, bush lupine (Lupinus arboreus). In grassland and dune habitats we established replicate experimental lupine populations in 81-m2 plots that were either protected from, or exposed to, herbivorous voles and granivorous mice (via fencing) and/or root feeding insects (via insecticide treatment). Populations were initiated with transplanted seedlings in 1999 and 2000. We followed the demography of these cohorts, subsequent generations, and the seed bank for 5.5 years. Voles and insects killed many seedlings in dune (1999 only) and grassland (1999 and 2000) habitats. After 2000, insects and voles had minimal effects on seedling or adult survival. Seed predation by granivorous mice, however, greatly depressed seedling recruitment, resulting in lower adult lupine abundance in control plots vs. those protected from rodents. In grasslands, initial effects of voles and insects on seedling survival produced large differences among treatments in adult plant density and the cumulative number of seeds produced throughout the experiment. Differences among grassland populations in seed rain, however, had little influence on the magnitude of seedling recruitment into this habitat. Instead, recruitment out of a preexisting seed bank compensated for the lack of seed production in populations exposed to consumers. Shading by dense adults in plots protected from consumers limited seedling establishment within these populations. Although differences among populations in cumulative seed rain did not influence adult establishment, populations protected from consumers accumulated substantially larger seed banks than controls. These results illustrate how density dependence, habitat-specific seed dynamics, and particular demographic impacts of consumers interact to shape plant population responses to consumers.     

Comparative analysis of impact of human occupancy on indoor microbiomes

• Exposure to indoor microbiomes is a public health concern in educational facilities. • Indoor microbiomes were characterized in two multifunctional university buildings. • Human occupancy had significant impact on the composition of indoor microbiomes. • The skin microbiota of occupants represented important sources of indoor microbiomes. Educational facilities serve as community hubs and consequently hotspots for exposure to pathogenic microorganisms. Therefore, it is of critical importance to understand processes shaping the indoor microbiomes in educational facilities to protect public health by reducing potential exposure risks of students and the broader community. In this study, the indoor surface bacterial microbiomes were characterized in two multifunctional university buildings with contrasting levels of human occupancy, of which one was recently constructed with minimal human occupancy while the other had been in full operation for six years. Higher levels of human occupancy in the older building were shown to result in greater microbial abundance in the indoor environment and greater proportion of the indoor surface bacterial microbiomes contributed from human-associated microbiota, particularly the skin microbiota. It was further revealed that human-associated microbiota had greater influence on the indoor surface bacterial microbiomes in areas of high occupancy than areas of low occupancy. Consistent with minimal impact from human occupancy in a new construction, the indoor microbiomes in the new building exhibited significantly lower influence from human-associated microbiota than in the older building, with microbial taxa originating from soil and plants representing the dominant constituents of the indoor surface bacterial microbiomes. In contrast, microbial taxa in the older building with extensive human occupancy were represented by constituents of the human microbiota, likely from occupants. These findings provide insights into processes shaping the indoor microbiomes which will aid the development of effective strategies to control microbial exposure risks of occupants in educational facilities.     

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.     

Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics

A stochastic individual-based model (IBM) of mosquitofish population dynamics in experimental ponds was constructed in order to increase, virtually, the number of replicates of control populations in an ecotoxicology trial, and thus to increase the statistical power of the experiments. In this context, great importance had to be paid to model calibration as this conditions the use of the model as a reference for statistical comparisons. Accordingly, model calibration required that both mean behaviour and variability behaviour of the model were in accordance with real data. Currently, identifying parameter values from observed data is still an open issue for IBMs, especially when the parameter space is large. Our model included 41 parameters: 30 driving the model expectancy and 11 driving the model variability. Under these conditions, the use of “Latin hypercube” sampling would most probably have “missed” some important combinations of parameter values. Therefore, complete factorial design was preferred. Unfortunately, due to the constraints of the computational capacity, cost-acceptable “complete designs” were limited to no more than nine parameters, the calibration question becoming a parameter selection question. In this study, successive “complete designs” were conducted with different sets of parameters and different parameter values, in order to progressively narrow the parameter space. For each “complete design”, the selection of a maximum of nine parameters and their respective n values was carefully guided by sensitivity analysis. Sensitivity analysis was decisive in selecting parameters that were both influential and likely to have strong interactions. According to this strategy, the model of mosquitofish population dynamics was calibrated on real data from two different years of experiments, and validated on real data from another independent year. This model includes two categories of agents; fish and their living environment. Fish agents have four main processes: growth, survival, puberty and reproduction. The outputs of the model are the length frequency distribution of the population and the 16 scalar variables describing the fish populations. In this study, the length frequency distribution was parameterized by 10 scalars in order to be able to perform calibration. The recently suggested notion of “probabilistic distribution of the distributions” was also applied to our case study, and was shown to be very promising for comparing length frequency distributions (as such).     

General methods for sensitivity analysis of equilibrium dynamics in patch occupancy models

Sensitivity analysis is a useful tool for the study of ecological models that has many potential applications for patch occupancy modeling. Drawing from the rich foundation of existing methods for Markov chain models, I demonstrate new methods for sensitivity analysis of the equilibrium state dynamics of occupancy models. Estimates from three previous studies are used to illustrate the utility of the sensitivity calculations: a joint occupancy model for a prey species, its predators, and habitat used by both; occurrence dynamics from a well-known metapopulation study of three butterfly species; and Golden Eagle occupancy and reproductive dynamics. I show how to deal efficiently with multistate models and how to calculate sensitivities involving derived state variables and lower-level parameters. In addition, I extend methods to incorporate environmental variation by allowing for spatial and temporal variability in transition probabilities. The approach used here is concise and general and can fully account for environmental variability in transition parameters. The methods can be used to improve inferences in occupancy studies by quantifying the effects of underlying parameters, aiding prediction of future system states, and identifying priorities for sampling effort.     

鼎湖山格木种群动态分析

对格木(Erythrophleum fordii)种群的生态环境及生长曲线和种群动态的研究,能揭示其生态特性和种群发展轨迹,对保护、繁殖这一珍稀植物种质具有积极的意义.在对鼎湖山国家自然保护区内的格木群落物种多样性测定的基础上,以国际上通用的CTFS规范对格木群落进行野外调查.文章首次通过对格木种群运用加速单纯形迭代法,逐次把种群数量数据代入6个种群动态模型方程进行拟合分析,得出实际的格木种群动态模型,比较残差平方和、决定系数和F检验显著性概率值,得出格木种群模型的适用度顺序依次为:李新运模型、张大勇模型、宋丁全模型、logistic模型、密度制约模型、崔启武模型.选用李新运模型,确定格木种群大小初始值的精确值N0后,把数据迭代入李新运模型重新拟合,结果显示,拟合优度进一步提高,并且可得知,该格木种群远未达到种群饱和态,而且也没达到种群增长最快处,还处于加速增长状态.故这个时期应该加大保护力度,建立监测体系,以维系格木群落的稳定发展.     

Estimating the extent and structure of trade in horticultural orchids via social media

The wildlife trade is a lucrative industry involving thousands of animal and plant species. The increasing use of the internet for both legal and illegal wildlife trade is well documented, but there is evidence that trade may be emerging on new online technologies such as social media. Using the orchid trade as a case study, we conducted the first systematic survey of wildlife trade on an international social‐media website. We focused on themed forums (groups), where people with similar interests can interact by uploading images or text (posts) that are visible to other group members. We used social‐network analysis to examine the ties between 150 of these orchid‐themed groups to determine the structure of the network. We found 4 communities of closely linked groups based around shared language. Most trade occurred in a community that consisted of English‐speaking and Southeast Asian groups. In addition to the network analysis, we randomly sampled 30 groups from the whole network to assess the prevalence of trade in cultivated and wild plants. Of 55,805 posts recorded over 12 weeks, 8.9% contained plants for sale, and 22–46% of these posts pertained to wild‐collected orchids. Although total numbers of posts about trade were relatively small, the large proportion of posts advertising wild orchids for sale supports calls for better monitoring of social media for trade in wild‐collected plants.     

Recolonizing wolves and mesopredator suppression of coyotes: impacts on pronghorn population dynamics.

Food web theory predicts that the loss of large carnivores may contribute to elevated predation rates and, hence, declining prey populations, through the process of mesopredator release. However, opportunities to test predictions of the mesopredator release hypothesis are rare, and the extent to which changes in predation rates influence prey population dynamics may not be clear due to a lack of demographic information on the prey population of interest. We utilized spatial and seasonal heterogeneity in wolf distribution and abundance to evaluate whether mesopredator release of coyotes (Canis latrans), resulting from the extirpation of wolves (Canis lupus) throughout much of the United States, contributes to high rates of neonatal mortality in ungulates. To test this hypothesis, we contrasted causes of mortality and survival rates of pronghorn (Antilocapra americana) neonates captured at wolf-free and wolf-abundant sites in western Wyoming, USA, between 2002 and 2004. We then used these data to parameterize stochastic population models to heuristically assess the impact of wolves on pronghorn population dynamics due to changes in neonatal survival. Coyote predation was the primary cause of mortality at all sites, but mortality due to coyotes was 34% lower in areas utilized by wolves (P < 0.001). Based on simulation modeling, the realized population growth rate was 0.92 based on fawn survival in the absence of wolves, and 1.06 at sites utilized by wolves. Thus, wolf restoration is predicted to shift the trajectory of the pronghorn population from a declining to an increasing trend. Our results suggest that reintroductions of large carnivores may influence biodiversity through effects on prey populations mediated by mesopredator suppression. In addition, our approach, which combines empirical data on the population of interest with information from other data sources, demonstrates the utility of using simulation modeling to more fully evaluate ecological theories by moving beyond estimating changes in vital rates to analyses of population-level impacts.     

Chronic exposure of arsenic via drinking water and its adverse health impacts on humans

Worldwide chronic arsenic (As) toxicity has become a human health threat. Arsenic exposure to humans mainly occurs from the ingestion of As contaminated water and food. This communication presents a review of current research conducted on the adverse health effects on humans exposed to As-contaminated water. Chronic exposure of As via drinking water causes various types of skin lesions such as melanosis, leucomelanosis, and keratosis. Other manifestations include neurological effects, obstetric problems, high blood pressure, diabetes mellitus, diseases of the respiratory system and of blood vessels including cardiovascular, and cancers typically involving the skin, lung, and bladder. The skin seems to be quite susceptible to the effects of As. Arsenic-induced skin lesions seem to be the most common and initial symptoms of arsenicosis. More systematic studies are needed to determine the link between As exposure and its related cancer and noncancer end points.     

Hidden demographic impacts of fishing and environmental drivers of fecundity in a sea turtle population

Fisheries bycatch is a critical threat to sea turtle populations worldwide, particularly because turtles are vulnerable to multiple gear types. The Canary Current is an intensely fished region, yet there has been no demographic assessment integrating bycatch and population management information of the globally significant Cabo Verde loggerhead turtle (Caretta caretta) population. Using Boa Vista island (Eastern Cabo Verde) subpopulation data from capture–recapture and nest monitoring (2013–2019), we evaluated population viability and estimated regional bycatch rates (2016–2020) in longline, trawl, purse-seine, and artisanal fisheries. We further evaluated current nesting trends in the context of bycatch estimates, existing hatchery conservation measures, and environmental (net primary productivity) variability in turtle foraging grounds. We projected that current bycatch mortality rates would lead to the near extinction of the Boa Vista subpopulation. Bycatch reduction in longline fisheries and all fisheries combined would increase finite population growth rate by 1.76% and 1.95%, respectively. Hatchery conservation increased hatchling production and reduced extinction risk, but alone it could not achieve population growth. Short-term increases in nest counts (2013–2021), putatively driven by temporary increases in net primary productivity, may be masking ongoing long-term population declines. When fecundity was linked to net primary productivity, our hindcast models simultaneously predicted these opposing long-term and short-term trends. Consequently, our results showed conservation management must diversify from land-based management. The masking effect we found has broad-reaching implications for monitoring sea turtle populations worldwide, demonstrating the importance of directly estimating adult survival and that nest counts might inadequately reflect underlying population trends.     

Modeling regional impacts of climate teleconnections using functional data analysis

Teleconnections are quasi-periodic changes in atmospheric circulation that oscillate over long periods of time and impact climate over large regions. These patterns are often linked to long-term variations in climate and extreme weather events and may explain regional differences in climate vulnerability. We apply methods of functional data analysis to examine regional impacts of teleconnections on climate in British Columbia, Canada, between 1951 and 2000. We focus on monthly mean temperature as an overall determinant of crop growth and apply functional principal components analysis (FPCA) to study variations in the impacts of four major teleconnection indices affecting the Northern Hemisphere (the Southern Oscillation Index, the Pacific North American (PNA), Pacific Decadal Oscillation, and the North American Oscillation indices). Two challenges we consider are that the impacts of teleconnections cannot be observed directly and that fine scale data required to study regional variations may come from different sources with highly varied records. We first fit thin-plate regression splines to the raw data to construct complete series of pseudo-data at fixed grid points. Regression models incorporating Bayesian P-splines were then fit to the pseudo-data to estimate the impacts of the four teleconnections over time. Finally, FPCA was then applied to study regional variations in these effects. Our analysis identified strong variations in mean temperature associated with the PNA. The resulting spatial patterns also reveal areas of increased/decreased temperature variability that may have higher climate risk or be suitable for expansion of agricultural activity.     

Modeling the dynamic habitat and breeding population of Southwestern Willow Flycatcher

To aid in the management and conservation of Southwestern Willow Flycatcher (Empidonax traillii extimus, hereafter “Flycatcher”), we developed numerous models of flycatcher breeding habitat at Roosevelt Lake, AZ. For model development and testing, we compiled 10 years of flycatcher territory data that were obtained from intensive fieldwork between 1996 and 2005. We identified riparian vegetation annually in the project area from Landsat Thematic Mapper images, and extracted floodplain features from a digital elevation model. We created a novel class of temporal (i.e., multiyear) variables by characterizing the stability and variability in breeding habitat over a 6-year time interval. We used logistic regression to determine associations between environmental variables and flycatcher territory occurrence, and to test specific hypotheses. We mapped the probability of territory occurrence with a GIS and determined model accuracies with a classification table and a 10-year population database. Environmental features that were associated with breeding flycatchers included floodplain size, proximity to water, and the density, heterogeneity, age and stability of riparian vegetation. Our best model explained 79% of the variability in the flycatcher breeding population at Roosevelt Lake. The majority of predicted flycatcher habitat formed between 1996 and 2004 on an exposed lakebed ～3 years after water levels receded during a prolonged drought. A high correlation between annual reservoir levels and predicted breeding habitat (r = ?0.82) indicates that we can create and manage habitat for conservation purposes. Our predictive models quantify and assess the relative quality of flycatcher breeding habitat remotely, and can be used to evaluate the effectiveness of habitat restoration activities. Numerous techniques we developed can be used to characterize riparian vegetation and patch dynamics directly off of satellite imagery, thereby increasing its utility for conservation purposes.     

Spatial uncertainty analysis of population models

This paper describes an approach for conducting spatial uncertainty analysis of spatial population models, and illustrates the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial population models typically simulate birth, death, and migration on an input map that describes habitat. Typically, only a single “reference” map is available, but we can imagine that a collection of other, slightly different, maps could be drawn to represent a particular species’ habitat. As a first approximation, our approach assumes that spatial uncertainty (i.e., the variation among values assigned to a location by such a collection of maps) is constrained by characteristics of the reference map, regardless of how the map was produced. Our approach produces lower levels of uncertainty than alternative methods used in landscape ecology because we condition our alternative landscapes on local properties of the reference map. Simulated spatial uncertainty was higher near the borders of patches. Consequently, average uncertainty was highest for reference maps with equal proportions of suitable and unsuitable habitat, and no spatial autocorrelation. We used two population viability models to evaluate the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial uncertainty produced larger variation among predictions of a spatially explicit model than those of a spatially implicit model. Spatially explicit model predictions of final female population size varied most among landscapes with enough clustered habitat to allow persistence. In contrast, predictions of population growth rate varied most among landscapes with only enough clustered habitat to support a small population, i.e., near a spatially mediated extinction threshold. We conclude that spatial uncertainty has the greatest effect on persistence when the amount and arrangement of suitable habitat are such that habitat capacity is near the minimum required for persistence.     

Uncertainty analysis of transient population dynamics

Two types of demographic analyses, perturbation analysis and uncertainty analysis, can be conducted to gain insights about matrix population models and guide population management. Perturbation analysis studies how the perturbation of demographic parameters (survival, growth, and reproduction parameters) may affect the population projection, while uncertainty analysis evaluates how much uncertainty there is in population dynamic predictions and where the uncertainty comes from. Previously, both perturbation analysis and uncertainty analysis were conducted on the long-term population growth rate. However, the population may not reach its equilibrium state, especially when there is management by harvesting or hunting. Recently, there has been an increased interest in short-term transient dynamics, which can differ from asymptotic long-term dynamics. There are currently techniques to conduct perturbation analyses of short-term transient dynamics, but no techniques have been proposed for uncertainty analysis of such dynamics. In this study, we introduced an uncertainty analysis technique, the general Fourier Amplitude Sensitivity Test (FAST), to study uncertainties in transient population dynamics. The general FAST is able to identify the amount of uncertainty in transient dynamics and contributions by different demographic parameters. We applied the general FAST to a mountain goat (Oreamnos americanus) matrix population model to give a clear illustration of how uncertainty analysis can be conducted for transient dynamics arising from matrix population models.     

A process-based population dynamics model to explore target and non-target impacts of a biological control agent

The risks and benefits associated with efforts to control invasive alien species using classical biological control are being subjected to increasing scrutiny. A process-based population dynamics model was developed to explore the interactions between a folivorous biological control agent, Cleopus japonicus, and its plant host Buddleja davidii. The model revealed that climate could have a significant impact upon the interactions between B. davidii and C. japonicus. At the coolest sites, the impact of C. japonicus on B. davidii was slowed, but it was still eventually capable of controlling populations of B. davidii. At the warmer sites where both B. davidii and C. japonicus grew faster, B. davidii succumbed rapidly to weevil damage. We hypothesise that barring an encounter with a natural enemy, C. japonicus will eventually be able to provide sustained control B. davidii throughout the North Island of New Zealand. The model scenarios illustrate the potential for the C. japonicus population to attain high densities rapidly, and to defoliate patches of B. davidii, creating the potential for spill-over feeding on non-target plants. The potential magnitude of this threat will depend partly on the climate suitability for C. japonicus, the pattern by which it migrates in response to a reduction in the available leaf resource, and the suitability of non-target plants as hosts. In all migration scenarios considered, the pattern of population growth and resource consumption by C. japonicus was exponential, with a strong tendency toward complete utilisation of resource patches more quickly at the warmer compared to colder sites. In addition to providing some useful hypotheses about the effects of climate on the biological control system, and the non-target risks, it also provides some insight into the mechanisms by which climate affects the system.     

Uncertainty analysis on aquatic environmental impacts of urban land use change

Uncertainties hamper the implementation of strategic environmental assessment (SEA). In order to quantitatively characterize the uncertainties of environmental impacts, this paper develops an integrated methodology through uncertainty analysis on land use change, which combines the scenario analysis approach, stochastic simulation technique, and statistics. Dalian city in China was taken as a case study in the present work. The results predict that the Fuzhou River poses the highest environmental pollution risk with a probability of 89.63% for COD in 2020. Furthermore, the Biliu River, Fuzhou River, Zhuang River, and Dasha River have 100% probabilities for NH₃-N. NH₃-N is a more critical pollutant than COD for all rivers. For COD, industry is the critical pollution source for all rivers except the Zhuang River. For NH₃-N, agriculture is the critical pollution source for the Biliu River, Yingna River, and Dasha River, sewage for the Fuzhou River and Zhuang River, and industry for the Dengsha River. This methodology can provide useful information, such as environmental risk, environmental pressure, and extremely environmental impact, especially under considerations of uncertainties. It can also help to ascertain the significance of each pollution source and its priority for control in urban planning.     

A critical appraisal of population viability analysis

Population viability analysis (PVA) is useful in management of imperiled species. Applications range from research design, threat assessment, and development of management frameworks. Given the importance of PVAs, it is essential that they be rigorous and adhere to widely accepted guidelines; however, the quality of published PVAs is rarely assessed. We evaluated the quality of 160 PVAs of 144 species of birds and mammals published in peer-reviewed journals from 1990 to 2017. We hypothesized that PVA quality would be lower with generic programs than with custom-built programs; be higher for those developed for imperiled species; change over time; and be higher for those published in journals with high impact factors (IFs). Each included study was evaluated based on answers to an evaluation framework containing 32 questions reflecting whether and to what extent the PVA study adhered to published PVA guidelines or contained important PVA components. All measures of PVA quality were generally lower for studies based on generic programs. Conservation status of the species did not affect any measure of PVA quality, but PVAs published in high IF journals were of higher quality. Quality generally declined over time, suggesting the quantitative literacy of PVA practitioners has not increased over time or that PVAs developed by unskilled users are being published in peer-reviewed journals. Only 18.1% of studies were of high quality (score >75%), which is troubling because poor-quality PVAs could misinform conservation decisions. We call for increased scrutiny of PVAs by journal editors and reviewers. Our evaluation framework can be used for this purpose. Because poor-quality PVAs continue to be published, we recommend caution while using PVA results in conservation decision making without thoroughly assessing the PVA quality.