Performance of Greater Sage-Grouse Models for Conservation Assessment in the Interior Columbia Basin, U.S.A.

Abstract: Valid modeling of habitats and populations of Greater Sage-Grouse ( Centrocercus urophasianus) is a critical management need because of increasing concern about population viability. Consequently, we evaluated the performance of two models designed to assess landscape conditions for Greater Sage-Grouse across 13.6 million ha of sagebrush steppe in the interior Columbia Basin and adjacent portions of the Great Basin of the western United States (referred to as the basin). The first model, the environmental index model, predicted conditions at the scale of the subwatershed (mean size of approximately 7800 ha) based on inputs of habitat density, habitat quality, and effects of human disturbance. Predictions ranged on a continuous scale from 0 for lowest environmental index to 2 for optimal environmental index. The second model, the population outcome model, predicted the composite, range-wide conditions for sage grouse based on the contribution of environmental index values from all subwatersheds and measures of range extent and connectivity. Population outcomes were expressed as five classes (A through E) that represented a gradient from continuous, well-distributed populations (outcome A) to sparse, highly isolated populations with a high likelihood of extirpation (outcome E). To evaluate performance, we predicted environmental index values and population outcome classes in areas currently occupied by sage grouse versus areas where extirpation has occurred. Our a priori expectations were that models should predict substantially worse environmental conditions ( lower environmental index) and a substantially higher probability of extirpation ( lower population outcome class) in extirpated areas. Results for both models met these expectations. For example, a population outcome of class E was predicted for extirpated areas, as opposed to class C for occupied areas. These results suggest that our models provided reliable landscape predictions for the conditions tested. This finding is important for conservation planning in the basin, where the models were used to evaluate management of federal lands for sage grouse.     

Joint modelling of breeding and survival in the kittiwake using frailty models

Assessment of population dynamics is central to population dynamics and conservation. In structured populations, matrix population models based on demographic data have been widely used to assess such dynamics. Although highlighted in several studies, the influence of heterogeneity among individuals in demographic parameters and of the possible correlation among these parameters has usually been ignored, mostly because of difficulties in estimating such individual-specific parameters. In the kittiwake (Rissa tridactyla), a long-lived seabird species, differences in survival and breeding probabilities among individual birds are well documented. Several approaches have been used in the animal ecology literature to establish the association between survival and breeding rates. However, most are based on observed heterogeneity between groups of individuals, an approach that seldom accounts for individual heterogeneity. Few attempts have been made to build models permitting estimation of the correlation between vital rates. For example, survival and breeding probability of individual birds were jointly modelled using logistic random effects models by [Cam, E., Link, W.A., Cooch, E.G., Monnat, J., Danchin, E., 2002. Individual covariation in life-history traits: seeing the trees despite the forest. Am. Naturalist, 159, in press]. This is the only example in wildlife animal populations we are aware of. Here we adopt the survival analysis approaches from epidemiology. We model the survival and the breeding probability jointly using a normally distributed random effect (frailty). Conditionally on this random effect, the survival time is modelled assuming a lognormal distribution, and breeding is modelled with a logistic model. Since the deaths are observed in year-intervals, we also take into account that the data are interval censored. The joint model is estimated using classic frequentist methods and also MCMC techniques in Winbugs. The association between survival and breeding attempt is quantified using the standard deviation of the random frailty parameters. We apply our joint model on a large data set of 862 birds, that was followed from 1984 to 1995 in Brittany (France). Survival is positively correlated with breeding indicating that birds with greater inclination to breed also had higher survival.     

A comparison of models for predicting population persistence

We consider a range of models that may be used to predict the future persistence of populations, particularly those based on discrete-state Markov processes. While the mathematical theory of such processes is very well-developed, they may be difficult to work with when attempting to estimate parameters or expected times to extinction. Hence, we focus on diffusion and other approximations to these models, presenting new and recent developments in parameter estimation for density dependent processes, and the calculation of extinction times for processes subject to catastrophes. We illustrate these and other methods using data from simulated and real time series. We give particular attention to a procedure, due to Ross et al. [Ross, J.V., Taimre, T., Pollett, P.K. On parameter estimation in population models, Theor. Popul. Biol., in press], for estimating the parameters of the stochastic SIS logistic model, and demonstrate ways in which these parameters may be used to estimate expected extinction times. Although the stochastic SIS logistic model is strictly density dependent and allows only for birth and death events, it nonetheless may be used to predict extinction times with some accuracy even for populations that are only weakly density dependent, or that are subject to catastrophes.     

Inference about density and temporary emigration in unmarked populations

Few species are distributed uniformly in space, and populations of mobile organisms are rarely closed with respect to movement, yet many models of density rely upon these assumptions. We present a hierarchical model allowing inference about the density of unmarked populations subject to temporary emigration and imperfect detection. The model can be fit to data collected using a variety of standard survey methods such as repeated point counts in which removal sampling, double-observer sampling, or distance sampling is used during each count. Simulation studies demonstrated that parameter estimators are unbiased when temporary emigration is either "completely random" or is determined by the size and location of home ranges relative to survey points. We also applied the model to repeated removal sampling data collected on Chestnut-sided Warblers (Dendroica pensylvancia) in the White Mountain National Forest, U.S.A. The density estimate from our model, 1.09 birds/ha, was similar to an estimate of 1.11 birds/ha produced by an intensive spot-mapping effort. Our model is also applicable when processes other than temporary emigration affect the probability of being available for detection, such as in studies using cue counts. Functions to implement the model have been added to the R package unmarked.     

The stability of trophic mass-balance models of marine ecosystems: a comparative analysis

Dynamic simulations of 18

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mass-balance marine trophic models are used to explore the stability of systems when briefly impacted by a fishery on the key ‘wasp–waist' populations occurring at intermediate trophic levels. The results are related to different ecosystem goal functions previously identified as representative of three attributes of ecosystems development: community complexity, homeostasis and energetics. System recovery time, the time required for all functional groups to returns to baseline level, and here used as a measure of model stability, was inversely correlated to Finn's Cycling Index, i.e. to the fraction of ecosystem throughput that is recycled, and to the mean length of trophic pathways in the systems. Systems with higher capacity to recycle detritus are systems with a higher ability to recover from perturbations. The results are in agreement with the E.P. Odum's theory of ecosystem development, where recycling is interpreted as a chief positive feedback mechanism that contributes to stability in the mature systems by preventing overshoots and destructive oscillations due to external impacts.     

Bayesian Small Area Models for Assessing Wildlife Conservation Risk in Patchy Populations

Abstract:  Species conservation risk assessments require accurate, probabilistic, and biologically meaningful maps of population distribution. In patchy populations, the reasons for discontinuities are not often well understood. We tested a novel approach to habitat modeling in which methods of small area estimation were used within a hierarchical Bayesian framework. Amphibian occurrence was modeled with logistic regression that included third-order drainages as hierarchical effects to account for patchy populations. Models including the random drainage effects adequately represented species occurrences in patchy populations of 4 amphibian species in the Oregon Coast Range (U.S.A.). Amphibian surveys from other locations within the same drainage were used to calibrate local drainage-scale effects. Cross-validation showed that prediction errors for calibrated models were 77% to 86% lower than comparable regionally constructed models, depending on species. When calibration data were unavailable, small area and regional models performed similarly, although poorly. Small area estimation models complement wildlife ecology and habitat studies, and can help managers develop a regional picture of the conservation status for relatively rare species.     

Actual and Potential Use of Population Viability Analyses in Recovery of Plant Species Listed under the U.S. Endangered Species Act

Use of population viability analyses (PVAs) in endangered species recovery planning has been met with both support and criticism. Previous reviews promote use of PVA for setting scientifically based, measurable, and objective recovery criteria and recommend improvements to increase the framework's utility. However, others have questioned the value of PVA models for setting recovery criteria and assert that PVAs are more appropriate for understanding relative trade‐offs between alternative management actions. We reviewed 258 final recovery plans for 642 plants listed under the U.S. Endangered Species Act to determine the number of plans that used or recommended PVA in recovery planning. We also reviewed 223 publications that describe plant PVAs to assess how these models were designed and whether those designs reflected previous recommendations for improvement of PVAs. Twenty‐four percent of listed species had recovery plans that used or recommended PVA. In publications, the typical model was a matrix population model parameterized with ≤5 years of demographic data that did not consider stochasticity, genetics, density dependence, seed banks, vegetative reproduction, dormancy, threats, or management strategies. Population growth rates for different populations of the same species or for the same population at different points in time were often statistically different or varied by >10%. Therefore, PVAs parameterized with underlying vital rates that vary to this degree may not accurately predict recovery objectives across a species’ entire distribution or over longer time scales. We assert that PVA, although an important tool as part of an adaptive‐management program, can help to determine quantitative recovery criteria only if more long‐term data sets that capture spatiotemporal variability in vital rates become available. Lacking this, there is a strong need for viable and comprehensive methods for determining quantitative, science‐based recovery criteria for endangered species with minimal data availability. Uso Actual y Potencial del Análisis de Viabilidad Poblacional para la Recuperación de Especies de Plantas Enlistadas en el Acta de Especies En Peligro de E.U.A     

Introduction of cob(I)alamin as an analytical tool: application to reaction-kinetic studies of oxiranes

The strongly nucleophilic cob(I)alamin, i.e. Vitamin B₁₂ with Co(III) reduced to Co(I), is introduced as a trapping agent in the determination of concentrations of electrophilic reagents. This compound was applied, in comparison with the previously used moderately reactive nicotinamide (H.J.C.F. Nelis and J.E. Sinsheimer (1981). Anal. Biochem., 115, 151.). Oxiranes, metabolites of 1-alkenes, were chosen as model electrophiles. The reagents (nicotinamide and cob(I)alamin) were evaluated in the determination of the rates of reaction toward valine methylamide, a model of N-terminal valines in hemoglobin often used for monitoring of doses in vivo of genotoxic carcinogens. The rate constants for reaction at 37°C with valine methylamide (k _VMA) determined by the cob(I)alamin and nicotinamide procedure, respectively, were for ethylene oxide (1.6, 1.7), propylene oxide (0.9, 1.1), 1,2-epoxybutane (0.7, 0.8) and 1,2-epoxyoctane (0.5, 0.6) M^?1?h^?1, decreasing with increasing number of carbons of the oxirane. Concentrations of oxiranes trapped with nicotinamide are underrated in reaction mixtures containing valine methylamide due to consumption by reaction with the competing nucleophile, a disturbance that is not observed in trapping with cob(I)alamin which reacts about 10⁵ times faster than nicotinamide. Cob(I)alamin which was demonstrated to be an efficient nucleophile for trapping of electrophiles, also in the presence of competing nucleophiles, is promising as an analytical tool in toxicological studies of reactive compounds. Furthermore, cob(I)alamin can be used to detect, measure and compare electrophilic reactivity of chemical substances, a property that is associated with genotoxic potency.     

Development and application of “U.S.E.D.”: A hydroclimate lake stratification model

Two model types are currently in use to model the thermal stratification cycle in lakes and reservoirs: the eddy diffusion and the mixed layer (or integral energy) approaches. Here the former is analysed and developments are proposed to remove the empiricisms previously implicit in these models. These discussions permit the reformulation of K_H0 independently of current shear, together with an expression for R_i. The deduced formulae are in good agreement with observations. The newly formulated model (the University of Salford eddy diffusion model, U.S.E.D.) is subsequently used in simulations of lakes and reservoirs at different latitudes which are found to be in good agreement with observations without requiring inter-site calibration.     

Global population collapse in a superabundant migratory bird and illegal trapping in China

Persecution and overexploitation by humans are major causes of species extinctions. Rare species, often confined to small geographic ranges, are usually at highest risk, whereas extinctions of superabundant species with very large ranges are rare. The Yellow‐breasted Bunting (Emberiza aureola) used to be one of the most abundant songbirds of the Palearctic, with a very large breeding range stretching from Scandinavia to the Russian Far East. Anecdotal information about rapid population declines across the range caused concern about unsustainable trapping along the species’ migration routes. We conducted a literature review and used long‐term monitoring data from across the species’ range to model population trend and geographical patterns of extinction. The population declined by 84.3–94.7% between 1980 and 2013, and the species’ range contracted by 5000 km. Quantitative evidence from police raids suggested rampant illegal trapping of the species along its East Asian flyway in China. A population model simulating an initial harvest level of 2% of the population, and an annual increase of 0.2% during the monitoring period produced a population trajectory that matched the observed decline. We suggest that trapping strongly contributed to the decline because the consumption of Yellow‐breasted Bunting and other songbirds has increased as a result of economic growth and prosperity in East Asia. The magnitude and speed of the decline is unprecedented among birds with a comparable range size, with the exception of the Passenger Pigeon (Ectopistes migratorius), which went extinct in 1914 due to industrial‐scale hunting. Our results demonstrate the urgent need for an improved monitoring of common and widespread species’ populations, and consumption levels throughout East Asia.     

A Critique of the Recovery of Greenback Cutthroat Trout

Abstract: There are no examples of recovery of fish listed under the U.S. Endangered Species Act, but the number of federally threatened greenback cutthroat trout (  Oncorhynchus clarki stomias ) populations is approaching the delisting goal. We evaluated recovery of this subspecies in light of developing theory in conservation biology and with regard to recovery of other salmonids in the inland western United States. Four of the five criteria used to define populations that would count toward delisting appeared to underestimate the risk of extinction of those populations. Typically, recovery goals for numbers of greenback cutthroat trout populations were less stringent than those for other inland salmonids petitioned for listing or listed as threatened under the Endangered Species Act and were comparable to those for a federally endangered species. Before delisting is considered, we propose that historical populations be replicated in additional waters to protect genetic diversity and that existing populations be enlarged to reduce their vulnerability to demographic variation, to increase their access to refugia, and to permit reestablishment of mobile life histories. Existing stocks should also be evaluated to determine whether they represent distinct population segments.     

Elysia timida (Risso, 1818) (Gastropoda, Opisthobranchia): relationship and feeding deterrence to a potential predator on the south-western Mediterranean coast

The relationship between the sacoglossan Elysia timida and the ornate wrasse Thalassoma pavo was studied in two laboratory experiments using artificial models. A feeding-preference experiment was conducted to determine whether mollusc extract deterred feeding by T. pavo, by using a "realistic" mollusc model (imitating the colour pattern of E. timida) coated with mollusc extract, and a reference model (without imitation or extract), and fishes collected from two locations. It was observed that fish approached, attacked and ate more reference models than mollusc models. A second feeding-preference experiment was designed with four different coloured models: "realistic" (W), green (G), red (R) and chequered (S) pattern. Both fish populations clearly rejected the S model, and differed in their colour preferences. Although both populations seemed to prefer the R and G models equally, the W model was clearly rejected by the fish that coexist with the mollusc at one site (Mazarrón), but was not rejected by the other population of fish which does not coexist with it (Cabo de Palos). Mazarrón fishes would identify the W model with the presence of a toxic compound during their coexistence, and therefore avoid attacking conspicuously coloured E. timida models as a response to their visual signals. Therefore, it was concluded that extract of E. timida is a deterrent for T. pavo, and its effect is sufficiently noxious that the fish tend to avoid it, so that the ability of the fishes to learn to recognise colours and identify certain colour patterns associated with obnoxious prey provides the molluscs the opportunity to survive by exhibiting a conspicuous coloration.     

A hierarchical model for spatial capture-recapture data

Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture-recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.     

An estimating function approach to the inference of catch-effort models

A class of catch-effort models, which allows for heterogeneous removal probabilities, is proposed for closed populations. The model includes three types of removal probabilities: multiplicative, Poisson and logistic. The usual removal and generalized removal models then become special cases. The equivalence of the proposed model and a special type of capture-recapture model is discussed. A unified estimating function approach is used to estimate the initial population size. For the homogeneous model, the resulting population size estimator based on optimal estimating functions is asymptotically equivalent to the maximum likelihood estimator. One advantage for our approach is that it can be extended to handle the heterogeneous populations in which the maximum likelihood estimators do not exist. The bootstrap method is applied to construct variance estimators and confidence intervals. We illustrate the method by two real data examples. Results of a simulation study investigating the performance of the proposed estimation procedure are presented.     

Comparison of absolute and relative growth patterns among five <Emphasis Type="Italic">Pinna nobilis</Emphasis> populations along the Tunisian coastline: an information theory approach

The variability in absolute and relative growth of Pinna nobilis along the Tunisian coastline was investigated. Five populations of P. nobilis were sampled, three from northern and two from eastern Tunisia. The specimens were aged and ten morphometric characters were measured on each individual. To test if differences existed in absolute and relative growth patterns among the different populations an information theory approach was followed. For absolute growth, von Bertalanffy, Gompertz, the logistic and the power models were fitted in combination with three assumptions regarding inter-population differences in absolute growth patterns: no differences, differences among all five populations or just between northern and eastern populations. The assumption of common absolute growth parameters among all five populations had the greatest support by the data, whereas the assumption of different growth patterns among all five populations had no support. Von Bertalanffy growth model and the power model were both equally supported by the data (while Gompertz had considerably less support and the logistic model had no support), and thus it may not be definitely concluded whether P. nobilis grows asymptotically or not. The P. nobilis populations of the Tunisian coastline had a slow growth and up to an age of ∼ 9 years their shells were smaller than from all other reported populations in the Mediterranean. For relative growth, apart from the classical allometric model Y = aX ^b , relating the size of a part of a body Y to another reference dimension X, more complicated models were used in combination with the three abovementioned assumptions regarding inter-population differences. Those models, of the form logY = f (logX), either assumed breakpoints in the relative growth trajectories or non-linearities. For most morphometric characters, the classical allometric model had no support by the data and more complicated models were necessary. In most cases, different relative growth either among all five populations or between the northern and eastern population groups was supported by the data. Further investigation is needed to relate the morphological differences observed among different populations of P. nobilis to environmental factors.     

Combining functional data with hierarchical Gaussian process models

Gaussian process models have been used in applications ranging from machine learning to fisheries management. In the Bayesian framework, the Gaussian process is used as a prior for unknown functions, allowing the data to drive the relationship between inputs and outputs. In our research, we consider a scenario in which response and input data are available from several similar, but not necessarily identical, sources. When little information is known about one or more of the populations it may be advantageous to model all populations together. We present a hierarchical Gaussian process model with a structure that allows distinct features for each source as well as shared underlying characteristics. Key features and properties of the model are discussed and demonstrated in a number of simulation examples. The model is then applied to a data set consisting of three populations of Rotifer Brachionus calyciflorus Pallas. Specifically, we model the log growth rate of the populations using a combination of lagged population sizes. The various lag combinations are formally compared to obtain the best model inputs. We then formally compare the leading hierarchical Gaussian process model with the inferential results obtained under the independent Gaussian process model.     

Estimating the abundance of mouse populations of known size: promises and pitfalls of new methods.

Knowledge of animal abundance is fundamental to many ecological studies. Frequently, researchers cannot determine true abundance, and so must estimate it using a method such as mark-recapture or distance sampling. Recent advances in abundance estimation allow one to model heterogeneity with individual covariates or mixture distributions and to derive multimodel abundance estimators that explicitly address uncertainty about which model parameterization best represents truth. Further, it is possible to borrow information on detection probability across several populations when data are sparse. While promising, these methods have not been evaluated using mark-recapture data from populations of known abundance, and thus far have largely been overlooked by ecologists. In this paper, we explored the utility of newly developed mark-recapture methods for estimating the abundance of 12 captive populations of wild house mice (Mus musculus). We found that mark-recapture methods employing individual covariates yielded satisfactory abundance estimates for most populations. In contrast, model sets with heterogeneity formulations consisting solely of mixture distributions did not perform well for several of the populations. We show through simulation that a higher number of trapping occasions would have been necessary to achieve good estimator performance in this case. Finally, we show that simultaneous analysis of data from low abundance populations can yield viable abundance estimates.     

Population Persistence in Florida Torreya: Comparing Modeled Projections of a Declining Coniferous Tree

Abstract: The Florida torreya (   Torreya taxifolia ) is a coniferous tree endemic to a 35-km stretch of bluffs and ravines along the east side of the Apalachicola River in northern Florida and southern Georgia. This formerly locally abundant tree has declined as a result of disease during the 1950s and is on the U.S. endangered species list. With no seed production in the wild, this species is headed toward extinction. We conducted a survey on roughly 200 trees from 1988 to 1996 and used these data to estimate the likelihood of population persistence during the next several decades. We compared a stage-class transition matrix model ( RAMAS) and an individual-based stochastic model ( TORSIM) of growth and mortality to project future populations. Given the current lack of seed production in the wild, all models predict extinction. The question of concern is the imminence of this predicted extinction. Differing predicted times to extinction would suggest different immediate management recommendations. Both models predicted an over 90% likelihood of persistence during the next 50 years. Predictions differed in that the transition matrix model was less optimistic than the individual-based model regarding persistence. Model sensitivity analysis showed that the results were robust to significant decreases in growth and sprouting probabilities. Submodels identified different persistence likelihoods in different populations. Nonetheless, unless management of the population can facilitate maturation and seed production, extinction of this species in the wild is inevitable.     

Analytical Solutions to Trade‐Offs between Size of Protected Areas and Land‐Use Intensity

Abstract: Land‐use change is affecting Earth's capacity to support both wild species and a growing human population. The question is how best to manage landscapes for both species conservation and economic output. If large areas are protected to conserve species richness, then the unprotected areas must be used more intensively. Likewise, low‐intensity use leaves less area protected but may allow wild species to persist in areas that are used for market purposes. This dilemma is present in policy debates on agriculture, housing, and forestry. Our goal was to develop a theoretical model to evaluate which land‐use strategy maximizes economic output while maintaining species richness. Our theoretical model extends previous analytical models by allowing land‐use intensity on unprotected land to influence species richness in protected areas. We devised general models in which species richness (with modified species‐area curves) and economic output (a Cobb–Douglas production function) are a function of land‐use intensity and the proportion of land protected. Economic output increased as land‐use intensity and extent increased, and species richness responded to increased intensity either negatively or following the intermediate disturbance hypothesis. We solved the model analytically to identify the combination of land‐use intensity and protected area that provided the maximum amount of economic output, given a target level of species richness. The land‐use strategy that maximized economic output while maintaining species richness depended jointly on the response of species richness to land‐use intensity and protection and the effect of land use outside protected areas on species richness within protected areas. Regardless of the land‐use strategy, species richness tended to respond to changing land‐use intensity and extent in a highly nonlinear fashion.     

Improving effectiveness of systematic conservation planning with density data

Systematic conservation planning aims to design networks of protected areas that meet conservation goals across large landscapes. The optimal design of these conservation networks is most frequently based on the modeled habitat suitability or probability of occurrence of species, despite evidence that model predictions may not be highly correlated with species density. We hypothesized that conservation networks designed using species density distributions more efficiently conserve populations of all species considered than networks designed using probability of occurrence models. To test this hypothesis, we used the Zonation conservation prioritization algorithm to evaluate conservation network designs based on probability of occurrence versus density models for 26 land bird species in the U.S. Pacific Northwest. We assessed the efficacy of each conservation network based on predicted species densities and predicted species diversity. High‐density model Zonation rankings protected more individuals per species when networks protected the highest priority 10‐40% of the landscape. Compared with density‐based models, the occurrence‐based models protected more individuals in the lowest 50% priority areas of the landscape. The 2 approaches conserved species diversity in similar ways: predicted diversity was higher in higher priority locations in both conservation networks. We conclude that both density and probability of occurrence models can be useful for setting conservation priorities but that density‐based models are best suited for identifying the highest priority areas. Developing methods to aggregate species count data from unrelated monitoring efforts and making these data widely available through ecoinformatics portals such as the Avian Knowledge Network will enable species count data to be more widely incorporated into systematic conservation planning efforts.