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At least 19 recordsLinked to original sources
USGS2007

Ohio Aquatic Gap Analysis-An Assessment of the Biodiversity and Conservation Status of Native Aquatic Animal Species

The goal of the GAP Analysis Program is to keep common species common by identifying those species and habitats that are not yet adequately represented in the existing matrix of conservation lands. The Gap Analysis Program (GAP) is sponsored by the Biological Resources Discipline of the U.S. Geological Survey (USGS). The Ohio Aquatic GAP (OH-GAP) is a pilot project that is applying the GAP concept to aquatic-specifically, riverine-data. The mission of GAP is to provide regional assessments of the conservation status of native animal species and to facilitate the application of this information to land-management activities. OH-GAP accomplished this through * mapping aquatic habitat types, * mapping the predicted distributions of fish, crayfish, and bivalves, * documenting the presence of aquatic species in areas managed for conservation, * providing GAP results to the public, planners, managers, policy makers, and researchers, and * building cooperation with multiple organizations to apply GAP results to state and regional management activities. Gap analysis is a coarse-scale assessment of aquatic biodiversity and conservation; the goal is to identify gaps in the conservation of native aquatic species. It is not a substitute for biological field studies and monitoring programs. Gap analysis was conducted for the continuously flowing streams in Ohio. Lakes, reservoirs, wetlands, and the Lake Erie islands were not included in this analysis. The streams in Ohio are in the Lake Erie and Ohio River watersheds and pass through six of the level III ecoregions defined by Omernik: the Eastern Corn Belt Plains, Southern Michigan/Northern Indiana Drift Plains, Huron/Erie Lake Plain, Erie Drift Plains, Interior Plateau, and the Western Allegheny Plateau. To characterize the aquatic habitats available to Ohio fish, crayfish, and bivalves, a classification system needed to be developed and mapped. The process of classification includes delineation of areas of relative homogeneity and labeling these areas using categories defined by the classification system. The variables were linked to the 1:100,000-scale streams of the National Hydrography Dataset of the USGS. Through discussions with Ohio aquatic experts, OH-GAP identified eight separate enduring physical features which, when combined, form the physical habitat type: * Shreve link (a measure of stream size) * Downstream Shreve link (a measure of stream connectivity and size) * Sinuosity * Gradient * Bedrock * Stream temperature * Character of glacial drift * Glacial-drift thickness Potential distribution models were developed for 130 fish, 70 bivalve, and 17 native crayfish species. These models are based on 5,686 fish, 4,469 crayfish, and 2,899 freshwater bivalve (mussels and clams) sampling locations, the variables describing the physical habitat types, and variables indicating the major drainage basins and Omernik's Level III ecoregion. All potential species distributions are displayed and analyzed at the 14-digit hydrologic unit (14-HUs), or subwatershed, level. Mainland Ohio contains 1,749 14-HUs. All statistics and conclusions, as well as spatial data, are discussed and presented in terms of these units. The Ohio Aquatic Gap Analysis Project compiled a map of public and private conservation lands and OH-GAP classified the lands into four status categories (status 1 through status 4) by the degree of protection offered based on management practices. A status of 1 denotes the highest, most permanent level of maintenance, and status 4 represents the lowest level of biodiversity management, or unknown status. The results of this mapping show that only about 3.7 percent of the state's land (4.3 percent if lakes and reservoirs are also included) is protected for conservation, either publicly or privately. Of this total, state agencies control about 52 percent, and Federal agencies control about 29 percent. Conservation areas that presently protect

Open-File Report↗
USGS2015

Emerging technologies to conserve biodiversity

Technologies to identify individual animals, follow their movements, identify and locate animal and plant species, and assess the status of their habitats remotely have become better, faster, and cheaper as threats to the survival of species are increasing. New technologies alone do not save species, and new data create new problems. For example, improving technologies alone cannot prevent poaching: solutions require providing appropriate tools to the right people. Habitat loss is another driver: the challenge here is to connect existing sophisticated remote sensing with species occurrence data to predict where species remain. Other challenges include assembling a wider public to crowdsource data, managing the massive quantities of data generated, and developing solutions to rapidly emerging threats.

Trends in Ecology and Evolution↗
USGS2008

Monitoring programs to assess reintroduction efforts: A critical component in recovery

Reintroduction is a powerful tool in our conservation toolbox. However, the necessary follow-up, i.e. long-term monitoring, is not commonplace and if instituted may lack rigor. We contend that valid monitoring is possible, even with sparse data. We present a means to monitor based on demographic data and a projection model using the Wyoming toad (Bufo baxten) as an example. Using an iterative process, existing data is built upon gradually such that demographic estimates and subsequent inferences increase in reliability. Reintroduction and defensible monitoring may become increasingly relevant as the outlook for amphibians, especially in tropical regions, continues to deteriorate and emergency collection, captive breeding, and reintroduction become necessary. Rigorous use of appropriate modeling and an adaptive approach can validate the use of reintroduction and substantially increase its value to recovery programs. ?? 2008 Museu de Cie??ncies Naturals.

Animal Biodiversity and Conservation↗
USGS2004

Costs of detection bias in index-based population monitoring

Managers of wildlife populations commonly rely on indirect, count-based measures of the population in making decisions regarding conservation, harvest, or control. The main appeal in the use of such counts is their low material expense compared to methods that directly measure the population. However, their correct use rests on the rarely-tested but often-assumed premise that they proportionately reflect population size, i.e., that they constitute a population index. This study investigates forest management for the endangered Red-cockaded Woodpecker (Picoides borealis) and the Wood Thrush (Hylocichla mustelina) at the Piedmont National Wildlife Refuge in central Georgia, U.S.A. Optimal decision policies for a joint species objective were derived for two alternative models of Wood Thrush population dynamics. Policies were simulated under scenarios of unbiasedness, consistent negative bias, and habitat-dependent negative bias in observed Wood Thrush densities. Differences in simulation outcomes between biased and unbiased detection scenarios indicated the expected loss in resource objectives (here, forest habitat and birds) through decision-making based on biased population counts. Given the models and objective function used in our analysis, expected losses were as great as 11%, a degree of loss perhaps not trivial for applications such as endangered species management. Our analysis demonstrates that costs of uncertainty about the relationship between the population and its observation can be measured in units of the resource, costs which may offset apparent savings achieved by collecting uncorrected population counts.

Animal Biodiversity and Conservation↗
USGS2004

Abundance estimation and conservation biology

Abundance is the state variable of interest in most population–level ecological research and in most programs involving management and conservation of animal populations. Abundance is the single parameter of interest in capture–recapture models for closed populations (e.g., Darroch, 1958; Otis et al., 1978; Chao, 2001). The initial capture–recapture models developed for partially (Darroch, 1959) and completely (Jolly, 1965; Seber, 1965) open populations represented efforts to relax the restrictive assumption of population closure for the purpose of estimating abundance. Subsequent emphases in capture–recapture work were on survival rate estimation in the 1970’s and 1980’s (e.g., Burnham et al., 1987; Lebreton et al.,1992), and on movement estimation in the 1990’s (Brownie et al., 1993; Schwarz et al., 1993). However, from the mid–1990’s until the present time, capture–recapture investigators have expressed a renewed interest in abundance and related parameters (Pradel, 1996; Schwarz & Arnason, 1996; Schwarz, 2001). The focus of this session was abundance, and presentations covered topics ranging from estimation of abundance and rate of change in abundance, to inferences about the demographic processes underlying changes in abundance, to occupancy as a surrogate of abundance. The plenary paper by Link & Barker (2004) is provocative and very interesting, and it contains a number of important messages and suggestions. Link & Barker (2004) emphasize that the increasing complexity of capture–recapture models has resulted in large numbers of parameters and that a challenge to ecologists is to extract ecological signals from this complexity. They offer hierarchical models as a natural approach to inference in which traditional parameters are viewed as realizations of stochastic processes. These processes are governed by hyperparameters, and the inferential approach focuses on these hyperparameters. Link & Barker (2004) also suggest that our attention should be focused on relationships between demographic processes such as survival and recruitment, the two quantities responsible for changes in abundance, rather than simply on the magnitudes of these quantities. They describe a type of Jolly–Seber capture–recapture model that permits inference about the underlying relationship between per capita recruitment rates and survival rates (Link & Barker, this volume). Implementation used Bayesian Markov Chain Monte Carlo methods and appeared to work well, yielding inferences about the relationship between recruitment and survival that were robust to selection of prior distribution. We believe that readers will find their arguments compelling, and we expect to see increased use of hierarchical modeling approaches in capture–recapture and related fields. Otto (presentation without paper) also recommended use of hierarchical models in analysis of multiple data sources dealing with population dynamics of North American mallards. He integrated survival inferences from ringing data, abundance information from aerial survey data, and recruitment information based on age ratios from a harvest survey. He used a Leslie matrix population projection model as an integrating framework and obtained estimates of breeding population size using all data.Otto’s approach also permitted inference about biases in estimated quantities. As with the work of Link & Barker (2004), we find Otto’s recommendation to use hierarchical models to integrate data from multiple sources to be very compelling. Alisauskas et al. (2004) report results of an analysis of capture–recapture data for a askatchewan population of white–winged scoters. They used the approach of Pradel (1996) to estimate population growth rate (See the PDF) directly. Estimates for 1975–1985 were quite low, but estimates for the recent period, 2000–2003,increased to values > 1. Parameter estimates for seniority, survival and per capita recruitment (Pradel, 1996) led to the inference that increased recruitment was largely responsible for the improvements in population status and growth. However, various data sources also indicated that this increase in recruitment was likely a result of increased immigration rather than improved reproduction on the area. This latter inference is important from a conservation perspective in indicating the importance of birds in other locations to growth and health of the study population. Lukacs and Burnham presented material to be published elsewhere that dealt with the use of genetic markers in capture–recapture studies. The data sources for such studies are samples of hair or feces, which are then analyzed using molecular genetic techniques in order to determine individual genotypes with respect to a usually small number of loci. Two types of classification error can arise in such analyses. First, if only a small number of loci is examined, then there may be nonnegligible probabilities that multiple individual animals will have the same genotypes. The second type of error arises during the polymerase chain reaction (PCR) process and can result from failure of alleles to amplify (allelic dropout) or from PCR inhibitors in hair and feces that produce the appearance of false alleles or misprinting (Creel et al., 2003). Lukacs and Burnham developed models that formally incorporate possible misclassification of samples resulting from these errors. These models permit estimation of parameters such as abundance and survival in a manner that properly incorporates this uncertainty of individual identity. We anticipate that noninvasive sampling based on molecular genetic analyses of hair or feces will become extremely important for some species, and that the models of Lukacs and Burnham will become very popular for such analyses. MacKenzie & Nichols (2004) discuss the use of occupancy (proportion of patches or habitat area that is occupied) as a surrogate for abundance. In cases of territorial species and where birds occur at low densities, the number of occupied patches may provide a reasonable estimate of abundance. In other cases, occupancy can be viewed as providing information about one tail of the abundance distribution, P (N = 0). The motivation for considering occupancy as a surrogate for abundance is that occupancy is based on so–called presence–absence surveys that are frequently less expensive of time and effort than methods that estimate abundance directly. We describe one set of models that can be used to estimate occupancy for a single season and another that can be used to estimate parameters such as local probabilities of extinction and colonization that are associated with occupancy dynamics. We outline a possible hybrid approach that combines occupancy data with data on marked individuals in order to betterexplore the mechanisms underlying occupancy dynamics. These five presentations made for an interesting session containing useful information and recommendations for future work. A number of themes connecting these presentations could be emphasized. For example, two of the presentations considered alternatives to standard capture–recapture sampling that can be used to draw inferences about abundance, or a portion of the abundance distribution, with field methods that should be less expensive than usual capture–recapture approaches of handling animals. We believe that the most important theme of the session was the emphasis on the processes responsible for changes in abundance. In particular, we are excited by the potential for using hierarchical models as a means of investigating relationships among vital rates and as a means of combining multiple sources of data relevant to system dynamics. Indeed, we expect the importance of this session theme to be reflected in the content and presentations of the next EURING meeting.

Animal Biodiversity and Conservation↗
USGS2018

Raptor nest-site use in relation to the proximity of coalbed methane development

Raptor nest–site use in relation to the proximity of coalbed–methane development. Energy development such as coalbed–methane (CBM) extraction is a major land use with largely unknown consequences for many animal species. Some raptor species may be especially vulnerable to habitat changes due to energy development given their ecological requirements and population trajectories. Using 12,977 observations of 3,074 nests of 12 raptor species across nine years (2003–2011) in the Powder River Basin, Wyoming, USA, we evaluated relationships between raptor nest–site use and CBM development. Our objectives were to determine temporal trends in nest–use rates, and whether nest–site use was related to the proximity of CBM development. Across the study area, nest–use rates varied across species and years in a non–linear fashion. We developed a novel randomization test to assess differences in use between nests at developed and undeveloped sites, while controlling for annual variation in nest–site use. Red–tailed hawks (Buteo jamaicensis), burrowing owls (Athene cunicularia), and long–eared owls (Asio otus) used nests in undeveloped areas more than nests in developed areas (i.e. nests near CBM development). Differences between development groups were equivocal for the remaining nine species; however, we caution that we likely had lower statistical power to detect differences for rarer species. Our findings suggest potential avoidance of nesting in areas near CBM development by some species and reveal that CBM effects may be fairly consistent across distances between 400–2,415 m from wells. Future work should consider habitat preferences and fitness outcomes, and control for other key factors such as local prey availability, raptor densities, and weather.

Wyoming↗
USGS2004

Coping with unobservable and mis-classified states in capture-recapture studies

Multistate mark-recapture methods provide an excellent conceptual framework for considering estimation in studies of marked animals. Traditional methods include the assumptions that (1) each state an animal occupies is observable, and (2) state is assigned correctly at each point in time. Failure of either of these assumptions can lead to biased estimates of demographic parameters. I review design and analysis options for minimizing or eliminating these biases. Unobservable states can be adjusted for by including them in the state space of the statistical model, with zero capture probability, and incorporating the robust design, or observing animals in the unobservable state through telemetry, tag recoveries, or incidental observations. Mis-classification can be adjusted for by auxiliary data or incorporating the robust design, in order to estimate the probability of detecting the state an animal occupies. For both unobservable and mis-classified states, the key feature of the robust design is the assumption that the state of the animal is static for at least two sampling occasions.

Animal Biodiversity and Conservation↗
USGS2004

Demographic estimation methods for plants with dormancy

Demographic studies in plants appear simple because unlike animals, plants do not run away. Plant individuals can be marked with, e.g., plastic tags, but often the coordinates of an individual may be sufficient to identify it. Vascular plants in temperate latitudes have a pronounced seasonal life–cycle, so most plant demographers survey their study plots once a year often during or shortly after flowering. Life–states are pervasive in plants, hence the results of a demographic study for an individual can be summarized in a familiar encounter history, such as 0VFVVF000. A zero means that an individual was not seen in a year and a letter denotes its state for years when it was seen aboveground. V and F here stand for vegetative and flowering states, respectively. Probabilities of survival and state transitions can then be obtained by mere counting. Problems arise when there is an unobservable dormant state, i.e., when plants may stay belowground for one or more growing seasons. Encounter histories such as 0VF00F000 may then occur where the meaning of zeroes becomes ambiguous. A zero can either mean a dead or a dormant plant. Various ad hoc methods in wide use among plant ecologists have made strong assumptions about when a zero should be equated to a dormant individual. These methods have never been compared among each other. In our talk and in Kéry et al. (submitted), we show that these ad hoc estimators provide spurious estimates of survival and should not be used. In contrast, if detection probabilities for aboveground plants are known or can be estimated, capturerecapture (CR) models can be used to estimate probabilities of survival and state–transitions and the fraction of the population that is dormant. We have used this approach in two studies of terrestrial orchids, Cleistes bifaria (Kéry et al., submitted) and Cypripedium reginae (Kéry & Gregg, submitted) in West Virginia, U.S.A. For Cleistes, our data comprised one population with a total of 620 marked ramets over 10 years, and for Cypripedium , two populations with 98 and 258 marked ramets over 11 years. We chose the ramet (= single stem or shoot) as the demographic unit of our study since there was no way distinguishing among genets (genet = genetical individual, i.e., the “individual” that animal ecologists are mostly concerned with). This will introduce some non–independence into the data, which can nevertheless be dealt with easily by correcting variances for overdispersion. Using ramets instead of genets has the further advantage that individuals can be assigned to a state such as flowering or vegetative in an unambiguous manner. This is not possible when genets are the demographic units. In all three populations, auxiliary data was available to show that detection probability of aboveground plants was m 0.995 We fitted multistate models in program MARK by specifying three states (D, V, F), even though the dormant state D does not occur in the encounter histories. Detection probability is fixed at 1 for the vegetative (V) and the flowering state (F) and at zero for the dormant state (D). Rates of survival and of state transitions as well as slopes of covariate relationships can be estimated and LRT or the AIC machinery be used to select among models. To estimate the fraction of the population in the unobservable dormant state, the encounter histories are collapsed to 0 (plant not observed aboveground) and 1 (plant observed aboveground). The Cormack–Jolly–Seber model without constraints on detection probability is used to estimate detection probability, the complement of which is the estimated fraction of the population in the dormant state. Parameter identifiability is an important issue in multi state models. We used the Catchpole–Morgan–Freeman approach to determine which parameters are estimable in principle in our multi state models. Most of 15 tested models were indeed estimable with the notable exception of the most general model, which has fully interactive state- and time-dependent survival and state transition rates. This model would become identifiable if at least some plants would be excavated in years when they do not show up aboveground. Our analyses for three analyzed populations of Cleistes and Cypripedium yielded annual ramet survival rates ranging from 0.86–0.96. Estimates of the average fraction dormant ranged from 0.02–0.30, but with up to half a population in the dormant state in some years. Ultrastructural modeling enables interesting hypotheses to be tested about the relationships of demographic rates with climatic covariates for instance. Such covariate modeling makes the CR approach particularly interesting for evolutionary–ecological questions about, e.g., the adaptive significance of the dormant state.

Animal Biodiversity and Conservation↗
USGS2004

Individual heterogeneity and identifiability in capture-recapture models

Individual heterogeneity in detection probabilities is a far more serious problem for capture-recapture modeling than has previously been recognized. In this note, I illustrate that population size is not an identifiable parameter under the general closed population mark-recapture model Mh. The problem of identifiability is obvious if the population includes individuals with pi = 0, but persists even when it is assumed that individual detection probabilities are bounded away from zero. Identifiability may be attained within parametric families of distributions for pi , but not among parametric families of distributions. Consequently, in the presence of individual heterogeneity in detection probability, capture-recapture analysis is strongly model dependent.

Animal Biodiversity and Conservation↗
USGS2004

Computing and software

The reality is that the statistical methods used for analysis of data depend upon the availability of software. Analysis of marked animal data is no different than the rest of the statistical field. The methods used for analysis are those that are available in reliable software packages. Thus, the critical importance of having reliable, up–to–date software available to biologists is obvious. Statisticians have continued to develop more robust models, ever expanding the suite of potential analysis methods available. But without software to implement these newer methods, they will languish in the abstract, and not be applied to the problems deserving them. In the Computers and Software Session, two new software packages are described, a comparison of implementation of methods for the estimation of nest survival is provided, and a more speculative paper about how the next generation of software might be structured is presented. Rotella et al. (2004) compare nest survival estimation with different software packages: SAS logistic regression, SAS non–linear mixed models, and Program MARK. Nests are assumed to be visited at various, possibly infrequent, intervals. All of the approaches described compute nest survival with the same likelihood, and require that the age of the nest is known to account for nests that eventually hatch. However, each approach offers advantages and disadvantages, explored by Rotella et al. (2004). Efford et al. (2004) present a new software package called DENSITY. The package computes population abundance and density from trapping arrays and other detection methods with a new and unique approach. DENSITY represents the first major addition to the analysis of trapping arrays in 20 years. Barker & White (2004) discuss how existing software such as Program MARK require that each new model’s likelihood must be programmed specifically for that model. They wishfully think that future software might allow the user to combine pieces of likelihood functions together to generate estimates. The idea is interesting, and maybe some bright young statistician can work out the specifics to implement the procedure. Choquet et al. (2004) describe MSURGE, a software package that implements the multistate capture–recapture models. The unique feature of MSURGE is that the design matrix is constructed with an interpreted language called GEMACO. Because MSURGE is limited to just multistate models, the special requirements of these likelihoods can be provided. The software and methods presented in these papers gives biologists and wildlife managers an expanding range of possibilities for data analysis. Although ease–of–use is generally getting better, it does not replace the need for understanding of the requirements and structure of the models being computed. The internet provides access to many free software packages as well as user–discussion groups to share knowledge and ideas. (A starting point for wildlife–related applications is (http://www.phidot.org).

Animal Biodiversity and Conservation↗
USGS2004

Evolution of quantitative methods for the study and management of avian populations: on the importance of individual contributions

The EURING meetings and the scientists who have attended them have contributed substantially to the growth of knowledge in the field of estimating parameters of animal populations. The contributions of David R. Anderson to process modeling, parameter estimation and decision analysis are briefly reviewed. Metrics are considered for assessing individual contributions to a field of inquiry, and it is concluded that Anderson’s contributions have been substantial. Important characteristics of Anderson and his career are the ability to identify and focus on important topics, the premium placed on dissemination of new methods to prospective users, the ability to assemble teams of complementary researchers, and the innovation and vision that characterized so much of his work. The paper concludes with a list of interesting current research topics for consideration by EURING participants.

Animal Biodiversity and Conservation↗
USGS2004

DENSITY: software for analysing capture-recapture data from passive detector arrays

A general computer-intensive method is described for fitting spatial detection functions to capture-recapture data from arrays of passive detectors such as live traps and mist nets. The method is used to estimate the population density of 10 species of breeding birds sampled by mist-netting in deciduous forest at Patuxent Research Refuge, Laurel, Maryland, U.S.A., from 1961 to 1972. Total density (9.9 ? 0.6 ha-1 mean ? SE) appeared to decline over time (slope -0.41 ? 0.15 ha-1y-1). The mean precision of annual estimates for all 10 species pooled was acceptable (CV(D) = 14%). Spatial analysis of closed-population capture-recapture data highlighted deficiencies in non-spatial methodologies. For example, effective trapping area cannot be assumed constant when detection probability is variable. Simulation may be used to evaluate alternative designs for mist net arrays where density estimation is a study goal.

Animal Biodiversity and Conservation↗
USGS2004

Generalized estimators of avian abundance from count survey data

I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture?recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations) constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed.

Animal Biodiversity and Conservation↗
USGS2004

Hierarchial mark-recapture models: a framework for inference about demographic processes

The development of sophisticated mark-recapture models over the last four decades has provided fundamental tools for the study of wildlife populations, allowing reliable inference about population sizes and demographic rates based on clearly formulated models for the sampling processes. Mark-recapture models are now routinely described by large numbers of parameters. These large models provide the next challenge to wildlife modelers: the extraction of signal from noise in large collections of parameters. Pattern among parameters can be described by strong, deterministic relations (as in ultrastructural models) but is more flexibly and credibly modeled using weaker, stochastic relations. Trend in survival rates is not likely to be manifest by a sequence of values falling precisely on a given parametric curve; rather, if we could somehow know the true values, we might anticipate a regression relation between parameters and explanatory variables, in which true value equals signal plus noise. Hierarchical models provide a useful framework for inference about collections of related parameters. Instead of regarding parameters as fixed but unknown quantities, we regard them as realizations of stochastic processes governed by hyperparameters. Inference about demographic processes is based on investigation of these hyperparameters. We advocate the Bayesian paradigm as a natural, mathematically and scientifically sound basis for inference about hierarchical models. We describe analysis of capture-recapture data from an open population based on hierarchical extensions of the Cormack-Jolly-Seber model. In addition to recaptures of marked animals, we model first captures of animals and losses on capture, and are thus able to estimate survival probabilities w (i.e., the complement of death or permanent emigration) and per capita growth rates f (i.e., the sum of recruitment and immigration rates). Covariation in these rates, a feature of demographic interest, is explicitly described in the model.

Animal Biodiversity and Conservation↗
USGS2004

Occupancy as a surrogate for abundance estimation

In many monitoring programmes it may be prohibitively expensive to estimate the actual abundance of a bird species in a defined area, particularly at large spatial scales, or where birds occur at very low densities. Often it may be appropriate to consider the proportion of area occupied by the species as an alternative state variable. However, as with abundance estimation, issues of detectability must be taken into account in order to make accurate inferences: the non?detection of the species does not imply the species is genuinely absent. Here we review some recent modelling developments that permit unbiased estimation of the proportion of area occupied, colonization and local extinction probabilities. These methods allow for unequal sampling effort and enable covariate information on sampling locations to be incorporated. We also describe how these models could be extended to incorporate information from marked individuals, which would enable finer questions of population dynamics (such as turnover rate of nest sites by specific breeding pairs) to be addressed. We believe these models may be applicable to a wide range of bird species and may be useful for investigating various questions of ecological interest. For example, with respect to habitat quality, we might predict that a species is more likely to have higher local extinction probabilities, or higher turnover rates of specific breeding pairs, in poor quality habitats.

Animal Biodiversity and Conservation↗
USGS2004

Evaluation of ultrastructure and random effects band recovery models for estimating relationships between survival and harvest rates in exploited populations

Increased population survival rate after an episode of seasonal exploitation is considered a type of compensatory population response. Lack of an increase is interpreted as evidence that exploitation results in added annual mortality in the population. Despite its importance to management of exploited species, there are limited statistical techniques for comparing relative support for these two alternative models. For exploited bird species, the most common technique is to use a fixed effect, deterministic ultrastructure model incorporated into band recovery models to estimate the relationship between harvest and survival rate. We present a new likelihood-based technique within a framework that assumes that survival and harvest are random effects that covary through time. We conducted a Monte Carlo simulation study under this framework to evaluate the performance of these two techniques. The ultrastructure models performed poorly in all simulated scenarios, due mainly to pathological distributional properties. The random effects estimators and their associated estimators of precision had relatively small negative bias under most scenarios, and profile likelihood intervals achieved nominal coverage. We suggest that the random effects estimation method approach has many advantages compared to the ultrastructure models, and that evaluation of robustness and generalization to more complex population structures are topics for additional research. ?? 2004 Museu de Cie??ncies Naturals.

Animal Biodiversity and Conservation↗
USGS2004

Multi-state analysis of the impacts of avian pox on a population of Serins (Serinus serinus): The importance of estimating recapture rates

Disease is one of the evolutionary forces shaping populations. Recent studies have shown that epidemics like avian pox, malaria, or mycoplasmosis have affected passerine population dynamics, being responsible for the decline of some populations or disproportionately killing males and larger individuals and thus selecting for specific morphotypes. However, few studies have estimated the effects of an epidemic by following individual birds using the capture-recapture approach. Because avian pox can be diagnosed by direct examination of the birds, we are here able to analyze, using multistate models, the development and consequences of an avian pox epidemic affecting in 1996, a population of Serins (Serinus serinus) in northeastern Spain. The epidemics lasted from June to the end of November of 1996, with a maximum apparent prevalence rate > 30% in October. However, recapture rate of sick birds was very high (0.81, range 0.37-0.93) compared to that of healthy birds (0.21, range 0.020-32), which highly inflated apparent prevalence rate. This was additionally supported by the low predicted transition from the state of being uninfected to the state of being infected (0.03, SE 0.03). Once infected, Serin avian pox was very virulent with (15-day) survival rate of infected birds being of only 0.46 (SE 0.17) compared to that of healthy ones (0.87, SE 0.03). Probability of recovery from disease, provided that the bird survived the first two weeks, however, was very high (0.65, SE 0.25). The use of these estimates together with a simple model, allowed us to predict an asymptotic increase to prevalence of about 4% by the end of the outbreak period, followed by a sharp decline, with the only remaining infestations being infected birds that had not yet recovered. This is in contrast to the apparent prevalence of pox and stresses the need to estimate recapture rates when estimating population dynamics parameters. ?? 2004 Museu de Cie??ncies Naturals.

Animal Biodiversity and Conservation↗