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The development of methods for dealing with continuous data with a spike at zero has lagged behind those for overdispersed or zero-inflated count data. We consider longitudinal ecological data corresponding to an annual average of 26 weekly maximum counts of birds, and are hence effectively continuous, bounded below by zero but also with a discrete mass at zero. We develop a Bayesian hierarchical Tweedie regression model that can directly accommodate the excess number of zeros common to this type of data, whilst accounting for both spatial and temporal correlation. Implementation of the model is conducted in a Markov chain Monte Carlo (MCMC) framework, using reversible jump MCMC to explore uncertainty across both parameter and model spaces. This regression modelling framework is very flexible and removes the need to make strong assumptions about mean-variance relationships a priori. It can also directly account for the spike at zero, whilst being easily applicable to other types of data and other model formulations. Whilst a correlative study such as this cannot prove causation, our results suggest that an increase in an avian predator may have led to an overall decrease in the number of one of its prey species visiting garden feeding stations in the United Kingdom. This may reflect a change in behaviour of house sparrows to avoid feeding stations frequented by sparrowhawks, or a reduction in house sparrow population size as a result of sparrowhawk increase. [ABSTRACT FROM AUTHOR]

Despite > 60 years of conservation in Uganda's national parks the populations of lions and spotted hyaenas in these areas have never been estimated using a census method. Estimates for some sites have been extrapolated to other protected areas and educated guesses have been made but there has been nothing more definitive. We used a lure count analysis method of call-up counts to estimate populations of the lion Panthera leo and spotted hyaena Crocuta crocuta in the parks where reasonable numbers of these species exist: Queen Elizabeth Protected Area, Murchison Falls Conservation Area and Kidepo Valley National Park. We estimated a total of 408 lions and 324 hyaenas for these three conservation areas. It is unlikely that other conservation areas in Uganda host > 10 lions or > 40 hyaenas. The Queen Elizabeth Protected Area had the largest populations of lions and hyaenas: 140 and 211, respectively. It is estimated that lion numbers have declined by 30% in this protected area since the late 1990s and there are increasing concerns for the long-term viability of both species in Uganda. [ABSTRACT FROM AUTHOR]

It is important to discern the magnitude of density dependence a species exhibits, as well as the time lag over which it operates. Knowledge of a species' likely response to natural as well as synthetic shocks will assist in effective species management. Statistically this is a challenging problem which does not usually admit closed-form mathematical analysis. Consequently, many people have used Bayesian methods to fit state space models of density dependence to many different species, of which we take eleven species of North American duck as our motivating examples. A Bayesian analysis requires a choice of model and parameter prior. The latter is difficult to do without inducing bias in model selection, and we attempt to address this problem., Our priors will be obtained by considering which parameter values are representative of features we expect to see in the data, and which would produce unnatural behaviour. To fit the models, we use a novel sequential Monte Carlo method (particle learning) not previously applied to ecological data sets., We show that existing analyses on the duck data may have been susceptible to a common problem in Bayesian model selection ( Lindley's paradox), and suggest methods for prior selection which mitigate this issue. We also discover that although it is possible to detect the existence of density dependence, it is unrealistic to expect to determine the time lag over which it operates without a great deal of data, even if said data are simulated from the model., We demonstrate that prior choices motivated by the above considerations can lead to substantially increased predictive accuracy over surprisingly long time scales whether model selection is of primary concern or not., We conclude from our analysis of real-world data that there is little evidence of density dependence in many duck species, suggesting that such effects, if present, are likely to be small in magnitude. [ABSTRACT FROM AUTHOR]

A survey of Scottish Crossbills Loxia scotica was carried out in 3,506 km2 of conifer woodland in northern Scotland during January to April 2008 to provide the first estimate of the global population size for this endemic bird. Population estimates were also made for Common Crossbills L. curvirostra and Parrot Crossbills L. pytyopsittacus within this range. Crossbills were lured to systematically selected survey points for counting, sexing and recording their calls for later call-type (species) identification from sonograms. Crossbills were located at 451 of the 852 survey points, and adequate tape-recordings made at 387 of these. The Scottish Crossbill had a disjunct distribution, occurring largely within the eastern part of the study area, but also in the northwest. Common Crossbills had a mainly westerly distribution. The population size of post-juvenile Scottish Crossbills was estimated as 13,600 (95% C.I. 8,130–22,700), which will approximate to 6,800 (4,065–11,350) pairs. Common Crossbills were more abundant within this range (27,100, 95% C.I. 14,700–38,400) and Parrot Crossbills rare (about 100). The sex ratio was not significantly different from parity for Scottish Crossbills. The modal number at survey points was two but numbers were larger in January than later in the survey. The numbers and distribution of all crossbill species are likely to vary between years, depending upon the size of the cone crops of the different conifers: all were coning in 2008. Common Crossbill and Parrot Crossbill numbers will also be affected by irruptions from continental Europe. A monitoring scheme is required to detect any population trend, and further work on their habitat requirement (e.g. conifer selection at different seasons) is needed to inform habitat management of native and planted conifer forests to ensure a secure future for this endemic bird. [ABSTRACT FROM PUBLISHER]

Double-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. The resulting data supplement conventional distance sampling data with two-sample mark–recapture data. Like conventional mark–recapture data, these have inherent problems for estimating abundance in the presence of heterogeneity. Unlike conventional mark–recapture methods, line transect methods use knowledge of the distribution of a covariate, which affects detection probability (namely, distance from the transect line) in inference. This knowledge can be used to diagnose unmodeled heterogeneity in the mark–recapture component of the data. By modeling the covariance in detection probabilities with distance, we show how the estimation problem can be formulated in terms of different levels of independence. At one extreme, full independence is assumed, as in the Petersen estimator (which does not use distance data); at the other extreme, independence only occurs in the limit as detection probability tends to one. Between the two extremes, there is a range of models, including those currently in common use, which have intermediate levels of independence. We show how this framework can be used to provide more reliable analysis of double-observer line transect data. We test the methods by simulation, and by analysis of a dataset for which true abundance is known. We illustrate the approach through analysis of minke whale sightings data from the North Sea and adjacent waters. [ABSTRACT FROM AUTHOR]

We compare two Monte Carlo (MC) procedures, sequential importance sampling (SIS) and Markov chain Monte Carlo (MCMC), for making Bayesian inferences about the unknown states and parameters of state–space models for animal populations. The procedures were applied to both simulated and real pup count data for the British grey seal metapopulation, as well as to simulated data for a Chinook salmon population. The MCMC implementation was based on tailor-made proposal distributions combined with analytical integration of some of the states and parameters. SIS was implemented in a more generic fashion. For the same computing time MCMC tended to yield posterior distributions with less MC variation across different runs of the algorithm than the SIS implementation with the exception in the seal model of some states and one of the parameters that mixed quite slowly. The efficiency of the SIS sampler greatly increased by analytically integrating out unknown parameters in the observation model. We consider that a careful implementation of MCMC for cases where data are informative relative to the priors sets the gold standard, but that SIS samplers are a viable alternative that can be programmed more quickly. Our SIS implementation is particularly competitive in situations where the data are relatively uninformative; in other cases, SIS may require substantially more computer power than an efficient implementation of MCMC to achieve the same level of MC error. [ABSTRACT FROM AUTHOR]

The dominant source of variance in line transect sampling is usually the encounter rate variance. Systematic survey designs are often used to reduce the true variability among different realizations of the design, but estimating the variance is difficult and estimators typically approximate the variance by treating the design as a simple random sample of lines. We explore the properties of different encounter rate variance estimators under random and systematic designs. We show that a design-based variance estimator improves upon the model-based estimator of Buckland et al. (2001, Introduction to Distance Sampling. Oxford: Oxford University Press, p. 79) when transects are positioned at random. However, if populations exhibit strong spatial trends, both estimators can have substantial positive bias under systematic designs. We show that poststratification is effective in reducing this bias. [ABSTRACT FROM AUTHOR]

Methods that allow unbiased estimation of animal abundance are increasingly demanded in management and conservation. The use of these methods should respect their assumptions. The need for accurate distance measurements in distance-sampling surveys is stressed. Here we present 2 alternative methods for measuring distance from a line to an object during helicopter surveys: 1) using a Global Positioning System (GPS) unit, with distances measured using appropriate software; and 2) recording declination angles and altitudes, using basic trigonometry to obtain the appropriate distances. These are compared to distances measured by a laser rangefinder (assumed to be true distances). The effect of the different errors on estimated densities is assessed by simulation. The GPS method appeared to be very accurate, while a potential downward bias in estimated density could be present if the inclinometer method is used. We discuss the implication for wildlife studies of using different measurement methods leading to different errors. [ABSTRACT FROM AUTHOR]

1. One of the most difficult problems in developing spatially explicit models of population dynamics is the validation and parameterization of the movement process. We show how movement models derived from capture–recapture analysis can be improved by incorporating them into a spatially explicit metapopulation model that is fitted to a time series of abundance data. 2. We applied multisite capture–recapture analysis techniques to photo-identification data collected from female grey seals at the four main breeding colonies in the North Sea between 1999 and 2001. The best-fitting movement models were then incorporated into state-space metapopulation models that explicitly accounted for demographic and observational stochasticity. 3. These metapopulation models were fitted to a 20-year time series of pup production data for each colony using a Bayesian approach. The best-fitting model, based on the Akaike Information Criterion (AIC), had only a single movement parameter, whose confidence interval was 82% less than that obtained from the capture–recapture study, but there was some support for a model that included an effect of distance between colonies. 4. The state-space modelling provided improved estimates of other demographic parameters. 5. The incorporation of movement, and the way in which it was modelled, affected both local and regional dynamics. These differences were most evident as colonies approached their carrying capacities, suggesting that our ability to discriminate between models should improve as the length of the grey seal time series increases. [ABSTRACT FROM AUTHOR]

1. The ability to monitor abundance of animal populations is becoming increasingly important, in light of growing concerns over the loss of biodiversity through anthropogenic changes. A widely used tool for such monitoring is distance sampling, in which distances of detected animals from a line or point are modelled, to estimate detectability and hence abundance. Nevertheless, many species still prove problematic to survey. We have developed two extensions to point transect sampling that potentially allow abundance to be estimated for a number of species from diverse taxa for which good survey methods have not previously been available. 2. For each method, the primary survey comprises a random sample of points, or more usually a systematic grid of points, through the region of interest. Animals are lured to a point, or trapped at a point, and the number of animals observed at each point is recorded. A separate study is conducted on a subset of animals, to record whether they respond to the lure or enter the trap, for a range of known distances from the point. These data are used to estimate the probability that an animal will respond to the lure or enter the trap, as a function of its initial distance from the point. This allows the counts to be converted to an estimate of abundance in the survey region. 3. We illustrated the methods using a lure survey of crossbills Loxia spp. in coniferous woodland in Scotland. 4. Synthesis and applications. Two extensions of point transect sampling that use the same statistical methodology, lure point transects and trapping point transects, have been developed. Lure point transects extend the applicability of distance sampling to species that can be lured to a point, while trapping point transects potentially allow abundance estimation of species that can be trapped, with fewer resources needed than trapping webs and conventional mark–recapture methods. [ABSTRACT FROM AUTHOR]

Analyzes the population trends for farmland birds using generalized additive models. Importance of knowledge of the direction, magnitude and timing of changes in bird population abundance in the identification of priority conservation concerns; Decline of the population trajectories in the 1970s; Stability of the majority of the population in the 1990s.

For prudent wildlife management based on population dynamics models, it is important to incorporate parameter uncertainty into the management advice. Much parameter uncertainty originates when it is not possible to parameterize the population management model for a population of interest using data from that population alone. Instead, information about parameter values obtained from other populations of the same species, or even from similar species, must be used. In addition, the age structure of wildlife populations is generally unknown. We show how sequential importance sampling can be used for combining information on demographic processes, obtained from closely studied populations, with aggregated count and cull information from the population to be managed. We resample parameter sets using kernel smoothing, which has the effect of perturbing parameter values. We show how the fitted model can be used to explore alternative culling strategies for red deer in Scotland. [ABSTRACT FROM AUTHOR]