Opportunistic Sampling for Joint Population Size and Density Estimation

Consider a set of probes, called “agents”, who sample, based on opportunistic contacts, a population moving between a set of discrete locations. An example of such agents are Bluetooth probes that sample the visible Bluetooth devices in a population. Based on the obtained measurements, we construct a parametric statistical model to jointly estimate the total population size (e.g., the number of visible Bluetooth devices) and their spatial density. We evaluate the performance of our estimators by using Bluetooth traces obtained during an open-air event and Wi-Fi traces obtained on a university campus

Estimation of species relative abundances and habitat preferences using opportunistic data

We develop a new statistical procedure to monitor, with opportunist data, relative species abundances and their respective preferences for dierent habitat types. Following Giraud et al. (2015), we combine the opportunistic data with some standardized data in order to correct the bias inherent to the opportunistic data collection. Our main contributions are (i) to tackle the bias induced by habitat selection behaviors, (ii) to handle data where the habitat type associated to each observation is unknown, (iii) to estimate probabilities of selection of habitat for the species. As an illustration, we estimate common bird species habitat preferences and abundances in the region of Aquitaine (France)

Epidemiology and economic impact of Johne's disease in Irish dairy herds

End of project reportThis project addressed two aspects of an emerging infectious disease of Irish cattle; the epidemiology and the economic impacts of Johne’s disease (paratuberculosis). Though this disease has been present in Irish cattle herds for decades, only since the introduction of the Single European Market in 1992 has it become more widespread. In addition to this change in the epidemiology of the disease in Irish cattle, there is increasing evidence that the causative organism, Mycobacterium avium subsp. paratuberculosis (MAP) may be implicated in a human illness, Crohn’s disease, though proof of a zoonotic link is currently disputed (Tremblay, 2004). Against this background a collaborative research project was set up by Teagasc and funded by Irish dairy farmers

A comparison of four different methods to estimate population size of Alpine marmot (Marmota marmota)

Obtaining reliable information on animal abundance in mountainous landscapes is challenging. Highly heterogeneous habitats tend to reduce detection probabilities, and the three-dimensional, rugged nature of the terrain poses severe limits to the fulfilment of a number of assumptions underlying several statistical methods. In this study, we aimed to compare the performance of 4 different methods to estimate population size of Alpine marmot (Marmota marmota), a highly social semifossorial rodent widely distributed on the European Alps. Between May and August 2015, in a study area within the Stelvio National Park (Italy) we conducted 8 sessions of capture-mark-recapture, 6 sessions of mark-resight from vantage points, 8 sessions of line distance sampling along 4 transects, and 2 sessions using double-observer methods from vantage points. The minimum number of animals alive, obtained during the mark-resight surveys, was n=54 individuals. Capture-mark-recapture models estimated a population size of n=56 individuals [95% CI (45,87)]; similar, but more precise estimates were obtained with the mark-resight approach {Bowden’s estimator: n=62 [95% CI (54,71)]; Poisson log-normal estimator: n=62 [95% CI (55,69)]}. Line distance sampling and double-observer methods were severely biased low {Line distance sampling: n=24 individuals [95% CI (19,31)]; Independent double-observer: n=24 [95% CI (22, 35)]; Dependent double-observer: n=15 [95% CI (15,20)]}. Our results suggest that the probabilistic approach based on marked individuals yielded fairly robust estimates of population size. The underestimates obtained using distance sampling and double-observer methods were likely due to the violation of some underlying assumptions. While the topography of the mountainous landscape makes it difficult to randomize the sampling scheme, the semifossorial behaviour of the target species is likely to lower the detection probabilities and violate the assumption of perfect detection on the transect

Spatial distribution of introduced brook trout Salvelinus fontinalis (Salmonidae) within alpine lakes: evidences from a fish eradication campaign

Brook trout Salvelinus fontinalis have been used worldwide to stock fishless alpine lakes, negatively affecting native biota. Understanding its spatial ecology in invaded ecosystems can provide information to interpret and contrast its ecological impact. We opportunistically used capture points of brook trout gillnetted during an eradication campaign to assess the distribution patterns of four unexploited populations inhabiting high-altitude lakes. The main eradication method implies the use of many gillnets with several mesh sizes, which are selective for different fish sizes. For each lake we drew six capture maps associated with as many different mesh sizes, and we tested whether the distance from the coastline (which in alpine lakes is a reliable proxy of the most important spatial gradients, e.g. depth, temperature, prey availability, lighting conditions) influences the proportion of captured fish belonging to different size classes and the number of fish captured by the nets with different mesh sizes. To interpret the results, we also provide a cartographic description of the lakes’ bathymetry and littoral microhabitats. We found (1) a negative relationship between brook trout distribution and the distance from the coastline in all of the size classes, lakes and mesh sizes; (2) that large brook trout can thrive in the lakes’ center, while small ones are limited to the littoral areas; and (3) that the distance from the coastline alone cannot explain all the differences in the catch densities in different parts of the lakes. As in their native range, introduced brook trout populations also have littoral habits. Microhabitats, prey availability and distance from the spawning ground are other likely factors determining the distribution patterns of brook trout populations introduced in alpine lakes. The obtained results also provide useful information on how to plan new eradication campaigns

Balancing precision and risk: Should multiple detection methods be analyzed separately in N-mixture models?

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method

Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records

1. Species’ abundances vary in space and time. Describing these patterns is a cornerstone of macroecology. Moreover, trends in population size are an important criterion for the assessment of a species’ conservation status. Because abundance trends are not homogeneous in space, we need to quantify variation in abundance trends across the geographical range of a species. A basic difficulty exists in that data sets that cover large geographic areas rarely include population abundance data at high temporal resolution. Whilst both broad-scale geographic distribution data and site-specific population trend data are becoming more widely available, approaches are required which integrate these different types of data. 2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality. 3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations. 4. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics

Probit models for capture-recapture data subject to imperfect detection, individual heterogeneity and misidentification

As noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). However, current methods do not allow for individual variation in parameters, such as detection or survival probability. Here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. To facilitate Bayesian inference using our approach, we extend standard probit regression techniques to latent multinomial models where the dimension and zeros of the response cannot be observed. We also present a novel Metropolis-Hastings within Gibbs algorithm for fitting these models using Markov chain Monte Carlo. Using closed population abundance models for illustration, we re-visit a DNA capture-recapture population study of black bears in Michigan, USA and find evidence of misidentification due to genotyping error, as well as temporal, behavioral and individual variation in detection probability. We also estimate a salamander population of known size from laboratory experiments evaluating the effectiveness of a marking technique commonly used for amphibians and fish. Our model was able to reliably estimate the size of this population and provided evidence of individual heterogeneity in misidentification probability that is attributable to variable mark quality. Our approach is more computationally demanding than previously proposed methods, but it provides the flexibility necessary for a much broader suite of models to be explored while properly accounting for uncertainty introduced by misidentification and imperfect detection. In the absence of misidentification, our probit formulation also provides a convenient and efficient Gibbs sampler for Bayesian analysis of traditional closed population capture-recapture data.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS783 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org