Estimating upper bounds for occupancy and number of manatees in areas potentially affected by oil from the Deepwater Horizon oil spill.

The explosion of the Deepwater Horizon drilling platform created the largest marine oil spill in U.S. history. As part of the Natural Resource Damage Assessment process, we applied an innovative modeling approach to obtain upper estimates for occupancy and for number of manatees in areas potentially affected by the oil spill. Our data consisted of aerial survey counts in waters of the Florida Panhandle, Alabama and Mississippi. Our method, which uses a Bayesian approach, allows for the propagation of uncertainty associated with estimates from empirical data and from the published literature. We illustrate that it is possible to derive estimates of occupancy rate and upper estimates of the number of manatees present at the time of sampling, even when no manatees were observed in our sampled plots during surveys. We estimated that fewer than 2.4% of potentially affected manatee habitat in our Florida study area may have been occupied by manatees. The upper estimate for the number of manatees present in potentially impacted areas (within our study area) was estimated with our model to be 74 (95%CI 46 to 107). This upper estimate for the number of manatees was conditioned on the upper 95%CI value of the occupancy rate. In other words, based on our estimates, it is highly probable that there were 107 or fewer manatees in our study area during the time of our surveys. Because our analyses apply to habitats considered likely manatee habitats, our inference is restricted to these sites and to the time frame of our surveys. Given that manatees may be hard to see during aerial surveys, it was important to account for imperfect detection. The approach that we described can be useful for determining the best allocation of resources for monitoring and conservation.

Estimating species richness from quadrat sampling data: a general approach.

We consider the problem of estimating the number of species (denoted by S) of a biological community located in a region divided into n quadrats. To address this question, different hierarchical parametric approaches have been recently developed. Despite a detailed modeling of the underlying biological processes, they all have some limitations. Indeed, some assume that n is theoretically infinite; as a result, n and the sampling fraction are not a part of such models. Others require some prior information on S to be efficiently implemented. Our approach is more general in that it applies without limitation on the size of n, and it can be used in the presence, as well as in the absence, of prior information on S. Moreover, it can be viewed as an extension of the approach of Dorazio and Royle (2005, Journal of the American Statistical Association 100, 389-398) in that n is a part of the model and a prior distribution is placed on S. Despite serious computational difficulties, we have perfected an efficient Markov chain Monte Carlo algorithm, which allows us to obtain the Bayesian estimate of S. We illustrate our approach by estimating the number of species of a bird community located in a forest.

Estimating the occupancy rate of spatially rare or hard to detect species: a conditional approach.

We consider the problem of estimating the occupancy rate of a target species in a region divided in spatial units (called quadrats); this quantity being defined as the proportion of quadrats occupied by this species. We mainly focus on spatially rare or hard to detect species that are typically detected in very few quadrats, and for which estimating the occupancy rate (with an acceptable precision) is problematic. We develop a conditional approach for estimating the quantity of interest; we condition on the presence of the target species in the region of study. We show that conditioning makes identifiable the occurrence and detectability parameters, regardless of the number of visits made in the sampled quadrats. Compared with an unconditional approach, it proves to be complementary, in that this allows us to deal with biological questions that cannot be addressed by the former. Two Bayesian analyses of the data are performed: one is noninformative, and the other takes advantage of the fact that some prior information on detectability is available. It emerges that taking such a prior into account significantly improves the precision of the estimate when the target species has been detected in few quadrats and is known to be easily detectable.

A bayesian approach to the multistate Jolly-Seber capture-recapture model.

This article considers a Bayesian approach to the multistate extension of the Jolly-Seber model commonly used to estimate population abundance in capture-recapture studies. It extends the work of George and Robert (1992, Biometrika79, 677-683), which dealt with the Bayesian estimation of a closed population with only a single state for all animals. A super-population is introduced to model new entrants in the population. Bayesian estimates of abundance are obtained by implementing a Gibbs sampling algorithm based on data augmentation of the missing data in the capture histories when the state of the animal is unknown. Moreover, a partitioning of the missing data is adopted to ensure the convergence of the Gibbs sampling algorithm even in the presence of impossible transitions between some states. Lastly, we apply our methodology to a population of fish to estimate abundance and movement.

Bayesian estimation of species richness from quadrat sampling data in the presence of prior information.

We consider the problem of estimating the number of species of an animal community. It is assumed that it is possible to draw up a list of species liable to be present in this community. Data are collected from quadrat sampling. Models considered in this article separate the assumptions related to the experimental protocol and those related to the spatial distribution of species in the quadrats. Our parameterization enables us to incorporate prior information on the presence, detectability, and spatial density of species. Moreover, we elaborate procedures to build the prior distributions on these parameters from information furnished by external data. A simulation study is carried out to examine the influence of different priors on the performances of our estimator. We illustrate our approach by estimating the number of nesting bird species in a forest.