Spatially-balanced adaptive web sampling

The paper deals with sampling from a finite population that is distributed over space and has a highly uneven spatial distribution. It suggests a sampling design that allocates a portion of the sample units that are well spread over the population and sequentially selects the remaining units in sub-areas that appear to be of more interest according to the study variable values observed during the survey. In order to estimate the population mean while using this sampling design, a computationally intense estimator, obtained via the Rao–Blackwell approach, is proposed and a resampling method is used that makes the inference computationally feasible. The whole sampling strategy is evaluated through several Monte Carlo experiments.     

Hierarchical spatial modeling for estimation of population size

Estimation of population size has traditionally been viewed from a finite population sampling perspective. Typically, the objective is to obtain an estimate of the total population count of individuals within some region. Often, some stratification scheme is used to estimate counts on subregions, whereby the total count is obtained by aggregation with weights, say, proportional to the areas of the subregions. We offer an alternative to the finite population sampling approach for estimating population size. The method does not require that the subregions on which counts are available form a complete partition of the region of interest. In fact, we envision counts coming from areal units that are small relative to the entire study region and that the total area sampled is a very small proportion of the total study area. In extrapolating to the entire region, we might benefit from assuming that there is spatial structure to the counts. We implement this by modeling the intensity surface as a realization from a spatially correlated random process. In the case of multiple population or species counts, we use the linear model of coregionalization to specify a multivariate process which provides associated intensity surfaces hence association between counts within and across areal units. We illustrate the method of population size estimation with simulated data and with tree counts from a Southwestern pinyon-juniper woodland data set.     

Importance of Assessing Population Genetic Structure before Eradication of Invasive Species: Examples from Insular Norway Rat Populations

Abstract:  Determining the inter-island migration abilities of pest species and delimiting eradication units enable more viable long-term eradication campaigns because recurrent colonization from neighboring islands is avoided. We examined the genetic structure of the invasive Norway rat ( Rattus norvegicus ) to identify gene flow between islands and delimit population units at different geographical scales. We investigated variation in eight microsatellite loci in rat populations from 18 islands, representing five archipelagos off the Brittany coast (France). Although most of the islands are isolated from each other, short genetic distances, weak F_ST values between close islands, and a high level of cross-assignment showed that individuals collected on different islands could represent a single population unit. A Bayesian clustering method also supported the existence of high levels of gene flow between some neighboring islands. Thus, the statement "one island equals one population" can be false when inter-island distances are less than a few hundred meters. Genetic studies enable the definition of island clusters among which migration may occur that should be considered eradication units. To avoid reinvasion and to minimize ecological and economic costs, rats on all islands in an eradication unit should be eradicated simultaneously. We suggest that the genetic monitoring we performed here can be applied for management of any pest.     

Markov Chain Monte Carlo on optimal adaptive sampling selections

Under a Bayesian population model with a given prior distribution, the optimal sampling strategy with a fixed sample size n is an n-phase adaptive one. That is, the selection of the next sampling units should sequentially depend on the information obtained from the previously selected units, including the observed values of interest. Such an optimal strategy is in general not executable in practice due to its intensive computation. In many survey sampling situations, an important problem is that one would like to select a set of units in addition to a certain number of sampling units which have been observed. If the optimal strategy is an adaptive one, the selection of the additional units should take both the labels and the observed values of the already selected units into account. Hence, a simpler optimal two-phase adaptive sampling strategy under a Bayesian population model is proposed in this article for practical interest. A Markov chain Monte Carlo method is used to approximate the posterior joint distribution of the unobserved population units after the first phase sampling, for the optimal selection of the second phase sample. This approximation method is found to be successful to select the optimal second-phase sample. Finally, this optimal strategy is applied to a set of data from a study of geothermal CO₂ emissions in Yellowstone National Park as a practical illustrative example.     

A simulation model to explore the relative value of stock enhancement versus harvest regulations for fishery sustainability

Harvest restrictions and stock enhancement are commonly proposed management responses for sustaining degraded fisheries, but comparisons of their relative effectiveness have seldom been considered prior to making policy choices. We built a population model that incorporated both size-dependent harvest restrictions and stock enhancement contributions to explore trade-offs between minimum length limits and stock enhancement for improving population sustainability and fishery metrics (e.g., catch). We used a Murray cod Maccullochella peelii peelii population as a test case, and the model incorporated density-dependent recruitment processes for both hatchery and wild fish. We estimated the spawning potential ratio (SPR) and fishery metrics (e.g., angler catch) across a range of minimum length limits and stocking rates. Model estimates showed that increased minimum length limits were much more effective than stock enhancement for increasing SPR and angler catches in exploited populations, but length limits resulted in reduced harvest. Stocking was predicted to significantly increase total recruitment, population sustainability, and fishery metrics only in systems where natural reproduction had been greatly reduced via habitat loss, fishing mortality was high, or both. If angler fishing effort increased with increased fish abundance from stocking efforts, fishing mortality was predicted to increase and reduce the benefits realized from stocking. The model also indicated that benefits from stock enhancement would be reduced if reproductive efficiency of hatchery-origin fish was compromised. The simulations indicated that stock enhancement was a less effective method to improve fishery sustainability than measures designed to reduce fishing mortality (e.g., length limits).     

Use of Empirically Derived Source-Destination Models to Map Regional Conservation Corridors

Abstract:  The ability of populations to be connected across large landscapes via dispersal is critical to long-term viability for many species. One means to mitigate population isolation is the protection of movement corridors among habitat patches. Nevertheless, the utility of small, narrow, linear features as habitat corridors has been hotly debated. Here, we argue that analysis of movement across continuously resistant landscapes allows a shift to a broader consideration of how landscape patterns influence connectivity at scales relevant to conservation. We further argue that this change in scale and definition of the connectivity problem improves one's ability to find solutions and may help resolve long-standing disputes regarding scale and definition of movement corridors and their importance to population connectivity. We used a new method that combines empirically derived landscape-resistance maps and least-cost path analysis between multiple source and destination locations to assess habitat isolation and identify corridors and barriers to organism movement. Specifically, we used a genetically based landscape resistance model for American black bears ( Ursus americanus ) to identify major movement corridors and barriers to population connectivity between Yellowstone National Park and the Canadian border. Even though western Montana and northern Idaho contain abundant public lands and the largest wilderness areas in the contiguous United States, moving from the Canadian border to Yellowstone Park along those paths indicated by modeled gene flow required bears to cross at least 6 potential barriers. Our methods are generic and can be applied to virtually any species for which reliable maps of landscape resistance can be developed.     

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.     

Estimating the functional form for the density dependence from life history data

Two contrasting approaches to the analysis of population dynamics are currently popular: demographic approaches where the associations between demographic rates and statistics summarizing the population dynamics are identified; and time series approaches where the associations between population dynamics, population density, and environmental covariates are investigated. In this paper, we develop an approach to combine these methods and apply it to detailed data from Soay sheep (Ovis aries). We examine how density dependence and climate contribute to fluctuations in population size via age- and sex-specific demographic rates, and how fluctuations in demographic structure influence population dynamics. Density dependence contributes most, followed by climatic variation, age structure fluctuations and interactions between density and climate. We then simplify the density-dependent, stochastic, age-structured demographic model and derive a new phenomenological time series which captures the dynamics better than previously selected functions. The simple method we develop has potential to provide substantial insight into the relative contributions of population and individual-level processes to the dynamics of populations in stochastic environments.     

Sampling from partially rank-ordered sets

In this paper we introduce a new sampling design. The proposed design is similar to a ranked set sampling (RSS) design with a clear difference that rankers are allowed to declare any two or more units are tied in ranks whenever the units can not be ranked with high confidence. These units are replaced in judgment subsets. The fully measured units are then selected from these partially ordered judgment subsets. Based on this sampling scheme, we develop unbiased estimators for the population mean and variance. We show that the proposed sampling procedure has some advantages over standard ranked set sampling.     

Cycle synchrony and probability of conception in female hamadryas baboonsPapio hamadryas

We investigated the factors affecting conception in a captive population ofPapio hamadryas. We collected data on reproductive states from 16 females over 12 years. Probability of conception was related to the outcome of the immediately preceding pregnancy and the fate of the preceding offspring. After abortions or when an infant survived more than 6 months, a female needed more cycles to conceive than when an infant died within the first 6 months post partum. The degree of estrus synchrony within, but not between, one-male units influenced the probability of conception. Females experiencing conceptive estrus showed less synchrony than those experiencing nonconceptive estrus. The number of females simultaneously in estrus within a one-male unit was negatively correlated with the probability of conception. Within our captive population, group size and male and female age had no influence on the probability of conception. The results indicate that sperm may be a limited resource in the one-male reproductive units of hamadryas baboons. Female-female competition for conception may exist and should influence the demography of one-male units.     

Bayesian predictive inference for the proportion of eroded land in a small area in Iowa

There is an increasing interest in the quality of soil, especially for small geographical areas. We present a method to estimate the percent of the area in a county or hydrological basin that is eroded. There are sample data (for several counties in eastern Iowa) from the National Resources Inventory and population data on land use, land capability class, rainfall and slope length and steepness. Using the Gibbs sampler we perform Bayesian predictive inference to obtain estimates for the non-sampled units. These estimates, together with the sample data, provide an estimate of the proportion of the total area that is eroded. We assess the quality of fit of our model using two cross-validation exercises and graphical methods.     

Assessing Risks to Biodiversity from Future Landscape Change

We examined the impacts of possible future land development patterns on the biodiversity of a landscape. Our landscape data included a remote sensing derived map of the current habitat of the study area and six maps of future habitat distributions resulting from different land development scenarios. Our species data included lists of all bird, mammal, reptile, and amphibian species in the study area, their habitat associations, and area requirements for each. We estimated the area requirements using home ranges, sampled population densities, or genetic area requirements that incorporate dispersal distances. Our measures of biodiversity were species richness and habitat abundance. We calculated habitat abundance in two ways. First, we computed the total habitat area for each species in each landscape. Second, we calculated the number of habitat units for each species in each landscape by dividing the size of each habitat patch in the landscape by the area requirement and summing over all patches. Species richness was based on presence of habitat. Species became extinct in the landscape if they had no habitat area or no habitat units, respectively. We then computed ratios of habitat abundance in each future landscape to habitat abundance in the present for each species. We also computed the ratio of future to present species richness. We then calculated summary statistics across all species. Species richness changed little from present to future. There were distinctly greater risks to habitat abundance in landscapes that extrapolated from present trends or zoning patterns, however, as opposed to landscapes in which land development activities followed more constrained patterns. These results were stable when tested using Monte Carlo simulations and sensitivity tests on the area requirements. We conclude that this methodology can begin to discriminate the effects of potential changes in land development on vertebrate biodiversity.     

Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments

This article reports on the application of dasymetric mapping techniques to interpolate and disaggregate block group population counts to smaller areal units (i.e., tax parcels) and derive surface population models with more realistic representations of population distributions in our residential study area in Miami-Dade, Florida. Three methods of dasymetric interpolation were tested: (i) binary, (ii) three-class, and (iii) limiting variable. Our enhanced limiting variable approach introduced an adjustment factor for parcel vacancy rates in the dasymetric calculations, and applied dasymetric mapping techniques to disaggregate future population projections to the tax lot level of analysis. The limiting variable interpolation generated the lowest coefficient of variation (0.188), followed by the three-class interpolation (0.645). We also found that population densities vary substantially within land use classes of single family, medium density and high density classes, and these variations also highlighted the importance of incorporating vacancy rates when interpolating population counts to categorical land use data. Overall, the enhanced dasymetric mapping technique is particularly useful for examining the impact of sea-level rise as its derivatives are compatible with high resolution LiDAR and orthoimagery data. Coastal counties can also benefit from such high resolution surface population models to enhance the accuracy of hazard-related vulnerability assessments and to guide the development of relevant shore zone conservation and adaptation strategies.     

Diagnosing Units of Conservation Management

Species-oriented conservation programs attempt to analyze and maintain intra-specific variation in order to maximally preserve biological diversity. The "evolutionarily significant unit" has become an operational term for a group of organisms that should be the minimial unit for conservation management. No generally accepted definition for this term exists that would be the basis for the evaluation of these units in practical conservation situations. Currently, taxonomic decisions in species conservation are mostly based on the biological species concept. But the universal application of criteria of reproductive isolation or phenetic similarity to delimit conservation units is problematical. We favor a definition for evolutionarily significant units based on patterns of variation. In the theoretical framework of the phylogenetic species concept, conservation units are delimited by characters that diagnose clusters of individuals or populations to the exclusion of other such clusters. Characters are used for cladistic analysis to infer hypotheses of the phylogenetic relationships of individuals, and differentiated populations are diagnosed using population aggregation analysis. Characters can be based on genetic, morphological, ecological, or behavioral information, provided they are inferred to be heritable. The use of cladistics and population aggregation analysis has the potential to make the evaluation of evoluntionarily significant units objective and testable, an important consideration in politically controversial cases. Our cladistic approach is demonstrated by the evaluation of potential conservation units in the endangered tiger beetles Cicindela dorsalis and C. puritana .     

Halton iterative partitioning: spatially balanced sampling via partitioning

A new spatially balanced sampling design for environmental surveys is introduced, called Halton iterative partitioning (HIP). The design draws sample locations that are well spread over the study area. Spatially balanced designs are known to be efficient when surveying natural resources because nearby locations tend to be similar. The HIP design uses structural properties of the Halton sequence to partition a resource into nested boxes. Sample locations are then drawn from specific boxes in the partition to ensure spatial diversity. The method is conceptually simple and computationally efficient, draws spatially balanced samples in two or more dimensions and uses standard design-based estimators. Furthermore, HIP samples have an implicit ordering that can be used to define spatially balanced over-samples. This feature is particularly useful when sampling natural resources because we can dynamically add spatially balanced units from the over-sample to the sample as non-target or inaccessible units are discovered. We use several populations to show that HIP sampling draws spatially balanced samples and gives precise estimates of population totals.     

Accounting for matching uncertainty in two stage capture–recapture experiments using photographic measurements of natural marks

We propose a Bayesian hierarchical modeling approach for estimating the size of a closed population from data obtained by identifying individuals through photographs of natural markings. We assume that noisy measurements of a set of distinctive features are available for each individual present in a photographic catalogue. To estimate the population size from two catalogues obtained during two different sampling occasions, we embed the standard two-stage $M_t$ capture–recapture model for closed population into a multivariate normal data matching model that identifies the common individuals across the catalogues. In addition to estimating the population size while accounting for the matching process uncertainty, this hierarchical modelling approach allows to identify the common individuals by using the information provided by the capture–recapture model. This way, our model also represents a novel and reliable tool able to reduce the amount of effort researchers have to expend in matching individuals. We illustrate and motivate the proposed approach via a real data set of photo-identification of narwhals. Moreover, we compare our method with a set of possible alternative approaches by using both the empirical data set and a simulation study.     

The ‘Bayesics’ of Ranked Set Sampling

Ranked set sampling can be useful when measurements are expensive but units from the popu- lation can be easily ranked. In this situation one may draw k units from the population, rank them, select one on which to make the expensive measurement, draw another k units, rank them, select one, and so on. The method was originally suggested by McIntyre (1952) in connection with pasture yields and is obviously applicable in other situations as well. Dell and Clutter (1972) and Patil et al. (1994) explain the basics from a classical point of view. Our aim is to examine the procedure from a Bayesian point of view, determine whether ranked set sampling provides advantages over simple random sampling and explore some optimality questions     

Bayesian analysis of abundance for binomial sighting data with unknown number of marked individuals

The mark-resight method for estimating the size of a closed population can in many circumstances be a less expensive and less invasive alternative to traditional mark-recapture. Despite its potential advantages, one major drawback of traditional mark-resight methodology is that the number of marked individuals in the population available for resighting needs to be known exactly. In real field studies, this can be quite difficult to accomplish. Here we develop a Bayesian model for estimating abundance when sighting data are acquired from distinct sampling occasions without replacement, but the exact number of marked individuals is unknown. By first augmenting the data with some fixed number of individuals comprising a marked “super population,” the problem may then be reformulated in terms of estimating the proportion of this marked super population that was actually available for resighting. This then allows the data for the marked population available for resighting to be modeled as random realizations from a binomial logit-normal distribution. We demonstrate the use of our model to estimate the New Zealand robin (Petroica australis) population size in a region of Fiordland National Park, New Zealand. We then evaluate the performance of the proposed model relative to other estimators via a series of simulation experiments. We generally found our model to have advantages over other models when sample sizes are smaller with individually heterogeneous resighting probabilities. Due to limited budgets and the inherent variability between individuals, this is a common occurrence in mark-resight population studies. WinBUGS and R code to carry out these analyses is available from .     

Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.     

Linking noninvasive genetic sampling and traditional monitoring to aid management of a trans-border carnivore population

Noninvasive genetic sampling has been embraced by wildlife managers and ecologists, especially those charged with monitoring rare and elusive species over large areas. Challenges arise when desired population measures are not directly attainable from genetic data and when monitoring targets trans-border populations. Norwegian management authorities count individual brown bears (Ursus arctos) using noninvasive genetic sampling but express management goals in the annual number of bear reproductions (females that produce cubs), a measure that is not directly available from genetic data. We combine noninvasive genetic sampling data with information obtained from a long-term intensive monitoring study in neighboring Sweden to estimate the number of annual reproductions by females detected within Norway. Most female brown bears in Norway occur near the border with neighboring countries (Sweden, Finland, and Russia) and their potential reproduction can therefore only partially be credited to Norway. Our model includes a simulation-based method that corrects census data to account for this. We estimated that 4.3 and 5.7 reproductions can be credited to females detected with noninvasive genetic sampling in Norway in 2008 and 2009, respectively. These numbers fall substantially short of the national target (15 annual reproductions). Ignoring the potential for home ranges to extend beyond Norway's borders leads to an increase in the estimate of the number of reproductions by -30%. Our study shows that combining noninvasive genetic sampling with information obtained from traditional intensive/invasive monitoring can help answer contemporary management questions in the currency desired by managers and policy makers. Furthermore, combining methodologies and thereby accounting for space use increases the accuracy of the information on which decisions are based. It is important that the information derived from multiple approaches is applicable to the same focal population and that predictions are cross-validated. When monitoring and management are constrained to administrative units, census data should be adjusted by discounting portions of individual space utilization that extend beyond the focal jurisdiction. Our simulation-based approach for making such an adjustment may be useful in other situations where management authorities target portions of trans-border populations.