Manly B.F.J.,Western EcoSystems Technology Inc.
Journal of Biopharmaceutical Statistics | Year: 2011
Two methods of bootstrap resampling are discussed with log-linear models for count data. The first involves the resampling of observations and the second involves the resampling of Pearson residuals taking into account changes in the distribution of residuals associated with the expected values of counts. The use of both methods is illustrated on two data sets; one data set concerns the number of ear infections of swimmers related to whether they are frequent swimmers or not and three other variables, and the other data set concerns the number of visits to a doctor made in the last 2 weeks related to the age of subjects and 10 other variables. A third data set on the number of marine mammal interactions in different years and fishing areas is also used as an example. In this case only the second bootstrap method can be used because the nature of the data allows the bootstrap resampling of observations to produce sets of data that could not have occurred in practice. Simulation results indicate that the bootstrap results are slightly better than the results from a conventional analysis for the first data set, and much better than the results from a conventional analysis for the second data set, but a conventional analysis works well for the third data set while there are problems with bootstrap analyses. © Taylor & Francis Group, LLC.
Castellon T.D.,Archbold Biological Station |
Rothermel B.B.,Archbold Biological Station |
Nomani S.Z.,Western EcoSystems Technology Inc.
Chelonian Conservation and Biology | Year: 2012
Gopher tortoises (Gopherus polyphemus) occur in a variety of habitats, but are primarily associated with sandhill communities. In peninsular Florida, however, mesic flatwoods make up the largest area of habitat, and scrub often replaces sandhill on inland ridges. Tortoise ecology is poorly understood in these habitats and few data are available to guide management. We surveyed tortoise burrows and assessed vegetation in scrub, flatwoods, and pine plantations on flatwoods soils at Avon Park Air Force Range in south-central Florida. Densities of noncollapsed burrows in scrub (1.93/ha) and flatwoods/plantations (1.42/ha) were generally lower than is typical for sandhill (3.25-9.95/ha), although total abundance was high (>20,000) because of the large habitat area. In scrub, low burrow densities may be due to low abundance of food plants. Nonetheless, the burrow density in scrub was significantly higher than in flatwoods/plantations, where food was abundant but soils were poorly drained and burrows were often flooded. The percentage of collapsed burrows was significantly higher in scrub (53%) than in flatwoods/plantations (35%), although a higher percentage of the remaining (noncollapsed) burrows were active in scrub (23%) than in flatwoods/plantations (16%). These patterns (and data from a subsequent radiotelemetry study) suggest that tortoises in scrub maintain strong fidelity to individual burrows, and frequently abandon others, whereas tortoises in flatwoods share burrows and move among them regularly, but rarely abandon them. This sharing and continual reuse of available burrows suggests a possible limitation on suitable conditions for burrow construction in flatwoods, probably related to the high water table. We suggest that scrub and flatwoods may constitute suboptimal habitats for gopher tortoises, due to low abundance of food in scrub and poorly drained soils in flatwoods. Nonetheless, large numbers of tortoises may occupy scrub and flatwoods, necessitating better understanding of their ecology in these habitats. © 2012 Chelonian Research Foundation.
Sawyer H.,University of Wyoming |
Sawyer H.,Western EcoSystems Technology Inc. |
Kauffman M.J.,University of Wyoming
Journal of Animal Ecology | Year: 2011
Birds that migrate long distances use stopover sites to optimize fuel loads and complete migration as quickly as possible. Stopover use has been predicted to facilitate a time-minimization strategy in land migrants as well, but empirical tests have been lacking, and alternative migration strategies have not been considered. We used fine-scale movement data to evaluate the ecological role of stopovers in migratory mule deer Odocoileus hemionus- a land migrant whose fitness is strongly influenced by energy intake rather than migration speed. Although deer could easily complete migrations (range 18-144km) in several days, they took an average of 3weeks and spent 95% of that time in a series of stopover sites that had higher forage quality than movement corridors. Forage quality of stopovers increased with elevation and distance from winter range. Mule deer use of stopovers corresponded with a narrow phenological range, such that deer occupied stopovers 44days prior to peak green-up, when forage quality was presumed to be highest. Mule deer used one stopover for every 5·3 and 6·7km travelled during spring and autumn migrations, respectively, and used the same stopovers in consecutive years. Study findings indicate that stopovers play a key role in the migration strategy of mule deer by allowing individuals to migrate in concert with plant phenology and maximize energy intake rather than speed. Our results suggest that stopover use may be more common among non-avian taxa than previously thought and, although the underlying migration strategies of temperate ungulates and birds are quite different, stopover use is important to both. Exploring the role of stopovers in land migrants broadens the scope of stopover ecology and recognizes that the applied and theoretical benefits of stopover ecology need not be limited to avian taxa. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.
Robertson B.L.,University of Canterbury |
Brown J.A.,University of Canterbury |
Mcdonald T.,Western EcoSystems Technology Inc. |
Jaksons P.,University of Canterbury
Biometrics | Year: 2013
To design an efficient survey or monitoring program for a natural resource it is important to consider the spatial distribution of the resource. Generally, sample designs that are spatially balanced are more efficient than designs which are not. A spatially balanced design selects a sample that is evenly distributed over the extent of the resource. In this article we present a new spatially balanced design that can be used to select a sample from discrete and continuous populations in multi-dimensional space. The design, which we call balanced acceptance sampling, utilizes the Halton sequence to assure spatial diversity of selected locations. Targeted inclusion probabilities are achieved by acceptance sampling. The BAS design is conceptually simpler than competing spatially balanced designs, executes faster, and achieves better spatial balance as measured by a number of quantities. The algorithm has been programed in an R package freely available for download. © 2013, The International Biometric Society.
Mcdonald T.L.,Western EcoSystems Technology Inc.
Journal of Animal Ecology | Year: 2013
Summary: Use-availability and presence-only analyses are synonyms. Both require two samples (one containing known locations, one containing potential locations), both estimate the same parameters, and both use the same fundamental likelihood. Use-availability and presence-only designs compare characteristics of points where an organism was located to those where the organism could have been located. These designs can be generalized to estimate the relative probability that any event occurred at a set of locations. This article generalizes the use-availability likelihood given in Johnson et al. (Resource selection functions based on use-availability data: theoretical motivation and evaluation methods, Journal of Wildlife Management, 2006) to point locations. This derivation arrives at the same likelihood as Fithian & Hastie (Statistical Models for Presence-Only Data: Finite-Sample Equivalence and Addressing Observer Bias, 2012) but uses a different technique and allows a more general link function. Fithian & Hastie (2012) use a case-control argument and Bayes theorem to derive the likelihood. This article uses Lagrangian multipliers to maximize the two-sample likelihood. Resource selection functions (RSF) defined here are ratios of density functions. RSFs must be positive and unbounded. Proper link functions must provide proportionality over their entire range. Given these conditions, the exponential link is the most logical and appropriate link function for RSFs. These conditions exclude the logistic link. This article affirms that estimation of a RSF does not involve 'running logistic regression'. By assigning 0 and 1 (pseudo-)responses to vectors of covariates associated with locations in the used and available sample, it is possible to 'trick' logistic regression software into maximizing the use-availability likelihood. Representing the analysis as 'logistic regression' is misleading because that implies use of the logistic link, which is inappropriate for RSF's. It is more accurate to state that the 'use-availability likelihood was maximized'. RSFs are more general, intuitive and useful than resource selection probability functions (RSPF). RSPFs depend heavily on sampling mechanisms and the number of used and available locations selected. Consequently, the objective of estimation in use-availability studies should be the RSF, not the RSPF. Two simple examples and R code in the Supporting Information illustrate computations. These examples maximize the general log likelihood without the aid of logistic regression software. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.