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Geology topics

P.M. Lukacs

Publications and source records attributed to P.M. Lukacs.

7 recordsLinked to original sources
USGS2023

Predicted connectivity pathways between grizzly bear ecosystems in western Montana

Habitat and corridor mapping are key components of many conservation programs. Grizzly bear populations in the continental US are fragmented and connectivity among federal recovery areas is a conservation goal. Building on recent work, we modeled movements to predict areas of connectivity, using integrated step selection functions (iSSFs) developed from GPS-collared grizzly bears (F = 46, M = 19) in the Northern Continental Divide Ecosystem (NCDE). We applied iSSFs in a >300,000 km 2 area including the NCDE, Cabinet–Yaak (CYE), Bitterroot (BE), and Greater Yellowstone (GYE) Ecosystems. First, we simulated directed movements (randomized shortest paths with 3 levels of exploration) between start and end nodes across populations. Second, we simulated undirected movements from start nodes in the NCDE, CYE, or GYE (no predetermined end nodes). We summarized and binned results as classes 1 (lowest relative predicted use) – 10 (highest relative predicted use) and evaluated predictions using 127 outlier grizzly bear locations. Connectivity pathways were primarily associated with mountainous areas and secondarily with river and stream courses in open valleys. Values at outlier locations indicated good model fit and mean classes at outlier locations (≥7.4) and Spearman rank correlations (≥0.87) were highest for undirected simulations and directed simulations with the highest level of exploration. Our resulting predictive maps can facilitate on-the-ground application of this research for prioritizing habitat conservation, human-bear conflict mitigation, and transportation planning. Additionally, our overall modeling approach has utility for myriad species and conservation applications.

Montana↗
USGS2011

Evaluating cost-efficiency and accuracy of hunter harvest survey designs

Effective management of harvested wildlife often requires accurate estimates of the number of animals harvested annually by hunters. A variety of techniques exist to obtain harvest data, such as hunter surveys, check stations, mandatory reporting requirements, and voluntary reporting of harvest. Agencies responsible for managing harvested wildlife such as deer (Odocoileus spp.), elk (Cervus elaphus), and pronghorn (Antilocapra americana) are challenged with balancing the cost of data collection versus the value of the information obtained. We compared precision, bias, and relative cost of several common strategies, including hunter self-reporting and random sampling, for estimating hunter harvest using a realistic set of simulations. Self-reporting with a follow-up survey of hunters who did not report produces the best estimate of harvest in terms of precision and bias, but it is also, by far, the most expensive technique. Self-reporting with no followup survey risks very large bias in harvest estimates, and the cost increases with increased response rate. Probability-based sampling provides a substantial cost savings, though accuracy can be affected by nonresponse bias. We recommend stratified random sampling with a calibration estimator used to reweight the sample based on the proportions of hunters responding in each covariate category as the best option for balancing cost and accuracy. ?? 2011 The Wildlife Society.

Wildlife Society Bulletin↗
USGS2009

Model selection bias and Freedman's paradox

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

Annals of the Institute of Statistical Mathematics↗
USGS2007

Concerns regarding a call for pluralism of information theory and hypothesis testing

1. Stephens et al. (2005) argue for 'pluralism' in statistical analysis, combining null hypothesis testing and information-theoretic (I-T) methods. We show that I-T methods are more informative even in single variable problems and we provide an ecological example. 2. I-T methods allow inferences to be made from multiple models simultaneously. We believe multimodel inference is the future of data analysis, which cannot be achieved with null hypothesis-testing approaches. 3. We argue for a stronger emphasis on critical thinking in science in general and less reliance on exploratory data analysis and data dredging. Deriving alternative hypotheses is central to science; deriving a single interesting science hypothesis and then comparing it to a default null hypothesis (e.g. 'no difference') is not an efficient strategy for gaining knowledge. We think this single-hypothesis strategy has been relied upon too often in the past. 4. We clarify misconceptions presented by Stephens et al. (2005) . 5. We think inference should be made about models, directly linked to scientific hypotheses, and their parameters conditioned on data, Prob(Hj| data). I-T methods provide a basis for this inference. Null hypothesis testing merely provides a probability statement about the data conditioned on a null model, Prob(data |H0). 6. Synthesis and applications . I-T methods provide a more informative approach to inference. I-T methods provide a direct measure of evidence for or against hypotheses and a means to consider simultaneously multiple hypotheses as a basis for rigorous inference. Progress in our science can be accelerated if modern methods can be used intelligently; this includes various I-T and Bayesian methods.

Journal of Applied Ecology↗
USGS2006

Monitoring low density avian populations: An example using Mountain Plovers

Declines in avian populations highlight a need for rigorous, broad-scale monitoring programs to document trends in avian populations that occur in low densities across expansive landscapes. Accounting for the spatial variation and variation in detection probability inherent to monitoring programs is thought to be effort-intensive and time-consuming. We determined the feasibility of the analytical method developed by Royle and Nichols (2003), which uses presence-absence (detection-non-detection) field data, to estimate abundance of Mountain Plovers (Charadrius montanus) per sampling unit in agricultural fields, grassland, and prairie dog habitat in eastern Colorado. Field methods were easy to implement and results suggest that the analytical method provides valuable insight into population patterning among habitats. Mountain Plover abundance was highest in prairie dog habitat, slightly lower in agricultural fields, and substantially lower in grassland. These results provided valuable insight to focus future research into Mountain Plover ecology and conservation. ?? The Cooper Ornithological Society 2006.

Condor↗
USGS2004

Estimating survival probabilities of unmarked dependent young when detection is imperfect

We present a capture–recapture modeling approach to the estimation of survival probability of dependent chicks when only the attending adult bird is marked. The model requires that the bird's nest is found prior to hatching and that the number of eggs that hatch are counted. Subsequent data are sightings of the marked adult and a count of chicks with the adult. The model allows for imperfect detection of chicks, but the number of chicks can never exceed the number of eggs in the nest (i.e., adults cannot adopt chicks). We use data from radio-tagged adult Mountain Plovers ( Charadrius montanus ) and their unmarked chicks as an example. We present the model in terms of precocial bird species, but the method extends to many other taxa.

The Condor↗