Geology topics

Krishna Pacifici

Publications and source records attributed to Krishna Pacifici.

21 records · Page 2Linked to original sources

USGS2015

Occupancy estimation for rare species using a spatially-adaptive sampling design

Summary 1. Spatially clustered populations create unique challenges for conservation monitoring programmes. Advances in methodology typically are focused on either the design or the modelling stage of the study but do not involve integration of both. 2. We integrate adaptive cluster sampling and spatial occupancy modelling by developing two models to handle the dependence induced by cluster sampling. We compare these models to scenarios using simple random sampling and traditional occupancy models via simulation and data collected on a rare plant species, Tamarix ramosissima , found in China. 3. Our simulations show a marked improvement in confidence interval coverage for the new models combined with cluster sampling compared to simple random sampling and traditional occupancy models, with greatest improvement in the presence of low detection probability and spatial correlation in occupancy. 4. Accounting for the design using the simple cluster random-effects model reduces bias considerably, and full spatial modelling reduces bias further, especially for large n when the spatial covariance parameters can be estimated reliably. Both new models build on the strength of occupancy modelling and adaptive sampling and perform at least as well, and often better, than occupancy modelling alone. 5. We believe our approach is unique and potentially useful for a variety of studies directed at patchily distributed, clustered or rare species exhibiting spatial variation.

Methods in Ecology and Evolution↗

USGS2014

Guidelines for a priori grouping of species in hierarchical community models

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process borrows from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

Ecology and Evolution↗

USGS2012

A two-phase sampling design for increasing detections of rare species in occupancy surveys

1. Occupancy estimation is a commonly used tool in ecological studies owing to the ease at which data can be collected and the large spatial extent that can be covered. One major obstacle to using an occupancy-based approach is the complications associated with designing and implementing an efficient survey. These logistical challenges become magnified when working with rare species when effort can be wasted in areas with none or very few individuals. 2. Here, we develop a two-phase sampling approach that mitigates these problems by using a design that places more effort in areas with higher predicted probability of occurrence. We compare our new sampling design to traditional single-season occupancy estimation under a range of conditions and population characteristics. We develop an intuitive measure of predictive error to compare the two approaches and use simulations to assess the relative accuracy of each approach. 3. Our two-phase approach exhibited lower predictive error rates compared to the traditional single-season approach in highly spatially correlated environments. The difference was greatest when detection probability was high (0·75) regardless of the habitat or sample size. When the true occupancy rate was below 0·4 (0·05-0·4), we found that allocating 25% of the sample to the first phase resulted in the lowest error rates. 4. In the majority of scenarios, the two-phase approach showed lower error rates compared to the traditional single-season approach suggesting our new approach is fairly robust to a broad range of conditions and design factors and merits use under a wide variety of settings. 5. Synthesis and applications. Conservation and management of rare species are a challenging task facing natural resource managers. It is critical for studies involving rare species to efficiently allocate effort and resources as they are usually of a finite nature. We believe our approach provides a framework for optimal allocation of effort while maximizing the information content of the data in an attempt to provide the highest conservation value per unit of effort.

Methods in Ecology and Evolution↗