Geology topics

Jamie S. Sanderlin

Publications and source records attributed to Jamie S. Sanderlin.

4 recordsLinked to original sources

USGS2024

Designing count-based studies in a world of hierarchical models

Advances in hierarchical modeling have improved estimation of ecological parameters from count data, especially those quantifying population abundance, distribution, and dynamics by explicitly accounting for observation processes, particularly incomplete detection. Even hierarchical models that account for incomplete detection, however, cannot compensate for data limitations stemming from poorly planned sampling. Ecologists therefore need guidance for planning count-based studies that follow established sampling theory, collect appropriate data, and apply current modeling approaches to answer their research questions. We synthesize available literature relevant to guiding count-based studies. Considering the central historical and ongoing contributions of avian studies to ecological knowledge, we focus on birds as a case study for this review, but the basic principles apply to all populations whose members are sufficiently observable to be counted. The sequence of our review represents the thought process in which we encourage ecologists to engage 1) the research question(s) and population parameters to measure, 2) sampling design, 3) analytical framework, 4) temporal design, and 5) survey protocol. We also provide 2 hypothetical demonstrations of these study plan components representing different research questions and study systems. Mirroring the structure of hierarchical models, we suggest researchers primarily focus on the ecological processes of interest when designing their approach to sampling, and wait to consider logistical constraints of data collection and observation processes when developing the survey protocol. We offer a broad framework for researchers planning count-based studies, while pointing to relevant literature elaborating on particular tools and concepts.

Journal of Wildlife Management↗

USGS2022

Climate refugia for Pinus spp. in topographic and bioclimatic environments of the Madrean sky islands of México and the United States

Climate refugia, or places where habitats are expected to remain relatively buffered from regional climate extremes, provide an important focus for science and conservation planning. Within high-priority, multi-jurisdictional landscapes like the Madrean sky islands of the United States and México, efforts to identify and manage climate refugia are hindered by the lack of high-quality and consistent transboundary datasets. To fill these data gaps, we assembled a bi-national field dataset ( n = 1416) for five pine species ( Pinus spp.) and used generalized boosted regression to model pine habitats in relation to topographic variability as a basis for identifying potential microrefugia at local scales in the context of current species’ distribution patterns. We developed additional models to quantify climatic refugial attributes using coarse scale bioclimatic variables and finer scale seasonal remote sensing indices. Terrain metrics including ruggedness, slope position, and aspect defined microrefugia for pines within elevation ranges preferred by each species. Response to bioclimatic variables indicated that small shifts in climate were important to some species (e.g., P. chihuahuana, P. strobiformis ), but others exhibited a broader tolerance (e.g., P. arizonica ). Response to seasonal climate was particularly important in modeling microrefugia for species with open canopy structure and where regular fires occur (e.g., P. engelmannii and P. chihuahuana ). Hotspots of microrefugia differed among species and were either limited to northern islands or occurred across central or southern latitudes. Mapping and validation of refugia and their ecological functions are necessary steps in developing regional conservation strategies that cross jurisdictional boundaries. A salient application will be incorporation of climate refugia in management of fire to restore and maintain pine ecology.

Arizona, Chihuahua, New Mexico, Sonora↗

USGS2020

Contemporary fire regimes provide a critical perspective on restoration needs in the Mexico-United States borderlands

The relationship between people and wildfire has always been paradoxical: fire is an essential ecological process and management tool, but can also be detrimental to life and property. Consequently, fire regimes have been modified throughout history through both intentional burning to promote benefits and active suppression to reduce risks. Reintroducing fire and its benefits back into the Sky Island mountains of the United States-Mexico borderlands has the potential to reduce adverse effects of altered fire regimes and build resilient ecosystems and human communities. To help guide regional fire restoration, we describe the frequency and severity of recent fires over a 32-year period (1985-2017) across a vast binational region in the United States-Mexico borderlands and assess variation in fire frequency and severity across climate gradients and in relation to vegetation and land tenure classes. We synthesize relevant literature on historical fire regimes within 9 major vegetation types and assess how observed contemporary fire characteristics vary from expectations based on historical patterns. Less than 28% of the study area burned during the observation period, excluding vegetation types in warmer climates that are not adapted to fire (eg, Desertscrub and Thornscrub). Average severity of recent fires was low despite some extreme outliers in cooler, wetter environments. Midway along regional temperature and precipitation gradients, approximately 64% of Pine-Oak Forests burned at least once, with fire frequencies that mainly corresponded to historical expectations on private lands in Mexico but less so on communal lands, suggesting the influence of land management. Fire frequency was higher than historical expectations in extremely cool and wet environments that support forest types such as Spruce-Fir, indicating threats to these systems possibly attributable to drought and other factors. In contrast, fires were absent or infrequent across large areas of Woodlands (~73% unburned) and Grasslands (~88% unburned) due possibly to overgrazing, which reduces abundance and continuity of fine fuels needed to carry fire. Our findings provide a new depiction of fire regimes in the Sky Islands that can help inform fire management, restoration, and regional conservation planning, fostered by local and traditional knowledge and collaboration among landowners and managers.

Arizona, Sonora↗

USGS2020

A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states

Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population models and is much faster. Our manuscript has two goals: 1) to introduce readers unfamiliar with marginalization to the concept and provide worked examples, and 2) to address topics associated with marginalization that have not been previously synthesized and are relevant to both Bayesian and maximum likelihood models. We begin by explaining marginalization using a Cormack-Jolly-Seber model. Next, we apply marginalization to multistate capture-recapture, community occupancy, and integrated population models and briefly discuss random effects, priors, and pseudo-R2. Then, we focus on recovery of discrete latent states, defining different types of conditional probabilities and showing how quantities such as population abundance or species richness can be estimated in marginalized code. Lastly, we show that occupancy and site abundance models with auto-covariates can be fit with marginalized code with minimal impact on parameter estimates. Marginalized code was anywhere from five to >1000 times faster than discrete code. Differences in inferences were minimal using marginalized code. Discrete latent states and fully conditional approaches provide the best estimates of conditional probabilities for a given site or individual. However, estimates for parameters and derived quantities such as species richness and abundance were minimally affected by marginalization and use of imperfect estimates of conditional probabilities. The results applied even when auto-covariates based on imperfect estimates of conditional probabilities were used. Understanding how marginalization works shrinks the divide between Bayesian and maximum likelihood approaches to population models. Some models that have only been presented in a Bayesian framework can easily be fit in maximum likelihood. On the other hand, factors such as informative priors, random effects, or pseudo-R2 values may motivate a Bayesian approach in some applications. An understanding of marginalization allows users to minimize the speed that is sacrificed when switching from a maximum likelihood approach. Widespread application of marginalization in Bayesian population models will facilitate more thorough simulation studies, comparisons of alternative model structures, and faster learning.

Ecological Applications↗