Geology topics

Janice Reid

Publications and source records attributed to Janice Reid.

4 recordsLinked to original sources

USGS2020

Is your ad hoc model selection strategy affecting your multimodel inference?

(Yackulic) 1. Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. 2. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex model structures for non-target sub-models. 3. We tested the performance of different model selection strategies using four datasets and three model types. For each dataset, we determined the “true” distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of “true” AIC weight we recovered under different model selection strategies. 4. Sequential-by-sub-model strategies often performed poorly. Build-up or secondary candidate sets were more reliable, provided all models within 5 AIC of the top model were carried forward to subsequent stages. The structure of non-target sub-models was less important. 5. Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.

Ecosphere↗

USGS2015

To predict the niche, model colonization and extinction

Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species' niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both because of ongoing invasions and because the distribution of suitable environmental conditions is always changing. This mismatch between the equilibrium assumptions inherent in many analyses and the disequilibrium conditions in the real world leads to inaccurate predictions of species' geographic distributions and suggests the need for theory and analytical tools that avoid equilibrium assumptions. Here, we develop a general theory of environmental associations during periods of transient dynamics. We show that time-invariant relationships between environmental conditions and rates of local colonization and extinction can produce substantial temporal variation in occupancy–environment relationships. We then estimate occupancy–environment relationships during three avian invasions. Changes in occupancy–environment relationships over time differ among species but are predicted by dynamic occupancy models. Since estimates of the occupancy–environment relationships themselves are frequently poor predictors of future occupancy patterns, research should increasingly focus on characterizing how rates of local colonization and extinction vary with environmental conditions.

Ecology↗

USGS2014

The roles of competition and habitat in the dynamics of populations and species distributions

The role of competition in structuring biotic communities at fine spatial scales is well known from detailed process-based studies. Our understanding of competition's importance at broader scales is less resolved and mainly based on static species distribution maps. Here, we bridge this gap by examining the joint occupancy dynamics of an invading (barred owl: Strix varia) and a resident species (Northern spotted owl: Strix occidentalis caurina) in a 1000 km2 study area over a 22 - year period. Past studies of these competitors have focused on the dynamics of one species at a time, hindering efforts to parse out the roles of habitat and competition and to forecast the future of the resident species. In addition, while these studies accounted for the imperfect detection of the focal species, no multiseason analysis of these species has accounted for the imperfect detection of the secondary species, potentially biasing inference. We analyze survey data using models that combine the general multistate-multiseason occupancy modeling framework with autologistic modeling - allowing us to account for important aspects of our study system. We find that local extinction probability increases for each species when the other is present; however, the effect of the invader on the resident is greater. Although the species prefer different habitats, these habitats are highly correlated at the patch scale and the impacts of invader on the resident are greatest in patches that would otherwise be optimal. As a consequence, competition leads to a weaker relationship between habitat and Northern spotted owl occupancy. Colonization and extinction rates of the invader are closely related to neighborhood occupancy, and over the first half of the study the availability of colonists limited the rate of population growth. Competition is likely to exclude the resident species both through its immediate effects on local extinction, and by indirectly lowering colonization rates as Northern spotted owl occupancy declines. Our analysis suggests that dispersal limitation affects both the invasion dynamics and the scale at which the effects of competition are observed. We also provide predictions regarding the potential costs and benefits of managing barred owl populations at different target levels.

Oregon↗

USGS2012

Neighborhood and habitat effects on vital rates: expansion of the Barred Owl in the Oregon Coast Ranges

In this paper, we modify dynamic occupancy models developed for detection-nondetection data to allow for the dependence of local vital rates on neighborhood occupancy, where neighborhood is defined very flexibly. Such dependence of occupancy dynamics on the status of a relevant neighborhood is pervasive, yet frequently ignored. Our framework permits joint inference about the importance of neighborhood effects and habitat covariates in determining colonization and extinction rates. Our specific motivation is the recent expansion of the Barred Owl (Strix varia) in western Oregon, USA, over the period 1990-2010. Because the focal period was one of dramatic range expansion and local population increase, the use of models that incorporate regional occupancy (sources of colonists) as determinants of dynamic rate parameters is especially appropriate. We began our analysis of 21 years of Barred Owl presence/nondetection data in the Tyee Density Study Area (TDSA) by testing a suite of six models that varied only in the covariates included in the modeling of detection probability. We then tested whether models that used regional occupancy as a covariate for colonization and extinction outperformed models with constant or year-specific colonization or extinction rates. Finally we tested whether habitat covariates improved the AIC of our models, focusing on which habitat covariates performed best, and whether the signs of habitat effects are consistent with a priori hypotheses. We conclude that all covariates used to model detection probability lead to improved AIC, that regional occupancy influences colonization and extinction rates, and that habitat plays an important role in determining extinction and colonization rates. As occupancy increases from low levels toward equilibrium, colonization increases and extinction decreases, presumably because there are more and more dispersing juveniles. While both rates are affected, colonization increases more than extinction decreases. Colonization is higher and extinction is lower in survey polygons with more riparian forest. The effects of riparian forest on extinction rates are greater than on colonization rates. Model results have implications for management of the invading Barred Owl, both through habitat alteration and removal.

Oregon↗