Geology topics

Darryl I. MacKenzie

Publications and source records attributed to Darryl I. MacKenzie.

9 recordsLinked to original sources

USGS2026

Listening for extinction: Range-wide occupancy highlights critical risks and priorities for northern spotted owls

The northern spotted owl ( Strix occidentalis caurina ) continues to decline across its range, threatened by habitat disturbances and invasive barred owls ( Strix varia ). We conducted a range-wide spotted owl occupancy assessment using 2.1 million h of passive acoustic recordings from 4081 survey stations within 1027 randomly selected 5-km 2 hexagons across six physiographic regions. Applying multistate occupancy models, we estimated probabilities of landscape use (by at least one northern spotted owl) and pair occupancy (occupied by both a male and a female), while accounting for imperfect and sex-biased detection probability. Detection probability of pairs was low in most regions, primarily due to undetected females, but increased with survey effort. If management decisions can only be made when there is a high probability of detecting both sexes when they are present, then longer surveys are one approach to support the decisions. Landscape use and pair occupancy were positively associated with old-growth forest structure and topographic diversity. We also found a pronounced latitudinal gradient in spotted owl landscape use rates with estimated mean landscape use of 0.17 (SE = 0.03) and 0.16 (SE = 0.02) in the two most northern regions, and 0.54 (SE = 0.04) and 0.52 (SE = 0.05) in the two most southern regions. Pair occupancy rates were higher in southern regions (0.29, SE = 0.03, 0.43, SE = 0.05) and very low in northern regions (0.04, SE = 0.01, 0.03, SE = 0.01). In contrast to spotted owls, barred owls were detected in 90% of northern hexagons and 50% of southern hexagons, and barred owl calling intensity was over eight times higher than spotted owls. These results highlight extinction risks in several regions and suggest that management actions, such as spatially targeted barred owl control and protection of structurally complex habitats, have a narrow temporal window in which they can be effective in lowering the probability of regional extirpations. We generated predictive maps of landscape use and pair occupancy, which could help guide regional conservation strategies. This study demonstrates how passive acoustic monitoring and machine learning can be used for broad-scale ecological monitoring, identifying extinction risk thresholds, supporting adaptive management, and improving conservation outcomes for wide-ranging and elusive species that can reliably be detected through unique vocalizations.

California, Oregon, Washington↗

USGS2019

Implementation of an occupancy-based monitoring protocol for a wide-spread and cryptic species, the New England cottontail Sylvilagus transitionalis

Designing effective long-term monitoring strategies is essential for managing wildlife populations. Implementing a cost-effective, practical monitoring program is especially challenging for widespread but locally rare species. Early successional habitat preferred by the New England cottontail (NEC) has become increasingly rare and fragmented, resulting in substantial declines from their peak distribution in the mid-1900s. The introduction of a possible competitor species, the eastern cottontail (EC), may also have played a role. Uncertainty surrounding how these factors have contributed to NEC declines has complicated management and necessitated development of an appropriate monitoring framework to understand possible drivers of distribution and dynamics.

Wildlife Research↗

USGS2018

Occupancy in community-level studies

Another type of multi-species studies, are those focused on community-level metrics such as species richness. In this chapter we detail how some of the single-species occupancy models described in earlier chapters have been applied, or extended, for use in such studies, while accounting for imperfect detection. We highlight how Bayesian methods using MCMC are particularly useful in such settings to easily calculate relevant community-level summaries based on presence/absence data. These modeling approaches can be used to assess richness at a single point in time, or to investigate changes in the species pool over time.

Book chapter↗

USGS2017

Model-based approaches to deal with detectability: a comment on Hutto (2016)

In a recent paper, Hutto (2016a) challenges the need to account for detectability when interpreting data from point counts. A number of issues with model-based approaches to deal with detectability are presented, and an alternative suggested: surveying an area around each point over which detectability is assumed certain. The article contains a number of false claims and errors of logic, and we address these here. We provide suggestions about appropriate uses of distance sampling and occupancy modeling, arising from an intersection of design- and model-based inference.

Ecological Applications↗

USGS2011

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost‐effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees ( Trichechus manatus latirostris ) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non‐independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non‐independent detection of individuals. We simulated correlated behaviour using beta‐binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta‐binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta‐binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness‐of‐fit procedure to evaluate the fit of beta‐binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta‐binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta‐binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness‐of‐fit when analysing ecological data with N‐mixture models. Indeed, both the simulations and the goodness‐of‐fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non‐independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N‐mixture models to ecological data.

Methods in Ecology and Evolution↗

USGS2010

Simultaneous modeling of habitat suitability, occupancy, and relative abundance: African elephants in Zimbabwe

The recent development of statistical models such as dynamic site occupancy models provides the opportunity to address fairly complex management and conservation problems with relatively simple models. However, surprisingly few empirical studies have simultaneously modeled habitat suitability and occupancy status of organisms over large landscapes for management purposes. Joint modeling of these components is particularly important in the context of management of wild populations, as it provides a more coherent framework to investigate the population dynamics of organisms in space and time for the application of management decision tools. We applied such an approach to the study of water hole use by African elephants in Hwange National Park, Zimbabwe. Here we show how such methodology may be implemented and derive estimates of annual transition probabilities among three dry-season states for water holes: (1) unsuitable state (dry water holes with no elephants); (2) suitable state (water hole with water) with low abundance of elephants; and (3) suitable state with high abundance of elephants. We found that annual rainfall and the number of neighboring water holes influenced the transition probabilities among these three states. Because of an increase in elephant densities in the park during the study period, we also found that transition probabilities from low abundance to high abundance states increased over time. The application of the joint habitat–occupancy models provides a coherent framework to examine how habitat suitability and factors that affect habitat suitability influence the distribution and abundance of organisms. We discuss how these simple models can further be used to apply structured decision-making tools in order to derive decisions that are optimal relative to specified management objectives. The modeling framework presented in this paper should be applicable to a wide range of existing data sets and should help to address important ecological, conservation, and management problems that deal with occupancy, relative abundance, and habitat suitability.

Hwange National Park↗

USGS2009

Dynamic models for problems of species occurrence with multiple states

Recent extensions of occupancy modeling have focused not only on the distribution of species over space, but also on additional state variables (e.g., reproducing or not, with or without disease organisms, relative abundance categories) that provide extra information about occupied sites. These biologist-driven extensions are characterized by ambiguity in both species presence and correct state classification, caused by imperfect detection. We first show the relationships between independently published approaches to the modeling of multistate occupancy. We then extend the pattern-based modeling to the case of sampling over multiple seasons or years in order to estimate state transition probabilities associated with system dynamics. The methodology and its potential for addressing relevant ecological questions are demonstrated using both maximum likelihood (occupancy and successful reproduction dynamics of California Spotted Owl) and Markov chain Monte Carlo estimation approaches (changes in relative abundance of green frogs in Maryland). Just as multistate capture–recapture modeling has revolutionized the study of individual marked animals, we believe that multistate occupancy modeling will dramatically increase our ability to address interesting questions about ecological processes underlying population-level dynamics.

Ecology↗

USGS2007

Sampling design trade-offs in occupancy studies with imperfect detection: Examples and software

Researchers have used occupancy, or probability of occupancy, as a response or state variable in a variety of studies (e.g., habitat modeling), and occupancy is increasingly favored by numerous state, federal, and international agencies engaged in monitoring programs. Recent advances in estimation methods have emphasized that reliable inferences can be made from these types of studies if detection and occupancy probabilities are simultaneously estimated. The need for temporal replication at sampled sites to estimate detection probability creates a trade-off between spatial replication (number of sample sites distributed within the area of interest/inference) and temporal replication (number of repeated surveys at each site). Here, we discuss a suite of questions commonly encountered during the design phase of occupancy studies, and we describe software (program GENPRES) developed to allow investigators to easily explore design trade-offs focused on particularities of their study system and sampling limitations. We illustrate the utility of program GENPRES using an amphibian example from Greater Yellowstone National Park, USA.

Ecological Applications↗

USGS2005

Designing occupancy studies: General advice and allocating survey effort

1. The fraction of sampling units in a landscape where a target species is present (occu pancy) is an extensively used concept in ecology. Yet in many applications the species will not always be detected in a sampling unit even when present, resulting in biased estimates of occupancy. Given that sampling units are surveyed repeatedly within a r elatively short timeframe, a number of similar methods have now been developed to provide unbiased occupancy estimates. However, practical guidance on the efficient design of occupancy studies has been lacking. 2. In this paper we comment on a number of general issues related to designing occu pancy studies, including the need for clear objectives that are explicitly linked to science or management, selection of sampling units, timing of repeat surveys and allocation of survey effort. Advice on the number of repeat surveys per sampling unit is considered in terms of the variance of the occupancy estimator, for three possible study designs. 3. We r ecommend that sampling units should be surveyed a minimum of three times w hen detection probability is high (> 0·5 survey − 1 ), unless a removal design is used. 4. We f ound that an optimal removal design will generally be the most efficient, but we suggest it may be less robust to assumption violations than a standard design. 5. Our results suggest that for a rare species it is more efficient to survey more sampling units less intensively, while for a common species fewer sampling units should be surveyed more intensively. 6. Synthesis and applications . Reliable inferences can only result from quality data. To ma ke the best use of logistical resources, study objectives must be clearly defined; sampling units must be selected, and repeated surveys timed appropriately; and a sufficient n umber of repeated surveys must be conducted. Failure to do so may compromise the integrity of the study. The guidance given here on study design issues is particularly a pplicable to studies of species occurrence and distribution, habitat selection and modelling, metapopulation studies and monitoring programmes.

Journal of Applied Ecology↗