Climate and the range dynamics of species with imperfect detection

Altwegg, Res; Wheeler, Marius; Erni, Birgit

doi:10.1098/rsbl.2008.0051

Cited by 49 publications

(48 citation statements)

References 19 publications

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“…Because the logistic regression cannot account for the possibility of a species being present but not seen, it will usually provide biased estimates of species occurrence and species-habitat relationships (Tyre et al 2003, Altwegg et al 2008, Ke´ry et al 2010. In 2002, MacKenzie et al developed an approach termed occupancy modeling, which became the new standard for estimating species occurrences while accounting for detection errors.…”

Section: Introductionmentioning

confidence: 99%

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas

Broms

Johnson

Altwegg

et al. 2014

Ecological Applications

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Abstract. Determining the range of a species and exploring species-habitat associations are central questions in ecology and can be answered by analyzing presence-absence data. Often, both the sampling of sites and the desired area of inference involve neighboring sites; thus, positive spatial autocorrelation between these sites is expected. Using survey data for the Southern Ground Hornbill (Bucorvus leadbeateri ) from the Southern African Bird Atlas Project, we compared advantages and disadvantages of three increasingly complex models for species occupancy: an occupancy model that accounted for nondetection but assumed all sites were independent, and two spatial occupancy models that accounted for both nondetection and spatial autocorrelation. We modeled the spatial autocorrelation with an intrinsic conditional autoregressive (ICAR) model and with a restricted spatial regression (RSR) model. Both spatial models can readily be applied to any other gridded, presence-absence data set using a newly introduced R package.The RSR model provided the best inference and was able to capture small-scale variation that the other models did not. It showed that ground hornbills are strongly dependent on protected areas in the north of their South African range, but less so further south. The ICAR models did not capture any spatial autocorrelation in the data, and they took an order of magnitude longer than the RSR models to run. Thus, the RSR occupancy model appears to be an attractive choice for modeling occurrences at large spatial domains, while accounting for imperfect detection and spatial autocorrelation.

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Section: Introductionmentioning

confidence: 99%

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas

Broms

Johnson

Altwegg

et al. 2014

Ecological Applications

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“…Multi‐season occupancy models have been applied broadly in ecology and have demonstrated utility in illuminating drivers and patterns of dynamic species distributions (e.g. Altwegg et al ., ; Kéry et al ., ).…”

Section: Methodsmentioning

confidence: 97%

Applying dynamic species distribution modelling to lek‐mating species

Sadoti

Albright

Johnson

2016

Journal of Biogeography

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Aim Lek‐mating species, in which males congregate to display and copulate with females, are of long‐standing behavioural, economic and conservation interest. Monitoring programs often target areas in which these species aggregate for mating (lek arenas) because of the often cryptic behaviours of these species when away from lek arenas. Due to survey methodologies and the ecology of many lek‐mating species, the building of species distribution models (SDMs) requires researchers to consider issues that are generally absent when modelling distributions of non‐lek‐mating species. We describe these considerations and illustrate the advantages of employing dynamic SDMs to address non‐equilibrium between a lek‐mating species (Lesser Prairie‐Chicken Tympanuchus pallidicinctus) and its environment. Location New Mexico, U.S.A. Methods We employed a 27‐year data set of 2771 lek surveys from a T. pallidicinctus metapopulation to build dynamic SDMs using multi‐season occupancy modelling. We considered covariates (1) indexing past abundance and distribution patterns expected to influence dispersal and local persistence, (2) describing landscape and weather dynamics expected to influence local recruitment, and (3) describing survey‐level conditions expected to influence lek attendance. Results The most supported dynamic SDM indicated an overall decline in occurrence and an ending distribution more restricted to the northern part of the 1222 km2 study area. Local colonization was best predicted by weather and landscape dynamics, while local extinction was best predicted by population processes and weather. Main conclusions Conservation of T. pallidicinctus will likely face challenges from (1) the reduced likelihood of colonization in areas of historical shrub‐targeting herbicide application and (2) projected patterns of seasonal temperature and precipitation under climate change. In considering lek‐mating species in general, we recommend the use of dynamic SDMs for an improved understanding of factors influencing distributions through time. Our approach and related efforts represent an important step in forecasting distributions under projected scenarios of environmental change to assist the conservation of these species.

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“…Dynamic models explicitly estimate colonization and local extinction between seasons. Extinction and colonization probabilities can be modelled as a function of grid cell‐specific covariates, offering exciting opportunities to test hypotheses about the drivers of occupancy dynamics (Altwegg, Wheeler & Erni, ).…”

Section: Introductionmentioning

confidence: 99%

Occupancy models for citizen‐science data

2019

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Large‐scale citizen‐science projects, such as atlases of species distribution, are an important source of data for macroecological research, for understanding the effects of climate change and other drivers on biodiversity, and for more applied conservation tasks, such as early‐warning systems for biodiversity loss. However, citizen‐science data are challenging to analyse because the observation process has to be taken into account. Typically, the observation process leads to heterogeneous and non‐random sampling, false absences, false detections, and spatial correlations in the data. Increasingly, occupancy models are being used to analyse atlas data. We advocate a dual approach to strengthen inference from citizen science data for the questions the programme is intended to address: (a) the survey design should be chosen with a particular set of questions and associated analysis strategy in mind and (b) the statistical methods should be tailored not only to those questions but also to the specific characteristics of the data. We review the consequences of particular survey design choices that typically need to be made in atlas‐style citizen‐science projects. These include spatial resolution of the sampling units, allocation of effort in space, and collection of information about the observation process. On the analysis side, we review extensions of the basic occupancy models that are frequently necessary with atlas data, including methods for dealing with heterogeneity, non‐independent detections, false detections, and violation of the closure assumption. New technologies, such as cell‐phone apps and fixed remote detection devices, are revolutionizing citizen‐science projects. There is an opportunity to maximize the usefulness of the resulting datasets if the protocols are rooted in robust statistical designs and data analysis issues are being considered. Our review provides guidelines for designing new projects and an overview of the current methods that can be used to analyse data from such projects.

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Climate and the range dynamics of species with imperfect detection

Cited by 49 publications

References 19 publications

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas

Applying dynamic species distribution modelling to lek‐mating species

Occupancy models for citizen‐science data

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