Dynamic occupancy models for explicit colonization processes

Broms, Kristin; Hooten, Mevin B.; Johnson, Devin S.; Altwegg, Res; Conquest, Loveday L.

doi:10.1890/15-0416.1

Cited by 63 publications

(66 citation statements)

References 38 publications

(92 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To deal with this second issue, occupancy models have been extended (Mackenzie et al , Royle and Kéry ) to account for the influence of dynamic processes such as colonization and extinction on the species range dynamics (Mackenzie et al ). So‐called multi‐season or dynamic site‐occupancy models are increasingly used to assess the range dynamics of expanding or invasive species (Bled et al , Broms et al ), but remain rarely applied to carnivores (Marcelli and Fusillo , Miller et al ).…”

mentioning

confidence: 99%

Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

et al. 2017

View full text Add to dashboard Cite

While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to 1) their imperfect detectability, 2) their dynamic ranges over time and 3) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves Canis lupus that have been recolonizing France since the early 1990s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time‐ and space‐varying sampling effort using dynamic site‐occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500 m and positively influenced by the number of observed occupied sites at short and long‐distances, forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decision‐makers to target sites where large carnivores are likely to occur and mitigate conflicts.

show abstract

mentioning

confidence: 99%

Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

et al. 2017

View full text Add to dashboard Cite

show abstract

“…; see also Broms et al . ). In these models, the adjacency of sites must be defined a priori , because probabilities of occupancy, local colonization and extinction are assumed to depend on the occupancy states of neighbouring sites.…”

Section: Discussionmentioning

confidence: 97%

A multistate dynamic site occupancy model for spatially aggregated sessile communities

Fukaya

Royle²,

Okuda

et al. 2016

Methods Ecol Evol

View full text Add to dashboard Cite

Summary Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e. a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations. We developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error. By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real data set of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account. Results suggest the importance of accounting for resampling error and local community structure for developing management plans that are based on Markovian models. Our approach provides a solution to this problem that is applicable to broad sessile communities. It can even accommodate an anisotropic spatial correlation of species composition, and may also serve as a basis for inferring complex nonlinear ecological dynamics.

show abstract

“…In particular, spatial and temporal dependence may be useful for representing limits to species occurrence [47]. Future developments of this approach might prioritize inclusion of spatiotemporally explicit colonization dynamics that account for occupancy status of neighboring sites, habitat quality, and dispersal functions [48]. These approaches will prove useful to understand how much of the spread of an invasive symbiont may be due to changes in the host distribution vs. changes in the symbiont distribution alone, with potential applications to the management of emerging infectious diseases [49].…”

Section: Discussionmentioning

confidence: 99%

Multilevel Models for the Distribution of Hosts and Symbionts

2016

View full text Add to dashboard Cite

Symbiont occurrence is influenced by host occurrence and vice versa, which leads to correlations in host-symbiont distributions at multiple levels. Interactions between co-infecting symbionts within host individuals can cause correlations in the abundance of two symbiont species across individual hosts. Similarly, interactions between symbiont transmission and host population dynamics can drive correlations between symbiont and host abundance across habitat patches. If ignored, these interactions can confound estimated responses of hosts and symbionts to other factors. Here, we present a general hierarchical modeling framework for distributions of hosts and symbionts, estimating correlations in host-symbiont distributions at the among-site, within-site, among-species, and among-individual levels. We present an empirical example from a multi-host multi-parasite system involving amphibians and their micro- and macroparasites. Amphibian hosts and their parasites were correlated at multiple levels of organization. Macroparasites often co-infected individual hosts, but rarely co-infected with the amphibian chytrid fungus. Such correlations may result from interactions among parasites and hosts, joint responses to environmental factors, or sampling bias. Joint host-symbiont models account for environmental constraints and species interactions while partitioning variance and dependence in abundance at multiple levels. This framework can be adapted to a wide variety of study systems and sampling designs.

show abstract

Dynamic occupancy models for explicit colonization processes

Cited by 63 publications

References 38 publications

Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

A multistate dynamic site occupancy model for spatially aggregated sessile communities

Multilevel Models for the Distribution of Hosts and Symbionts

Contact Info

Product

Resources

About