An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Williams, Perry J.; Hooten, Mevin B.; Womble, Jamie N.; Esslinger, George G.; Bower, Michael R.; Hefley, Trevor J.

doi:10.1002/ecy.1643

Cited by 49 publications

(107 citation statements)

References 37 publications

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“…We chose a reflective boundary condition for considering dispersal probability across the edge of our study area (Conn et al, 2015;Williams et al, 2017). As most elements of the shortterm movement probability are zero, the annual dispersal probability can be efficiently calculated by sparse matrix implementation.…”

Section: Parameter Modelsmentioning

confidence: 99%

“…For dispersal process, advection and diffusion are definitely distinguished by directionality of movement. This is still computationally impractical in our modeling, but applying rough approximation (e.g., ignoring dispersal-related demographic stochasticity;Wikle, 2003;Williams et al, 2017) may help in modeling density-dependent dispersal. White to red colors represent low to a high population growth rate, while blue to red colors represent a negative to positive dispersal habitat preference 2017).…”

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confidence: 99%

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Estimating range expansion of wildlife in heterogeneous landscapes: A spatially explicit state‐space matrix model coupled with an improved numerical integration technique

Osada

Kuriyama

Asada

et al. 2018

Ecology and Evolution

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Dispersal as well as population growth is a key demographic process that determines population dynamics. However, determining the effects of environmental covariates on dispersal from spatial‐temporal abundance proxy data is challenging owing to the complexity of model specification for directional dispersal permeability and the extremely high computational loads for numerical integration. In this paper, we present a case study estimating how environmental covariates affect the dispersal of Japanese sika deer by developing a spatially explicit state‐space matrix model coupled with an improved numerical integration technique (Markov chain Monte Carlo with particle filters). In particular, we explored the environmental drivers of inhomogeneous range expansion, characteristic of animals with short dispersal. Our model framework successfully reproduced the complex population dynamics of sika deer, including rapid changes in densely populated areas and distribution fronts within a decade. Furthermore, our results revealed that the inhomogeneous range expansion of sika deer seemed to be primarily caused by the dispersal process (i.e., movement barriers in fragmented forests) rather than population growth. Our state‐space matrix model enables the inference of population dynamics for a broad range of organisms, even those with low dispersal ability, in heterogeneous landscapes, and could address many pressing issues in conservation biology and ecosystem management.

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Section: Parameter Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

Estimating range expansion of wildlife in heterogeneous landscapes: A spatially explicit state‐space matrix model coupled with an improved numerical integration technique

Osada

Kuriyama

Asada

et al. 2018

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…Importantly, collaboration between scientists, resource managers and policy makers also ensures that the program remains relevant [21, 90, 91]. Together, these components readily differentiate the paradigm from ad hoc , reactive, or surveillance monitoring [88, 92, 93]. …”

Section: Discussionmentioning

confidence: 99%

Understanding meta-population trends of the Australian fur seal, with insights for adaptive monitoring

et al. 2018

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Effective ecosystem-based management requires estimates of abundance and population trends of species of interest. Trend analyses are often limited due to sparse or short-term abundance estimates for populations that can be logistically difficult to monitor over time. Therefore it is critical to assess regularly the quality of the metrics in long-term monitoring programs. For a monitoring program to provide meaningful data and remain relevant, it needs to incorporate technological improvements and the changing requirements of stakeholders, while maintaining the integrity of the data. In this paper we critically examine the monitoring program for the Australian fur seal (AFS) Arctocephalus pusillus doriferus as an example of an ad-hoc monitoring program that was co-ordinated across multiple stakeholders as a range-wide census of live pups in the Austral summers of 2002, 2007 and 2013. This 5-yearly census, combined with historic counts at individual sites, successfully tracked increasing population trends as signs of population recovery up to 2007. The 2013 census identified the first reduction in AFS pup numbers (14,248 live pups, -4.2% change per annum since 2007), however we have limited information to understand this change. We analyse the trends at breeding colonies and perform a power analysis to critically examine the reliability of those trends. We then assess the gaps in the monitoring program and discuss how we may transition this surveillance style program to an adaptive monitoring program than can evolve over time and achieve its goals. The census results are used for ecosystem-based modelling for fisheries management and emergency response planning. The ultimate goal for this program is to obtain the data we need with minimal cost, effort and impact on the fur seals. In conclusion we identify the importance of power analyses for interpreting trends, the value of regularly assessing long-term monitoring programs and proper design so that adaptive monitoring principles can be applied.

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“…Diffusion refers to the process of spreading out over an increasingly larger area through time (Skellam 1951, Wikle andHooten 2010). Ecological diffusion is a flexible diffusion model that accommodates this variation in motility by predicting animals will eventually accumulate in desirable habitats, and leave or avoid undesirable ones (Turchin 1998, Garlick et al 2011, Hefley et al 2017, Williams et al 2017b). During diffusion, individual organisms are usually influenced by habitat type.…”

Section: Optimal Dynamic Survey Designmentioning

confidence: 99%

“…These dynamics are often ignored when developing spatial survey designs (Wikle and Royle 2005). There has been a proliferation of statistical methods for modeling and forecasting the distribution and abundance of a spreading population (e.g., Wikle 2003, Wikle and Hooten 2006, Hooten et al 2007, Hooten and Wikle 2008, Williams et al 2017b. There has been a proliferation of statistical methods for modeling and forecasting the distribution and abundance of a spreading population (e.g., Wikle 2003, Wikle and Hooten 2006, Hooten et al 2007, Hooten and Wikle 2008, Williams et al 2017b.…”

Section: Introductionmentioning

confidence: 99%

Monitoring dynamic spatio‐temporal ecological processes optimally

Williams

Hooten

Womble

et al. 2018

Ecology

Self Cite

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Population dynamics vary in space and time. Survey designs that ignore these dynamics may be inefficient and fail to capture essential spatio-temporal variability of a process. Alternatively, dynamic survey designs explicitly incorporate knowledge of ecological processes, the associated uncertainty in those processes, and can be optimized with respect to monitoring objectives. We describe a cohesive framework for monitoring a spreading population that explicitly links animal movement models with survey design and monitoring objectives. We apply the framework to develop an optimal survey design for sea otters in Glacier Bay. Sea otters were first detected in Glacier Bay in 1988 and have since increased in both abundance and distribution; abundance estimates increased from 5 otters to >5,000 otters, and they have spread faster than 2.7 km/yr. By explicitly linking animal movement models and survey design, we are able to reduce uncertainty associated with forecasting occupancy, abundance, and distribution compared to other potential random designs. The framework we describe is general, and we outline steps to applying it to novel systems and taxa.

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An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Cited by 49 publications

References 37 publications

Estimating range expansion of wildlife in heterogeneous landscapes: A spatially explicit state‐space matrix model coupled with an improved numerical integration technique

Estimating range expansion of wildlife in heterogeneous landscapes: A spatially explicit state‐space matrix model coupled with an improved numerical integration technique

Understanding meta-population trends of the Australian fur seal, with insights for adaptive monitoring

Monitoring dynamic spatio‐temporal ecological processes optimally

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