Effective Use of Trails for Assessing Terrestrial Salamander Abundance and Detection: A Case Study at Great Smoky Mountains National Park

Milanovich, Joseph R.; Hocking, Daniel J.; Peterman, William E.; Crawford, John A.

doi:10.3375/043.035.0412

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…To date, N-mixture models have described basic changes in populations varying along environmental and biological gradients when detection is imperfect (Royle and Nichols 2003). For example, N-mixture models have revealed effects of hiking trails on salamanders (Milanovich et al 2015), drought on alligators (Waddle et al 2015), and localized competition between native and invasive geckos (Buckland et al 2014). In the present study, we integrated multiple physiologically relevant covariates into an Nmixture modeling framework to account for dispersal of individuals in a newly established population of the endangered St. Croix ground lizard (Ameiva polops), five years after a conservation introduction (translocation outside the native range; Seddon et al 2012, Fitzgerald et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Dispersal and population state of an endangered island lizard following a conservation translocation

Angeli

Lundgren

Pollock

et al. 2018

Ecological Applications

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Population size is widely used as a unit of ecological analysis, yet to estimate population size requires accounting for observed and latent heterogeneity influencing dispersion of individuals across landscapes. In newly established populations, such as when animals are translocated for conservation, dispersal and availability of resources influence patterns of abundance. We developed a process to estimate population size using N-mixture models and spatial models for newly established and dispersing populations. We used our approach to estimate the population size of critically endangered St. Croix ground lizards (Ameiva polops) five years after translocation of 57 individuals to Buck Island, an offshore island of St. Croix, United States Virgin Islands. Estimates of population size incorporated abiotic variables, dispersal limits, and operative environmental temperature available to the lizards to account for low species detection. Operative environmental temperature and distance from the translocation site were always important in fitting the N-mixture model indicating effects of dispersal and species biology on estimates of population size. We found that the population is increasing its range across the island by 5-10% every six months. We spatially interpolated site-specific abundance from the N-mixture model to the entire island, and we estimated 1,473 (95% CI, 940-1,802) St. Croix ground lizards on Buck Island in 2013 corresponding to survey results. This represents a 26-fold increase since the translocation. We predicted the future dispersal of the lizards to all habitats on Buck Island, with the potential for the population to increase by another five times in the future. Incorporating biologically relevant covariates as explicit parameters in population models can improve predictions of population size and the future spread of species introduced to new localities.

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Section: Introductionmentioning

confidence: 99%

Dispersal and population state of an endangered island lizard following a conservation translocation

Angeli

Lundgren

Pollock

et al. 2018

Ecological Applications

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show abstract

“…Many fish biologists have focused on weekly, monthly, or summer-only metrics of stream temperature to relate warm conditions to trout distributions [Al-Chokhachy et al, 2013b;Jones et al, 2014]. However, daily temperatures are useful because they can be used to understand and model activity or detection conditional on the thermal conditions at the time of sampling [Kery, 2010;Hocking et al, 2013;Milanovich et al, 2015]. They can also be summarized into any derived metrics of interest by scientists or managers [Jackson et al, 2018].…”

Section: Introductionmentioning

confidence: 99%

A hierarchical model of daily stream temperature for regional predictions

Hocking

O'Neil

Letcher

2018

Preprint

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Stream temperature is an important exogenous factor influencing populations of stream organisms such as fish, amphibians, and invertebrates. Many states regulate stream protections based on temperature. Therefore, stream temperature models are important, particularly for estimating thermal regimes in unsampled space and time. To help meet this need, we developed a hierarchical model of daily stream temperature and applied it across the eastern United States. Our model accommodates many of the key challenges associated with daily stream temperature models including the lagged response of water temperature to changes in air temperature, incomplete and widely-varying observed time series, spatial and temporal autocorrelation, and the inclusion of predictors other than air temperature. We used 248,517 daily stream temperature records from 1,352 streams to fit our model and 100,909 records were withheld for model validation. Our model had a root mean squared error of 0.61 C for the fitted data and 2.03 C for the validation data, indicating excellent fit and good predictive power for understanding regional temperature patterns. We then used our model to predict daily stream temperatures from 1980 - 2015 for all streams <200 km2 from Maine to Virginia. From these, we calculated derived stream metrics including mean July temperature, mean summer temperature, and the thermal sensitivity of each stream reach to changes in air temperature. Although generally water temperature follows similar latitudinal and altitudinal patterns as air temperature, there are considerable differences at the reach scale based on landscape and land-use factors.

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“…A second limitation is the inability to distinguish between process and observation error to account for imperfect detection (Peterson et al 2013). For example, if sampling is done in a series of transects, all sites within a transect are treated the same (Hocking et al 2013, Peterman and Semlitsch 2013, Milanovich et al 2015) even though it is likely that adjacent sites are more correlated than distant sites at the opposite ends of the transect. This results in a problem of inference regarding the populations and environmental effects on the population, particularly when the probability of detection is variable in time and space.…”

Section: Introductionmentioning

confidence: 99%

“…This coarse grouping does not allow for autocorrelation as a function of distance. For example, if sampling is done in a series of transects, all sites within a transect are treated the same (Hocking et al 2013, Peterman and Semlitsch 2013, Milanovich et al 2015 even though it is likely that adjacent sites are more correlated than distant sites at the opposite ends of the transect. A final limitation of current spatial stream models is the computational challenges with analyzing large networks due to estimating large covariance structures (Peterson et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A geostatistical state‐space model of animal densities for stream networks

Hocking

Thorson

O'Neil

et al. 2018

Ecological Applications

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Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty underestimated. We developed a novel statistical method to account for spatiotemporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 yr. Increasing the number of survey sites within the network improved the performance of the nonspatial model but only marginally improved the density estimates in the spatiotemporal model. We applied this model to brook trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 yr from 1981 to 2014. We found the model including temporal and spatiotemporal autocorrelation best described young of the year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately high spatiotemporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatiotemporal correlation and higher temporal autocorrelation.

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Effective Use of Trails for Assessing Terrestrial Salamander Abundance and Detection: A Case Study at Great Smoky Mountains National Park

Cited by 11 publications

References 34 publications

Dispersal and population state of an endangered island lizard following a conservation translocation

Dispersal and population state of an endangered island lizard following a conservation translocation

A hierarchical model of daily stream temperature for regional predictions

A geostatistical state‐space model of animal densities for stream networks

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