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Abstract
Bigheaded carps are invasive fishes threatening to invade the Great Lakes basin and
establish spawning populations, and have been monitored using environmental
DNA (
eDNA). Not only does
eDNA hold potential for detecting the presence of species, but may also allow for quantitative
comparisons like relative abundance of species across time or space. We examined the
relationships among bigheaded carp movement, hydrography, spawning and
eDNA on the Wabash River,
IN,
USA. We found positive relationships between
eDNA and movement and
eDNA and hydrography. We did not find a relationship between
eDNA and spawning activity in the form of drifting eggs. Our first finding demonstrates
how
eDNA may be used to monitor species abundance, whereas our second finding illustrates
the need for additional research into
eDNA methodologies. Current applications of
eDNA are widespread, but the relatively new technology requires further refinement.
Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.
This is an open access article under the terms of the
http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
History
Date
received
: 28
August
2015
Date
revision received
: 04
April
2016
Date
accepted
: 07
April
2016
Page count
Pages: 9
Funding
Funded by: U.S. Environmental Protection Agency – Great Lakes Restoration Initiative
Funded by: U.S. Geological Survey
Funded by: Indiana Department of Natural Resources
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