Hidden in plain sight: Integrated population models to resolve partially observable latent population structure
Abstract Population models often require detailed information on sex‐, age‐, or size‐specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
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| Series: | Ecosphere |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ecs2.4321 |
| _version_ | 1811173196842401792 |
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| author | Abigail J. Lawson Patrick G. R. Jodice Thomas R. Rainwater Kylee D. Dunham Morgan Hart Joseph W. Butfiloski Philip M. Wilkinson K. W. McFadden Clinton T. Moore |
| author_facet | Abigail J. Lawson Patrick G. R. Jodice Thomas R. Rainwater Kylee D. Dunham Morgan Hart Joseph W. Butfiloski Philip M. Wilkinson K. W. McFadden Clinton T. Moore |
| author_sort | Abigail J. Lawson |
| collection | DOAJ |
| description | Abstract Population models often require detailed information on sex‐, age‐, or size‐specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage‐ or size‐structured species subject to consumptive use. American alligators (Alligator mississippiensis; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class‐specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state‐space count model to derive estimates of size class‐specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class‐specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high‐resolution output of latent population structure that is partially observed during the monitoring process. |
| first_indexed | 2024-04-10T17:43:50Z |
| format | Article |
| id | doaj.art-fa9e9130c0e04a5eb0affb218081f726 |
| institution | Directory Open Access Journal |
| issn | 2150-8925 |
| language | English |
| last_indexed | 2024-04-10T17:43:50Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj.art-fa9e9130c0e04a5eb0affb218081f7262023-02-03T07:25:47ZengWileyEcosphere2150-89252022-12-011312n/an/a10.1002/ecs2.4321Hidden in plain sight: Integrated population models to resolve partially observable latent population structureAbigail J. Lawson0Patrick G. R. Jodice1Thomas R. Rainwater2Kylee D. Dunham3Morgan Hart4Joseph W. Butfiloski5Philip M. Wilkinson6K. W. McFadden7Clinton T. Moore8Department of Forestry and Environmental Conservation Clemson University Clemson South Carolina USAU.S. Geological Survey, South Carolina Cooperative Fish and Wildlife Research Unit Clemson University Clemson South Carolina USADepartment of Forestry and Environmental Conservation Clemson University Clemson South Carolina USAU.S. Geological Survey, Eastern Ecological Science Center Laurel Maryland USASouth Carolina Department of Natural Resources Columbia South Carolina USASouth Carolina Department of Natural Resources Columbia South Carolina USATom Yawkey Wildlife Center Georgetown South Carolina USAU.S. Geological Survey, South Carolina Cooperative Fish and Wildlife Research Unit Clemson University Clemson South Carolina USAU.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural Resources University of Georgia Athens Georgia USAAbstract Population models often require detailed information on sex‐, age‐, or size‐specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage‐ or size‐structured species subject to consumptive use. American alligators (Alligator mississippiensis; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class‐specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state‐space count model to derive estimates of size class‐specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class‐specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high‐resolution output of latent population structure that is partially observed during the monitoring process.https://doi.org/10.1002/ecs2.4321Alligator mississippiensisBayesianharvesthierarchical modelintegrated population modelpopulation dynamics |
| spellingShingle | Abigail J. Lawson Patrick G. R. Jodice Thomas R. Rainwater Kylee D. Dunham Morgan Hart Joseph W. Butfiloski Philip M. Wilkinson K. W. McFadden Clinton T. Moore Hidden in plain sight: Integrated population models to resolve partially observable latent population structure Ecosphere Alligator mississippiensis Bayesian harvest hierarchical model integrated population model population dynamics |
| title | Hidden in plain sight: Integrated population models to resolve partially observable latent population structure |
| title_full | Hidden in plain sight: Integrated population models to resolve partially observable latent population structure |
| title_fullStr | Hidden in plain sight: Integrated population models to resolve partially observable latent population structure |
| title_full_unstemmed | Hidden in plain sight: Integrated population models to resolve partially observable latent population structure |
| title_short | Hidden in plain sight: Integrated population models to resolve partially observable latent population structure |
| title_sort | hidden in plain sight integrated population models to resolve partially observable latent population structure |
| topic | Alligator mississippiensis Bayesian harvest hierarchical model integrated population model population dynamics |
| url | https://doi.org/10.1002/ecs2.4321 |
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