Efficient use of harvest data: An integrated population model for exploited animal populations
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CC-BY-NC-ND-4.0
Abstract
Many populations are affected by hunting or fishing. Models designed to assess the sustainability of harvest management require accurate estimates of demographic parameters (e.g. survival, reproduction) hardly estimable with limited data collected on exploited populations. The joint analysis of different data sources with integrated population models (IPM) is an optimal framework to obtain reliable estimates for parameters usually difficult to estimate, while accounting for imperfect detection and observation error. The IPM built so far for exploited populations have integrated count-based surveys and catch-at-age data into ageclass structured population models. But the age of harvested individuals is difficult to assess and often not recorded, and population counts are often not performed on a regular basis, limiting their use for the monitoring of exploited populations. Here, we propose an IPM that makes efficient use of data commonly collected in exploited marine and terrestrial populations of vertebrates. As individual measures of body mass at both capture and death are often collected in fish and terrestrial game species, our model integrates capture-mark-recapture-recovery data and data collected at death into a body mass-structured population model. It allows the observed number of individuals harvested to be compared with the expected number and provides accurate estimates of demographic parameters. We illustrate the usefulness of this IPM using an emblematic game species distributed worldwide, the wild boar Sus scrofa , as a case study. For this species that has increased in distribution and abundance over the last decades, the model provides accurate and precise annual estimates of key demographic parameters (survival, reproduction, growth) and of population size while accounting for imperfect detection and observation error. To avoid an overexploitation of declining populations or an under-exploitation of increasing populations, it is crucial to gain a good understanding of the dynamics of exploited populations. When managers or conservationists have limited demographic data, the IPM offers a powerful framework to assess population dynamics. Being highly flexible, the approach is broadly applicable to both terrestrial and marine exploited populations for which measures of body mass are commonly recorded and more generally, to all populations suffering from anthropogenic mortality causes.
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- An integrated population model for estimating the relative effects of natural and anthropogenic factors on a threatened population of Pacific trout via crossref
- doi:10.1111/j.0006-341x.2001.00563.x via crossref
- doi:10.37045/aslh-2009-0013 via crossref
- doi:10.1111/j.0006-341x.2002.00540.x via crossref
- doi:10.1016/s0065-2504(09)00405-x via crossref
- doi:10.1080/10618600.1998.10474787 via crossref
- doi:10.1098/rspb.2006.0338 via crossref
- doi:10.1016/j.gecco.2015.06.006 via crossref
- doi:10.1111/1365-2664.12476 via crossref
- doi:10.1111/j.1365-2656.2010.01734.x via crossref
- doi:10.1644/07-mamm-a-164.1 via crossref
- doi:10.1146/annurev.ecolsys.31.1.367 via crossref
- doi:10.1016/b978-0-12-800049-6.00085-8 via crossref
- doi:10.1086/430330 via crossref
- doi:10.1016/b978-0-12-801238-3.11330-3 via crossref
- doi:10.1111/j.1558-5646.2011.01366.x via crossref
- doi:10.1111/1365-2656.12073 via crossref
- doi:10.1111/j.1365-2664.2012.02160.x via crossref
- doi:10.1111/1365-2664.13466 via crossref
- doi:10.1890/06-0953 via crossref
- doi:10.1007/s10344-018-1227-5 via crossref
- doi:10.1139/f94-214 via crossref
- doi:10.1641/0006-3568(2004)054[0297:mfpccf]2.0.co;2 via crossref
- doi:10.1007/s10344-009-0296-x via crossref
- doi:10.1890/14-0932.1 via crossref
- doi:10.1016/j.ecolmodel.2005.05.003 via crossref
- doi:10.1111/j.1467-842x.2005.00371.x via crossref
- doi:10.1080/00063659909477230 via crossref
- doi:10.1016/s0065-2504(09)00403-6 via crossref
- doi:10.1002/ps.3965 via crossref
- doi:10.1016/j.actao.2003.11.008 via crossref
- doi:10.1016/j.fishres.2012.07.025 via crossref
- doi:10.1016/j.fishres.2012.10.012 via crossref
- doi:10.2307/2269409 via crossref
- doi:10.1002/ece3.882 via crossref
- doi:10.1038/nature01017 via crossref
- doi:10.1093/acprof:oso/9780199554232.003.0007 via crossref
- doi:10.1890/09-1525.1 via crossref
- doi:10.1098/rsos.160498 via crossref
- doi:10.1111/1365-2435.13148 via crossref
- doi:10.1111/1365-2664.13080 via crossref
- doi:10.1007/s10336-010-0632-7 via crossref
- doi:10.1038/s41598-019-40962-3 via crossref
- doi:10.1890/09-1931.1 via crossref
- doi:10.1111/j.1365-2656.2009.01579.x via crossref
- doi:10.1002/ece3.5485 via crossref
- doi:10.2193/2007-378 via crossref
- doi:10.2981/wlb.2003.056 via crossref
- doi:10.1890/0012-9615(2002)072[0057:fpmipn]2.0.co;2 via crossref
- doi:10.1002/jwmg.21404 via crossref
- doi:10.1016/j.biocon.2017.10.017 via crossref
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License: CC-BY-NC-ND-4.0