Publication details.

Paper

Year:2019
Author(s):R. Rodríguez-Caro, T. Wiegand, E. White, A. Sanz-Aguilar, A. Giménez, E. Graciá, K. van Benthem, J. Anadón
Title:A low cost approach to estimate demographic rates using inverse modeling
Journal:BIOLOGICAL CONSERVATION
ISSN:0006-3207
JCR Impact Factor:4.711
Volume:237
Pages:358-365
D.O.I.:10.1016/j.biocon.2019.07.011
Web:https://dx.doi.org/10.1016/j.biocon.2019.07.011
Abstract:© 2019 Elsevier LtdSurvival is a key parameter in species' management and conservation. Most methods for estimating survival require time series data, large sample sizes and, overall, costly monitoring efforts. Inverse modeling approaches can be less data hungry, however they are underused in conservation sciences. Here we present an inverse modeling approach for estimating relative survival rates of long-lived species that is mathematically straightforward and evaluate its performance under constraints common in conservation studies related to small sample sizes. Specifically, we (i) estimated the relative survival rates in a Testudo graeca population based on one-year monitoring, (ii) assessed the impact of sample size on the accuracy, and (iii) tested alternative hypotheses on the impact of fire on the survival rates. We then compared the results of our approach with capture-recapture (CRC) estimates based on long-term monitoring. Our approach (153 individuals within a single year) yielded estimates of survival rates overlapping those of CRC estimates (11 years of data and 1009 individuals) for adults and subadults, but not for juveniles. Simulation experiments showed that our method provides robust estimates if sample size is above 100 individuals. The best models describing the impact of fire on survival identified by our approach predicts a decrease in survival especially in hatchlings and juvenile individuals, similar to CRC estimates. Our work proves that inverse modeling can decrease the cost for estimating demographic rates, especially for long-lived species and as such, its use should be encouraged in conservation and management sciences.

Related staff

  • Ana Sanz Aguilar
  • Related departments

  • Animal and Microbial Biodiversity
  • Related research groups

  • Ecology and Evolution