Pérez‐Jorge Sergi

No Thumbnail Available
Last Name
First Name

Search Results

Now showing 1 - 2 of 2
  • Article
    Multi-state open robust design applied to opportunistic data reveals dynamics of wide-ranging taxa: The sperm whale case.
    (Ecological Society of America, 2019-03-04) Boys, Rebecca M. ; Oliveira, Claudia ; Pérez‐Jorge, Sergi ; Prieto, Rui ; Steiner, Lisa ; Silva, Monica A.
    Capture–mark–recapture methods have been extensively used to estimate abundance, demography, and life history parameters of populations of several taxa. However, the high mobility of many species means that dedicated surveys are logistically complicated and expensive. Use of opportunistic data may be an alternative, if modeling takes into account the inevitable heterogeneity in capture probability from imperfect detection and incomplete sampling, which can produce significant bias in parameter estimates. Here, we compare covariate‐based open Jolly‐Seber models (POPAN) and multi‐state open robust design (MSORD) models to estimate demographic parameters of the sperm whale population summering in the Azores, from photo‐identification data collected opportunistically by whale‐watching operators and researchers. The structure of the MSORD also allows for extra information to be obtained, estimating temporary emigration and improving precision of estimated parameters. Estimates of survival from both POPAN and MSORD were high, constant, and very similar. The POPAN model, which partially accounted for heterogeneity in capture probabilities, estimated an unbiased super‐population of ~1470 whales, with annual abundance showing a positive trend from 351 individuals (95% CI: 234–526) in 2010 to 718 (95% CI: 477–1082) in 2015. In contrast, estimates of abundance from MSORD models that explicitly incorporated imperfect detection due to temporary emigration were less biased, more precise, and showed no trend over years, from 275 individuals (95% CI: 188–404) in 2014 to 367 (95% CI: 248–542) in 2012. The MSORD estimated short residence time and an even‐flow temporary emigration, meaning that the probability of whales emigrating from and immigrating to the area was equal. Our results illustrate how failure to account for transience and temporary emigration can lead to biased estimates and trends in abundance, compromising our ability to detect true population changes. MSORD models should improve inferences of population dynamics, especially when capture probability is low and highly variable, due to wide‐ranging behavior of individuals or to non‐standardized sampling. Therefore, these models should provide less biased estimates and more accurate assessments of uncertainty that can inform management and conservation measures.
  • Article
    Environmental drivers of large-scale movements of baleen whales in the mid-North Atlantic Ocean
    (Wiley Open Access, 2020-03-21) Pérez‐Jorge, Sergi ; Tobeña Morcillo, Marta ; Prieto, Rui ; Vandeperre, Frederic ; Calmettes, Beatriz ; Lehodey, Patrick ; Silva, Monica A.
    Aim Understanding the environmental drivers of movement and habitat use of highly migratory marine species is crucial to implement appropriate management and conservation measures. However, this requires quantitative information on their spatial and temporal presence, which is limited in the high seas. Here, we aimed to gain insights of the essential habitats of three baleen whale species around the mid‐North Atlantic (NA) region, linking their large‐scale movements with information on oceanographic and biological processes. Location Mid‐NA Ocean. Methods We present the first study combining data from 31 satellite tracks of baleen whales (15, 10 and 6 from fin, blue and sei whales, respectively) from March to July (2008–2016) with data on remotely sensed oceanography and mid‐ and lower trophic level biomass derived from the spatial ecosystem and population dynamics model (SEAPODYM). A Bayesian switching state‐space model was applied to obtain regular tracks and correct for location errors, and pseudo‐absences were created through simulated positions using a correlated random walk model. Based on the tracks and pseudo‐absences, we applied generalized additive mixed models (GAMMs) to determine the probability of occurrence and predict monthly distributions. Results This study provides the most detailed research on the spatio‐temporal distribution of baleen whales in the mid‐NA, showing how dynamic biophysical processes determine their habitat preference. Movement patterns were mainly influenced by the interaction of temperature and the lower trophic level biomass; however, this relationship differed substantially among species. Best‐fit models suggest that movements of whales migrating towards more productive areas in northern latitudes were constrained by depth and eddy kinetic energy. Main conclusions These novel insights highlight the importance of integrating telemetry data with spatially explicit prey models to understand which factors shape the movement patterns of highly migratory species across large geographical scales. In addition, our outcomes could contribute to inform management of anthropogenic threats to baleen whales in sparsely surveyed region.