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Rouyer, T., Sadykov, A., Ohlberger, J., & Stenseth, N. C. (2012). Does increasing mortality change the response of fish populations to environmental fluctuations? Ecology Letters, 15(7), 658–665.
Résumé: Ecology Letters (2012) 15: 658–665 Abstract
Fluctuations of fish populations abundances are shaped by the interplay between population dynamics and the stochastic forcing of the environment. Age-structured populations behave as a filter of the environment. This filter is characterised by the species-specific life cycle and life-history traits. An increased mortality of mature individuals alters these characteristics and may therefore induce changes in the variability of populations. The response of a generic age-structured model was analysed to investigate the expected changes in the fluctuations of fish populations in response to decreased adult survival. These expectations were then tested on an extensive dataset. In accordance with theory, the analyses revealed that decreased adult survival and mean age of spawners were linked to an increase in the relative importance of short-term fluctuations. It suggests that intensive exploitation can lead to a change in the variability of fish populations, an issue of central interest from both conservation and management perspectives. |
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Santos, B. S., Friedrichs, M. A. M., Rose, S. A., Barco, S. G., & Kaplan, D. M. (2018). Likely locations of sea turtle stranding mortality using experimentally-calibrated, time and space-specific drift models. Biol. Conserv., 226, 127–143.
Résumé: Sea turtle stranding events provide an opportunity to study drivers of mortality, but causes of strandings are poorly understood. A general sea turtle carcass oceanographic drift model was developed to estimate likely mortality locations from coastal sea turtle stranding records. Key model advancements include realistic direct wind forcing on carcasses, temperature driven carcass decomposition and the development of mortality location predictions for individual strandings. We applied this model to 2009-2014 stranding events within the Chesapeake Bay, Virginia. Predicted origin of vessel strike strandings were compared to commercial vessel data, and potential hazardous turtle-vessel interactions were identified in the southeastern Bay and James River. Commercial fishing activity of gear types with known sea turtle interactions were compared to predicted mortality locations for stranded turtles with suggested fisheries-induced mortality. Probable mortality locations for these strandings varied seasonally, with two distinct areas in the southwest and southeast portions of the lower Bay. Spatial overlap was noted between potential mortality locations and gillnet, seine, pot, and pound net fisheries, providing important information for focusing future research on mitigating conflict between sea turtles and human activities. Our ability to quantitatively assess spatial and temporal overlap between sea turtle mortality and human uses of the habitat were hindered by the low resolution of human use datasets, especially those for recreational vessel and commercial fishing gear distributions. This study highlights the importance of addressing these data gaps and provides a meaningful conservation tool that can be applied to stranding data of sea turtles and other marine megafauna worldwide.
Mots-Clés: bycatch; chesapeake bay; Chesapeake Bay; Drift simulations; Endangered species; fisheries; Fisheries and vessel interactions; global patterns; hotspots; ichthyoplankton; manatees; Marine conservation; megafauna; Protected species management; Sea turtle mortality; Sea turtle strandings; vessel; virginia
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Schilds, A., Mourier, J., Huveneers, C., Nazimi, L., Fox, A., & Leu, S. T. (2019). Evidence for non-random co-occurrences in a white shark aggregation. Behav. Ecol. Sociobiol., 73(10), Unsp-138.
Résumé: Groups or aggregations of animals can result from individuals being attracted to a common resource or because of synchronised patterns of daily or seasonal activity. Although mostly solitary throughout its distribution, white sharks (Carcharodon carcharias) seasonally aggregate at a number of sites worldwide to feed on calorie-rich pinnipeds. At the Neptune Islands, South Australia, large numbers of white sharks can be sighted throughout the year, including during periods of low seal abundance. We use a combination of photo-identification and network analysis based on co-occurrence of individuals visiting the site on the same day to elucidate the population structure and aggregatory behaviour of Australia's largest aggregation of sub-adult and adult white sharks. We photo-identified 282 sharks (183 males, 97 females, 2 unknown) over a 4.5-year period (June 2010-November 2014) and found that white sharks did not randomly co-occur with their conspecifics, but formed four distinct communities. Tendency to co-occur varied across months with males co-occurring with more individuals than females. Sex-dependent patterns of visitation at the Neptune Islands and resulting intraspecific competition likely drive the observed community structure and temporal variability in co-occurrences. This study provides new insights into the aggregatory behaviour of white sharks at a seal colony and shows for the first time that white shark co-occurrence can be non-random.
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Sirot, C., Gronkjaer, P., Pedersen, J. B., Panfili, J., Zetina-Rejon, M., Tripp-Valdez, A., et al. (2017). Using otolith organic matter to detect diet shifts in Bardiella chrysoura, during a period of environmental changes. Mar. Ecol.-Prog. Ser., 575, 137–152.
Résumé: Accurate knowledge on fish trophic ecology and its modifications is crucial for understanding the impact of global change on ecosystems. In this context, we investigated the value of the delta C-13 and delta N-15 of otolith soluble organic matter (SOM) for identifying temporal diet shifts in American silver perch Bairdiella chrysoura over a 30-yr period characterized by strong changes in its population size and habitats within the Terminos Lagoon (Mexico). We first compared the otolith SOM isotopic signatures from present-clay adults to those of muscle and the main local prey. Our results suggest that otolith SOM can be confidently extracted and analyzed for both present and past otoliths of this species. The mean otolith SOM signatures obtained (-15.92 +/- 1.35%, for delta C-13 and 9.38 +/- 0.93%, for delta N-15) were consistent with those of the diet as 85% of the individual signatures were included within the prey isotopic niche area. Moreover, this study supports a trophic enrichment factor between diet and otolith (TEFdiet-otolith) close to 0 for delta N-15, while for delta C-13, the TEFololith-muscle of +0.02% warrants further investigation. Then, we compared past and contemporary otolith SOM signatures to investigate temporal diet shifts in B. chrysoura. This showed that 613C and delta N-15 differed significantly between the past and present period even if the temporal shift remained relatively small (respectively +1.17%, and 0.55%). The present study substantiates the use of otolith SOM delta C-13 and delta N-15 as a proxy of fish present and past trophic position, opening the possibility for major progress in studies of temporal changes in food web ecology.
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Thiebault, A., Dubroca, L., Mullers, R. H. E., Tremblay, Y., & Pistorius, P. A. (2018). “M2B” package in R: Deriving multiple variables from movement data to predict behavioural states with random forests. Methods Ecol. Evol., 9(6), 1548–1555.
Résumé: 1. The behaviour of individuals affect their distributions and is therefore fundamental in determining ecological patterns. While, the direct observation of behaviour is often limited due to logistical constraints, collection of movement data has been greatly facilitated through the development of bio-logging. Movement data obtained through tracking instrumentation may potentially constitute a relevant proxy to infer behaviour. 2. To infer behaviour from movement data is a key focus within the “movement ecology” discipline. Statistical learning constitutes a number of methods that can be used to assess the link between given variables from a fully informed training dataset and then predict the values on a non-informed variable. We chose the random forest algorithm for its high prediction accuracy and its ease of implementation. The strength of random forest partly lies in its ability to handle a very large number of variables. Our methodology is accordingly based on the derivation of multiple predictor variables from movement data over various temporal scales, to capture as much information as possible from changes and variations in movement. 3. The methodology is described in four steps, using examples on foraging seabirds and fishing vessels for illustration. The models showed very high prediction accuracy (92%-97%), thereby confirming the influence of behaviour on movement decisions and demonstrating the ability to derive multiple variables from movement data to predict behaviour with random forests. 4. The codes developed for this methodology are published in the “M2B” (Movement to Behaviour) R package, available at https://CRAN.R-project.org/package=m2b. They can be used and adapted to datasets where movement was sampled from a wide range of taxa, sampling schemes or tracking devices. Observations are needed for a subset of the data, but once the model is trained, it can be used on any dataset with similar movement data.
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