Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- Forecasting shifts in habitat suitability across the distribution range of a temperate small pelagic fish under different scenarios of climate changePublication . Lima, André R.A.; Baltazar-Soares, Miguel; Garrido, Susana; Riveiro, I. (Isabel); Carrera, Pablo; Piecho-Santos, A. Miguel; Peck, Myron; Silva, GonçaloClimate change often leads to shifts in the distribution of small pelagic fish, likely by changing the matchmismatch dynamics between these sensitive species within their environmental optima. Using present-day habitat suitability, we projected how different scenarios of climate change (IPCC Representative Concentration Pathways 2.6, 4.5 and 8.5) may alter the large scale distribution of European sardine Sardina pilchardus (a model species) by 2050 and 2100. We evaluated the variability of species-specific environmental optima allowing a comparison between present-day and future scenarios. Regardless of the scenario, sea surface temperature and salinity and the interaction between current velocity and distance to the nearest coast were the main descriptors responsible for the main effects on sardine's distribution. Present-day and future potential “hotspots” for sardine were neritic zones (<250 km) withwater currents <0.4ms−1, where SST was between 10 and 22 °C and SSS>20 (PSU), on average.Most variability in projected shifts among climatic scenarioswas in habitats with moderate to low suitability. By the end of this century, habitat suitability was projected to increase in the Canary Islands, Iberian Peninsula, central North Sea, northern Mediterranean, and eastern Black Sea and to decrease in the Atlantic African coast, southwest Mediterranean, English Channel, northern North Sea and Western U.K. A gradual poleward-eastward shift in sardine distribution was also projected among scenarios. This shift was most pronounced in 2100 under RCP 8.5. In that scenario, sardines had a 9.6% range expansion which included waters along the entire coast of Norway up and into the White Sea. As habitat suitability is mediated by the synergic effects of climate variability and change on species fitness, it is critical to apply models with robust underlying species-habitat data that integrate knowledge on the full range of processes shaping species productivity and distribution.
- Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modellingPublication . Baltazar-Soares, Miguel; Lima, André R.A.; Silva, Gonçalo; Gaget, ElieThe establishment of high-throughput sequencing technologies and subsequent large-scale genomic datasets has flourished across fields of fundamental biological sciences. The introduction of genomic resources in fisheries management has been proposed from multiple angles, ranging from an accurate re-definition of geographical limitations of stocks and connectivity, identification of fine-scale stock structure linked to locally adapted subpopulations, or even the integration with individual-based biophysical models to explore life history strategies. While those clearly enhance our perception of patterns at the light of a spatial scale, temporal depth and consequently forecasting ability might be compromised as an analytical trade-off. Here, we present a framework to reinforce our understanding of stock dynamics by adding also a temporal point of view. We propose to integrate genomic information on temporal projections of species distributions computed by Species Distribution Models (SDMs). SDMs have the potential to project the current and future distribution ranges of a given species from relevant environmental predictors. These projections serve as tools to inform about range expansions and contractions of fish stocks and suggest either suitable locations or local extirpations that may arise in the future. However, SDMs assume that the whole population respond homogenously to the range of environmental conditions. Here, we conceptualize a framework that leverages a conventional Bayesian joint-SDM approach with the incorporation of genomic data. We propose that introducing genomic information at the basis of a joint-SDM will explore the range of suitable habitats where stocks could thrive in the future as a function of their current evolutionary potential.