Are shifts in species distribution triggered by climate change? A swordfish case study

Erauskin-Extramiana, Maite; Arrizabalaga, Haritz; Cabré, Anna; Coelho, Rui; Rosa, Daniela; Ibaibarriaga, Leire; Chust, Guillem

doi:10.1016/j.dsr2.2019.104666

Cited by 19 publications

(14 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The distribution of large pelagic fishes may also be associated with various environmental conditions, despite their highly migratory activity. Several populations of swordfish have shifted latitudinally, whereas the Mediterranean population has shifted longitudinally towards the west, as a result of climate change [74]. Local conditions, such as clusters of higher density occurring near converging fronts and strong thermoclines may also affect swordfish distribution at a more local scale [75].…”

Section: Discussionmentioning

confidence: 99%

Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

et al. 2020

View full text Add to dashboard Cite

In this work a fish species distribution model (SDM) was developed, by merging species occurrence data with environmental layers, with the scope to produce high resolution habitability maps for the whole Mediterranean Sea. The final model is capable to predict the probability of occurrence of each fish species at any location in the Mediterranean Sea. Eight pelagic, commercial fish species were selected for this study namely Engraulis encrasicolus, Sardina pilchardus, Sardinella aurita, Scomber colias, Scomber scombrus, Spicara smaris, Thunnus thynnus and Xiphias gladius. The SDM environmental predictors were obtained from the databases of Copernicus Marine Environmental Service (CMEMS) and the European Marine Observation and Data Network (EMODnet). The probabilities of fish occurrence data in low resolution and with several gaps were obtained from Aquamaps (FAO Fishbase). Data pre-processing involved feature engineering to construct 6830 features, representing the distribution of several mean-monthly environmental variables, covering a time-span of 10 years. Feature selection with the ensemble Reciprocal Ranking method was used to rank the features according to their relative importance. This technique increased model’s performance by 34%. Ten machine learning algorithms were then applied and tested based on their overall performance per species. The XGBoost algorithm performed better and was used as the final model. Feature categories were explored, with neighbor-based, extreme values, monthly and surface ones contributing most to the model. Environmental variables like salinity, temperature, distance to coast, dissolved oxygen and nitrate were found the strongest ones in predicting the probability of occurrence for the above eight species.

show abstract

Section: Discussionmentioning

confidence: 99%

Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Combining data from multiple sampling programs can help overcome this problem (Waggitt et al, 2020;Maureaud et al, 2021), but also increases the intrinsic variability related to observers' skills, sampling design and protocols, which may result in confounding species range shifts with variations in the distribution and intensity of the sampling effort (Thorson et al, 2016). For that reason, separating the observation process from the true underlying spatial distribution is essential to accurately identify range shifts over time (Chust et al, 2014b) and to identify potential drivers (Erauskin-Extramiana et al, 2019b). Recently, a species distribution function (SDF) able to distinguish between sampling variation and true geographic variability has been developed (Thorson et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

The Role of Climate, Oceanography, and Prey in Driving Decadal Spatio-Temporal Patterns of a Highly Mobile Top Predator

et al. 2021

Self Cite

View full text Add to dashboard Cite

Marine mammals have been proposed as ecosystem sentinels due to their conspicuous nature, wide ranging distribution, and capacity to respond to changes in ecosystem structure and functioning. In southern European Atlantic waters, their response to climate variability has been little explored, partly because of the inherent difficulty of investigating higher trophic levels and long lifespan animals. Here, we analyzed spatio-temporal patterns from 1994 to 2018 of one of the most abundant cetaceans in the area, the common dolphin (Delphinus delphis), in order to (1) explore changes in its abundance and distribution, and (2) identify the underlying drivers. For that, we estimated the density of the species and the center of gravity of its distribution in the Bay of Biscay (BoB) and tested the effect of three sets of potential drivers (climate indices, oceanographic conditions, and prey biomasses) with a Vector Autoregressive Spatio Temporal (VAST) model that accounts for changes in sampling effort resulting from the combination of multiple datasets. Our results showed that the common dolphin significantly increased in abundance in the BoB during the study period. These changes were best explained by climate indices such as the North Atlantic Oscillation (NAO) and by prey species biomass. Oceanographic variables such as chlorophyll a concentration and temperature were less useful or not related. In addition, we found high variability in the geographic center of gravity of the species within the study region, with shifts between the inner (southeast) and the outer (northwest) part of the BoB, although the majority of this variability could not be attributed to the drivers considered in the study. Overall, these findings indicate that considering temperature alone for projecting spatio-temporal patterns of highly mobile predators is insufficient in this region and suggest important influences from prey and climate indices that integrate multiple ecological influences. Further integration of existing observational datasets to understand the causes of past shifts will be important for making accurate projections into the future.

show abstract

“…We chose a Gaussian distribution with canonical (identity) link function, cubic smoothing spline and low-rank tensor product smooth to add modelling functionality (Wood 2006). GAMs model the monthly and interannual response of the species, which in our study is CPUE (Villarino et al 2015;Erauskin-Extramiana et al 2020), the number of positions at site level (0.001°) in each 0.5° (55.5 km) fishing grid square corresponding to the total catch (kg) by number of days fished (Silva et al 2015) within the central Queensland coastal domain. Spatio-temporal factors were considered by fitting a three-dimensional smoother to the product of three variables, namely, latitude, longitude and month.…”

Section: Gam Analysismentioning

confidence: 99%

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

2023

View full text Add to dashboard Cite

Context Coastal and estuarine finfish species are responding to human-induced climate change by altering their distributions. In tropical regions, the species mostly affected by warming have limited acclimation capacity or live close to their upper thermal limits. Consequently, coastal fish assemblages may dramatically contract in range, experience declining population abundance or local extinction. Aim Here we use two different predictive modelling techniques that cope with non-linear empirical relationships between responses and environmental predictors to investigate distribution change. Methods The habitat-suitability models we use are the maximum entropy model (MaxEnt) and the generalised additive model (GAM). We built the models for the period 2004–2019 with environmental data relevant to coastal systems. We incorporated climate change at current conditions, near future (2015–2054) and distant future (2055–2100) from CMIP6 climate models. Key results We identified bathymetry and sea-surface temperature to be key variables explaining the current and future distribution of coastal finfish and elasmobranchs of the Great Barrier Reef coast in central Queensland. Conclusions We showed how the distributions of valuable fisheries species will change under future warming conditions. Implications The objective is to inform fisheries management supporting the restructure of existing fisheries or the development of new resources for the dual purposes of conservation and food security.

show abstract

Are shifts in species distribution triggered by climate change? A swordfish case study

Cited by 19 publications

References 59 publications

Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea

The Role of Climate, Oceanography, and Prey in Driving Decadal Spatio-Temporal Patterns of a Highly Mobile Top Predator

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

Contact Info

Product

Resources

About