Predicted regime shift in the seagrass ecosystem of the Gulf of Arguin driven by climate change

Chefaoui, Rosa M.; Duarte, Carlos M.; Tavares, Ana I.; Frade, Duarte G.; Cheikh, Mohamed Ahmed Sidi; Ba, M. Abdoull; Serrão, Ester Á.

doi:10.1016/j.gecco.2021.e01890

Cited by 13 publications

(11 citation statements)

References 13 publications

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“…Particular MPAs of Cape Verde, Guinea, Senegal, and Sierra Leone with higher betweenness values (Figure 3) may play a key role toward connectivity in the face of future environmental changes, by acting as stepping-stones for the dispersal of propagules between different islands or regions/countries. Importantly, the few data describing population structure along the West African coastal waters and its network of MPAs already reports losses at the ecosystem level (seagrass, rocky bottom communities and coral reefs; Failler et al, 2020a;Chefaoui et al, 2021). However, betweenness centrality is strongly dependent on network configuration, and one MPA can be particularly important in promoting stepping-stone connectivity for a given ecological group, but not for others.…”

Section: Mpa Aggregationsmentioning

confidence: 99%

Potential Biodiversity Connectivity in the Network of Marine Protected Areas in Western Africa

et al. 2021

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Marine Protected Areas (MPAs) must function as networks with sufficient stepping-stone continuity between suitable habitats to ensure the conservation of naturally connected regional pools of biodiversity in the long-term. For most marine biodiversity, population connectivity is mediated by passively dispersed planktonic stages with contrasting dispersal periods, ranging from a few hours to hundreds of days. These processes exert a major influence on whether threatened populations should be conserved as either isolated units or linked metapopulations. However, the distance scales at which individual MPAs are connected are insufficiently understood. Here, we use a biophysical model integrating high-resolution ocean currents and contrasting dispersal periods to predict connectivity across the Network of MPAs in Western Africa. Our results revealed that connectivity differs sharply among distinct ecological groups, from highly connected (e.g., fish and crustacea) to predominantly isolated ecosystem structuring species (e.g., corals, macroalgae and seagrass) that might potentially undermine conservation efforts because they are the feeding or nursery habitats required by many other species. Regardless of their dispersal duration, all ecological groups showed a common connectivity gap in the Bijagós region of Guinea-Bissau, highlighting the important role of MPAs there and the need to further support and increase MPA coverage to ensure connectivity along the whole network. Our findings provide key insights for the future management of the Network of MPAs in Western Africa, highlighting the need to protect and ensure continuity of isolated ecosystem structuring species and identifying key regions that function as stepping-stone connectivity corridors.

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Section: Mpa Aggregationsmentioning

confidence: 99%

Potential Biodiversity Connectivity in the Network of Marine Protected Areas in Western Africa

et al. 2021

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“…Our ensemble SDM for eelgrass fit within a subset of its biogeographic range demonstrated comparable predictive performance to previous single-technique SDMs developed in other regions within the biogeographic range of Zostera marina at a similar nominal resolution (Bekkby et al, 2008;Downie et al, 2013;Schubert et al, 2015;Bobsien et al, 2021). However, ensemble and single-model SDMs fit over the entire species biogeographic range of marine macrophytes or across a broader latitudinal gradient have typically shown higher predictive performance (AUC > 0.9, TSS > 0.7) for seagrasses (Valle et al, 2014;Chefaoui et al, 2016;Chefaoui et al, 2017;Chefaoui et al, 2018;Wilson and Lotze, 2019;Chefaoui et al, 2021;Hu et al, 2021), kelps (Assis et al, 2018;Wilson et al, 2019b;Goldsmit et al, 2021), and mangroves (Record et al, 2013). These range-wide models mainly employ climatic predictors at a coarser resolution (e.g., ~5 arc min), which may explain their improved fit given that marine species occupy geographic ranges close to their thermal limits (Sunday et al, 2012), potentially yielding stronger correlations with species occurrence across their temperature range at a coarse scale.…”

Section: Discussionmentioning

confidence: 55%

“…The choice of model scale (within-range vs. range-wide) should be informed to a large degree by the modeling objectives and intended applications as they are best suited for different purposes based on a trade-off between spatial resolution and extent. Whereas, models covering the entire biogeographic range calibrated with coarser grain climatic predictors may reveal tolerance limits and permit projections of past (Chefaoui et al, 2017;Assis et al, 2018) or future species distributions (Wilson and Lotze, 2019;Chefaoui et al, 2021), models fit within a species' range can uncover more subtle niche preferences and local adaptation (Nephin et al, 2020), corresponding to more fine-scale and complex forcing that reflect habitat suitability closer to the scale of habitat patch sizes. These more nuanced relationships deteriorate at larger spatial scales (Record et al, 2013) and the relative influence of environmental predictors may change (Nyström Sandman et al, 2013), yet such fine-grain, regional distribution patterns are critical to local conservation planning and habitat management.…”

Section: Discussionmentioning

confidence: 99%

“…Despite these advantages and evidence for improved performance of ensemble-based approaches (Grenouillet et al, 2011;Guo et al, 2015), there has been considerably less uptake by marine SDM studies compared to terrestrial environments (Hao et al, 2019). The few examples of ensemble models for seagrasses (Chefaoui et al, 2016;Chefaoui et al, 2017;Chefaoui et al, 2018;Chefaoui et al, 2021) and kelps (Goldsmit et al, 2021) have demonstrated the potential applications of this approach for SAV and often highlight improved performance compared to individual models. These studies made use of widely available climatic predictors for ensemble construction and projections; consistent with the typical application of ensemble models (Hao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Fine-scale ensemble species distribution modeling of eelgrass (Zostera marina) to inform nearshore conservation planning and habitat management

O’Brien

Wong²,

Stanley³

2022

Front. Mar. Sci.

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Baseline data on the distribution and extent of biogenic habitat-forming species at a high spatial resolution are essential to inform habitat management strategies, preserve ecosystem integrity, and achieve effective conservation objectives in the nearshore. Model-based approaches to map suitable habitat for these species are a key tool to address this need, filling in gaps where observations are otherwise unavailable and remote sensing methods are limited by turbid waters or cannot be applied at scale. We developed a high resolution (35 m) ensemble species distribution model to predict the distribution of eelgrass (Zostera marina) along the Atlantic coast of Nova Scotia, Canada where the observational coverage of eelgrass occurrence is sparse and nearshore waters are optically complex. Our ensemble model was derived as a performance-weighted average prediction of 7 different modeling methods fit to 6 physical predictors (substrate type, depth, wave exposure, slope, and two bathymetric position indices) and evaluated with a 5-fold spatially-blocked cross-validation procedure. The ensemble model showed moderate predictive performance (Area Under the Receiver-Operating Characteristic Curve (AUC) = 0.803 ± 0.061, True Skill Statistic (TSS) = 0.531 ± 0.100; mean ± SD), high sensitivity (92.0 ± 4.5), and offered some improvement over individual models. Substrate type, depth, and relative wave exposure were the most influential predictors associated with eelgrass occurrence, where the highest probabilities were associated with sandy and sandy-mud sediments, depths ranging 0 m – 4 m, and low to intermediate wave exposure. Within our study region, we predicted a total extent of suitable eelgrass habitat of 38,130 ha. We found suitable habitat was particularly extensive within the long narrow inlets and extensive shallow flats of the South Shore, Eastern Shore, and Bras d’Or Lakes. We also identified substantial overlap of eelgrass habitat with previously identified Ecologically and Biologically Significant Areas that guide regional conservation planning while also highlighting areas of greater prediction uncertainty arising from disagreement among modeling methods. By offering improved sensitivity and insights into the fine-scale regional distribution of a habitat-forming species with associated uncertainties, our ensemble-based modeling approach provides improved support to numerous nearshore applications including conservation planning and restoration, marine spatial and emergency response planning, environmental impact assessments, and fish habitat protection.

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“…Still, it is compelling to learn that green turtles in the Bijagoś can thrive mostly on macroalgae. If the rich seagrass meadows present in other West African foraging areas collapse (Cardona et al, 2009;Chefaoui et al, 2021), and are substituted by adequate macroalgae, local green turtle populations may be able to adopt a similar diet and persist. Alternatively, if they are too specialized to adapt, they may migrate to other more suitable foraging locations as in other populations (Kale et al, 2022).…”

Section: Isotopic Niche Segregationmentioning

confidence: 99%

Fine-scale foraging segregation in a green turtle (Chelonia mydas) feeding ground in the Bijagós archipelago, Guinea Bissau

et al. 2022

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Green turtles (Chelonia mydas) are highly dependent on neritic foraging areas throughout much of their life. Still, knowledge of recruitment dynamics, foraging habits, and habitat use in these areas is limited. Here, we evaluated how the distribution and food preferences of green sea turtles from different life stages varied within a foraging aggregation. We focused on two islands in Guinea-Bissau, Unhocomo and Unhocomozinho, using water captures and survey dives to record habitat use and characteristics, and stable isotopes to infer diet. Additionally, we used stable isotopes to infer their diet. Two habitat types were sampled: deeper (2.26 ± 0.4 m) rocky sites fringed by mangrove with macroalgae, and sandy shallows (1.37 ± 0.12 m) surrounded by rocky reefs with macroalgae and seagrass. The two benthic communities were similar isotopically and in terms of species composition, except for the presence or absence of seagrass, which had unique signatures. We captured 89 turtles ranging from 35 cm to 97 cm in curved carapace length (i.e., juvenile to adult stages). Size distribution was habitat-dependent, with most smaller turtles present in sandy shallows and larger turtles favoring slightly deeper rocky sites. Turtle isotopic signatures differed between the habitat of capture, regardless of size, revealing a marked dichotomy in foraging preference. All turtles fed primarily on macroalgae, mostly rhodophytes. However, individuals captured in sandy habitats had evident seagrass skewed isotopic signatures. Larger turtles may be unable to use the more diverse shallower foraging sites due to increased vulnerability to predation. Despite the proximity of the sampled foraging sites (2.7 km apart), the two foraging subgroups seem to maintain consistently different feeding habits. Our study highlights how heterogeneous green turtle foraging habits can be within populations, even at small geographic scales.

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Predicted regime shift in the seagrass ecosystem of the Gulf of Arguin driven by climate change

Cited by 13 publications

References 13 publications

Potential Biodiversity Connectivity in the Network of Marine Protected Areas in Western Africa

Potential Biodiversity Connectivity in the Network of Marine Protected Areas in Western Africa

Fine-scale ensemble species distribution modeling of eelgrass (Zostera marina) to inform nearshore conservation planning and habitat management

Fine-scale foraging segregation in a green turtle (Chelonia mydas) feeding ground in the Bijagós archipelago, Guinea Bissau

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