Use of topographic predictors for macrobenthic community mapping in the Marine Reserve of La Palma (Canary Islands, Spain)

Martín-García, Laura; González-Lorenzo, Gustavo; Brito-Izquierdo, Isabel T.; Barquín-Diez, Jacinto

doi:10.1016/j.ecolmodel.2013.04.005

Cited by 20 publications

(14 citation statements)

References 52 publications

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“…For example, substrate type (i.e., basalt, calcareous, granite) can influence benthic community composition, biological interactions and resource availability (Edmunds, Chidgey, & Willcox, ). Likewise, VRM is a physical measure of seabed complexity which can also influence benthic community compositions and biological interactions, as well as wave energy exposure, local oxygen saturation levels, and nutrient and resource availability (Martin‐Garcia, Gonzalez‐Lorenzo, Brito‐Izquierdo, & Barquin‐Diez, ; Sappington, Longshore, & Thompson, ; Young, Iampietro, Kvitek, & Garza, ). This is also true for subtidal reefs that vary with respect to exposure to wave power and ocean currents (Young, Ierodiaconou, & Womersley, ).…”

Section: Discussionmentioning

confidence: 99%

Local and regional scale habitat heterogeneity contribute to genetic adaptation in a commercially important marine mollusc (Haliotis rubra) from southeastern Australia

et al. 2019

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Characterising adaptive genetic divergence among conspecific populations is often achieved by studying genetic variation across defined environmental gradients. In marine systems this is challenging due to a paucity of information on habitat heterogeneity at local and regional scales and a dependency on sampling regimes that are typically limited to broad longitudinal and latitudinal environmental gradients. As a result, the spatial scales at which selection processes operate and the environmental factors that contribute to genetic adaptation in marine systems are likely to be unclear. In this study we explore patterns of adaptive genetic structuring in a commercially-harvested abalone species (Haliotis rubra) from southeastern Australia, using a panel of genome-wide SNP markers (5,239 SNPs), and a sampling regime informed by marine LiDAR bathymetric imagery and 20-year hindcasted oceanographic models. Despite a lack of overall genetic structure across the sampling distribution, significant genotype associations with heterogeneous habitat features were observed at local and regional spatial scales, including associations with wave energy, ocean current, sea surface temperature, and geology. These findings provide insights into the potential resilience of the species to changing marine climates and the role of migration and selection on recruitment processes, with implications for conservation and fisheries management. This study points to the spatial scales at which selection processes operate in marine systems and highlights the benefits of geospatially-informed sampling regimes for overcoming limitations associated with marine population genomic research.

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Section: Discussionmentioning

confidence: 99%

Local and regional scale habitat heterogeneity contribute to genetic adaptation in a commercially important marine mollusc (Haliotis rubra) from southeastern Australia

et al. 2019

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“…The spatial structure of the upwelling SST signature was clearly detectable from the MODIS SST (Figure ). The topographic position index (TPI, Weiss, ) is often used to indicate slope positions of the elevation surface (Martin‐Garcia et al, ; Tagil & Jenness, ). Similar to Huang and Feng (), the TPI showed the SST difference between a center cell and its neighbors (i.e., local SST anomaly) in this study, as derived from the following equation:

italicTPI ((),, x, y) = italicSST ((),, x, y) - W ((),, x, y)

where

W ((),, x, y) = \frac{\sum_{j = - n}^{n} \sum_{i = - n}^{n} italicSST ((),, x - i, y - j)}{N^{2}}, n = \frac{N - 1}{2}

, x and y are the positions of the center cell, W ( x , y ) is the window averaged SST, and N is the window size.…”

Section: Methodsmentioning

confidence: 99%

The Contribution of Local Wind and Ocean Circulation to the Interannual Variability in Coastal Upwelling Intensity in the Northern South China Sea

Shu

Wang

Feng³

et al. 2018

JGR Oceans

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Using in situ data, satellite observations, and model outputs, we analyzed the interannual variability in coastal upwelling intensity in the northern South China Sea. Comparing coastal upwelling observed from three cruises during the summers of 2008 and 2016, we found that coastal upwelling was stronger during 2016 compared to 2008, although the local upwelling favorable wind was stronger in 2008. The stronger near-bottom cross-shelf current and shallower thermocline in the slope resulted in stronger upwelling intensity during the summer of 2016. The topographic position index (TPI), which is defined by the sea surface temperature difference between one center cell and its neighbors, was used to quantify the interannual variability in upwelling. Stronger (weaker) upwelling intensity occurred during the summers of ) when the local wind was more favorable (less favorable) to coastal upwelling. The correlation coefficient between the area-weighted TPI and alongshore wind speed was À0.60, thereby confirming that local wind is the primary dynamical factor controlling the interannual variability in upwelling intensity. The correlation coefficient between the area-weighted TPI and the eastward boundary current transport averaged between the 75-and 100-m isobaths on the shelf was À0.42, indicating that the interannual variability in large-scale circulation in the northern South China Sea also contributes to the interannual variability in upwelling intensity. The anomalously shallow thermocline in the summer of 2016 was likely associated with the strong 2015-2016 El Niño event through planetary wave propagations.Plain Language Summary Coastal upwelling, transporting deep, cold, saline, and nutrient-rich water to the surface result in high levels of primary production and fishery production. The coast of the northern South China Sea (NSCS) features seasonal coastal upwelling during the boreal summer. The summer southwesterly wind is widely regarded as the main driving mechanism for coastal upwelling in the NSCS. Previous studies have shown that the interannual variability in coastal upwelling intensity is largely controlled by the interannual variability in local winds in the NSCS, which is closely related to the El Niño-Southern Oscillation. Based on in situ data, we found that the upwelling was much stronger during 2016 than during 2008, though the local wind was more favorable to upwelling during 2008, which indicated that local wind is not the sole factor controlling the interannual variability in upwelling intensity in the NSCS. Further studies showed that, beside the locale wind, the interannual variability in shelf circulation could also contribute to the interannual variability in coastal upwelling intensity due to the topographically induced upwelling (induced by the interaction between northeastward large-scale current and alongshore variable topography) in the NSCS. In addition, thermocline depth variation on interannual time scale likely influences the coastal upwelling intensity in the NSCS.

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“…These models are increasingly popular in the marine ecosystems' literature and recently they have started to be used even when the absences were available, under the assumption that such absence data were unreliable (e.g., Monk et al, 2010;Martín-García et al, 2013;Ross and Howell, 2013). Monk (2013) suggested that this preference for the presence-only models is a result of the misconception that imperfect detection can be ignored when using presence-only models.…”

Section: Models Performancementioning

confidence: 98%

“…Comparing both approaches in marine ecosystems is relevant since the use of presence-only models in the marine environment has increased markedly during the last few years (e.g., Bryan & Metaxas, 2007;Galparsoro et al 2009;Monk et al, 2010;Hermosilla et al, 2011;Reiss et al, 2011;Pierrat et al, 2012;Ross & Howell, 2013;Martín-García et al, 2013;García-Alegre et al, in press) although the reasons behind choosing this type of method and the consequences of not including the absences are not always clear (Dorazio, 2012;Monk, 2013;Yackulic et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Comparing species distribution models: a case study of four deep sea urchin species

et al. 2014

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There is an increasing demand for biodiversity mapping to address new challenges in the management of marine ecosystems. Species distribution models are a key tool in supplying part of this information. However, the use of these models in the marine environment is still developing and the reasons for the underlying use of different methodological approaches are not always clear. In this work, we compared four different statistical techniques: the ecological niche factor analysis (ENFA), the MAXimun ENTropy algorithm (MAXENT), general additive Models (GAMs), and Random Forest. ENFA and MAXENT were applied using presence-only data whereas GAM and Random Forest used presenceabsence data. As a case study, we used four deep sea urchin species: Centrostephanus longispinus, Coelopleurus floridanus, Stylocidaris affinis, and Cidaris cidaris. The distribution of the studied sea urchins showed strong bathymetric segregation. Depth was the most important variable, followed by reflectivity and slope. The correlations between the predictive outputs of the models were similar between GAM, Random Forest and MAXENT, and lower for ENFA. Models using presence/absence data showed the highest scores in the four species, significantly outperforming ENFA in most of the cases, although differences with MAXENT were significant in only one species.

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Use of topographic predictors for macrobenthic community mapping in the Marine Reserve of La Palma (Canary Islands, Spain)

Cited by 20 publications

References 52 publications

Local and regional scale habitat heterogeneity contribute to genetic adaptation in a commercially important marine mollusc (Haliotis rubra) from southeastern Australia

Local and regional scale habitat heterogeneity contribute to genetic adaptation in a commercially important marine mollusc (Haliotis rubra) from southeastern Australia

The Contribution of Local Wind and Ocean Circulation to the Interannual Variability in Coastal Upwelling Intensity in the Northern South China Sea

Comparing species distribution models: a case study of four deep sea urchin species

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