Falace Annalisa, Kaleb Sara, Curiel Daniele, Miotti Chiara, Galli Giovanni, Querin Stefano, Ballesteros Enric, Solidoro Cosimo, Bandelj Vinko
Department of Life Sciences, University of Trieste, Trieste, Italia.
SELC Society Marghera, Venezia, Italia.
PLoS One. 2015 Nov 11;10(11):e0140931. doi: 10.1371/journal.pone.0140931. eCollection 2015.
Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM) clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders) characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.). Habitat C (non-articulated coralline, Polycitor adriaticus) is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat model outputs provide insight into the environmental drivers that control the distribution of the habitat and can be used to guide future research efforts and cost-effective management and conservation plans.
栖息地分类为跨地理区域绘制和比较海洋资源提供了指导方针。钙质生物结核及其相关生物群落尚未得到详尽分类。此外，出于管理和保护目的，物种和栖息地绘图至关重要。最近，预测性栖息地建模领域有了一些进展，并且有多种方法可用。在本研究中，我们定义了构成亚得里亚海北部生物礁的栖息地，并创建了一个预测性栖息地分布模型。我们使用了一个更新的浅海底栖生物组合数据集来定义栖息地，并使用模糊k均值（FKM）聚类方法进行了验证。采用冗余分析来模拟环境描述符与FKM隶属度之间的关系。进行了预测建模以绘制整个流域的栖息地。栖息地A（机会性大型藻类、覆盖型多孔动物、生物侵蚀者）表征最靠近海岸线的礁石，这些礁石受沿岸流和河流输入的影响。栖息地B的特征是块状多孔动物、直立被囊动物和非钙质覆盖藻类（佩松藻属）。栖息地C（非节状珊瑚藻、亚得里亚海多细胞虫）预计出现在更深的区域。由于沿海淡水的影响，近岸 - 离岸梯度解释了生物组合的变异性，沿海淡水是营养动态的主要驱动因素。该模型支持将栖息地A和C解释为表征浅海底栖生物组合的梯度的两端，而栖息地B表现出中间特征。过渡区域是海洋环境的自然特征，可能包括栖息地和物种的混合。所提出的栖息地在实地易于识别，与不同的环境特征相关，并且可能适用于专注于其他地理区域的研究。栖息地模型输出提供了对控制栖息地分布的环境驱动因素的洞察，可用于指导未来的研究工作以及具有成本效益的管理和保护计划。
