Grossi Francesca, Hazen Elliott L, Leo Giulio De, David Léa, Di-Méglio Nathalie, Arcangeli Antonella, Pasanisi Eugenia, Campana Ilaria, Paraboschi Miriam, Castelli Alberto, Rosso Massimiliano, Moulins Aurelie, Tepsich Paola
CIMA Research Foundation Savona Italy.
DIBRIS University of Genoa Genova Italy.
Ecol Evol. 2025 Mar 7;15(3):e71007. doi: 10.1002/ece3.71007. eCollection 2025 Mar.
Understanding the habitat of highly migratory species is aided by using species distribution models to identify species-habitat relationships and to inform conservation and management plans. While Generalized Additive Models (GAMs) are commonly used in ecology, and particularly the habitat modeling of marine mammals, there remains a debate between modeling habitat (presence/absence) versus density (# individuals). Our study assesses the performance and predictive capabilities of GAMs compared to boosted regression trees (BRTs) for modeling both fin whale density and habitat suitability alongside Hurdle Models treating presence/absence and density as a two-stage process to address the challenge of zero-inflated data. Fin whale data were collected from 2008 to 2022 along fixed transects crossing the NW Mediterranean Sea during the summer period. Data were analyzed using traditional line transect methodology, obtaining the Effective Area monitored. Based on existing literature, we select various covariates, either static in nature, such as bathymetry and slope, or variable in time, for example, SST, MLD, Chl concentration, EKE, and FSLE. We compared both the explanatory power and predictive skill of the different modeling techniques. Our results show that all models performed well in distinguishing presences and absences but, while density and presence patterns for the fin whale were similar, their dependencies on environmental factors can vary depending on the chosen model. Bathymetry was the most important variable in all models, followed by SST and the chlorophyll recorded 2 months before the sighting. This study underscores the role SDMs can play in marine mammal conservation efforts and emphasizes the importance of selecting appropriate modeling techniques. It also quantifies the relationship between environmental variables and fin whale distribution in an understudied area, providing a solid foundation for informed decision making and spatial management.
通过使用物种分布模型来识别物种与栖息地的关系，并为保护和管理计划提供信息，有助于了解高度洄游物种的栖息地。虽然广义相加模型（GAMs）在生态学中普遍使用，特别是在海洋哺乳动物的栖息地建模中，但在对栖息地（存在/不存在）与密度（个体数量）进行建模之间仍存在争议。我们的研究评估了GAMs与增强回归树（BRTs）相比在对长须鲸密度和栖息地适宜性进行建模时的性能和预测能力，同时将障碍模型（Hurdle Models）作为处理存在/不存在和密度的两阶段过程，以应对零膨胀数据的挑战。长须鲸数据于2008年至2022年夏季期间沿着穿越地中海西北部的固定样带收集。使用传统的线样带方法对数据进行分析，得出监测的有效面积。基于现有文献，我们选择了各种协变量，包括本质上静态的，如测深和坡度，或随时间变化的，例如海表温度（SST）、混合层深度（MLD）、叶绿素浓度、海流动能（EKE）和有限扩散率（FSLE）。我们比较了不同建模技术的解释力和预测技能。我们的结果表明，所有模型在区分存在和不存在方面都表现良好，但是，虽然长须鲸的密度和存在模式相似，但它们对环境因素的依赖性可能因所选模型而异。测深在所有模型中是最重要的变量，其次是海表温度和在目击前2个月记录的叶绿素。这项研究强调了物种分布模型在海洋哺乳动物保护工作中可以发挥的作用，并强调了选择合适建模技术的重要性。它还量化了一个研究较少地区环境变量与长须鲸分布之间的关系，为明智的决策和空间管理提供了坚实的基础。
