Centro de Investigação em Biodiversidade e Recursos Genéticos (InBIO/CIBIO-ICETA), Universidade do Porto, Vairão, Portugal.
Departamento de Botánica, Facultad de Ciencias, Universidad de Granada, Granada, Spain.
PLoS One. 2018 Jun 18;13(6):e0199292. doi: 10.1371/journal.pone.0199292. eCollection 2018.
Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.
全球环境变化正在迅速影响世界各地物种的分布和栖息地适宜性，需要不断更新生物多样性状况，以支持有效决策，制定保护政策和管理措施。在这方面，卫星衍生的生态系统功能属性 (EFA) 比气候和结构或组成景观属性更能综合和快速地评估生态系统对环境驱动因素和变化的响应。因此，EFA 作为物种分布模型 (SDM) 的预测因子和实施多尺度物种监测计划可能具有优势。在这里，我们描述了一个建模框架，用于评估 EFA 作为基本生物多样性变量 (EBA) 的预测能力，与传统数据集（气候、土地覆盖）在多个尺度上的预测能力。我们使用两个受保护的欧盟栖息地指令保护的具有不同生活史、范围和分布模式的保护关注植物物种（Iris boissieri 和 Taxus baccata）的多尺度生境适宜性评估来测试该框架。我们为两个测试物种拟合了四组 SDM，并用以下方法进行校准：插值气候变量；景观变量；EFA；以及气候和景观变量的组合。基于 EFA 的模型在多个尺度上表现非常出色（AUC 中位数从 0.881±0.072 到 0.983±0.125），与传统的基于气候的模型一样，无论是单独使用还是与土地覆盖预测因子结合使用（AUC 中位数从 0.882±0.059 到 0.995±0.083）。此外，基于 EFA 的模型确定了更多适宜的区域，并为两个测试物种（分布范围较窄和较宽）的粗尺度和细尺度的生境适宜性的功能特征提供了有价值的信息。我们的结果表明，卫星衍生的 EFA 的预测能力相对较小尺度依赖，支持将其用作从区域和更广泛的尺度到更局部和更精细的尺度的 SDM 中的有意义的 EBV。由于评估物种的保护状况和生境质量应尽可能基于与有意义的过程相关的可扩展指标，因此我们的框架可以指导保护管理人员在与生物多样性监测和报告计划相关的决策中做出决策。
