Presence-only data are typical occurrence information used in species distribution modelling. Data may be originated from different sources, and their integration is a challenging exercise in spatial ecology as detection biases are rarely fully considered. We propose a new protocol for presence-only data fusion, where information sources include social media platforms, to investigate several possible solutions to reduce uncertainty in the modelling outputs. As a case study, we use spatial data on two dolphin species with different ecological characteristics and distribution, collected in central Tyrrhenian through traditional research campaigns and derived from a careful selection of social media images and videos. We built a spatial log-Gaussian cox process that incorporates different detection functions and thinning for each data source. To finalize the model in a Bayesian framework, we specified priors for all model parameters. We used slightly informative priors to avoid identifiability issues when estimating both the animal intensity and the observation process. We compared different types of detection function and accessibility explanations. We showed how the detection function's variation affects ecological findings on two species representatives for different habitats and with different spatial distribution. Our findings allow for a sound understanding of the species distribution in the study area, confirming the proposed approach's appropriateness. Besides, the straightforward implementation in the R software, and the provision of examples' code with simulated data, consistently facilitate broader applicability of the method and allow for further validations. The proposed approach is widely functional and can be considered with different species and ecological contexts.

中文翻译：

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整合来自多个来源的仅存在数据：海豚空间分布的案例研究

仅存在数据是用于物种分布建模的典型发生信息。数据可能来自不同的来源，它们的整合在空间生态学中是一项具有挑战性的工作，因为很少充分考虑检测偏差。我们提出了一种仅存在数据融合的新协议，其中信息源包括社交媒体平台，以研究几种可能的解决方案，以减少建模输出中的不确定性。作为案例研究，我们使用了两种具有不同生态特征和分布的海豚物种的空间数据，这些数据是通过传统研究活动在第勒尼安中部收集的，并源自精心挑选的社交媒体图像和视频。我们构建了一个空间对数高斯 cox 过程，该过程包含针对每个数据源的不同检测功能和细化。为了在贝叶斯框架中完成模型，我们为所有模型参数指定了先验。在估计动物强度和观察过程时，我们使用了少量信息先验来避免可识别性问题。我们比较了不同类型的检测功能和可访问性解释。我们展示了检测函数的变化如何影响不同栖息地和不同空间分布的两个物种代表的生态发现。我们的发现有助于对研究区域的物种分布有一个合理的了解，证实了所提议方法的适当性。此外，R 软件中的直接实现，以及提供带有模拟数据的示例代码，始终促进该方法的更广泛适用性并允许进一步验证。