Data from: Point process models for presence-only analysis

1. Presence-only data are widely used for species distribution modelling, and point process regression models are a exible tool that has considerable potential for this problem, when data arise as point events. 2. In this paper we review point process models, some of their advantages, and some common methods of fitting them to presence-only data. 3. Advantages include (and are not limited to): clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudoabsences or \background points”) objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; ways forward regarding difficult issues such as accounting for sampling bias. 4. Point process models are related to some common approaches to presenceonly species distribution modelling, which means that a variety of different software tools can be used to fit these models, including MAXENT or generalised linear modelling software.

1. 仅存在数据（Presence-only data）被广泛应用于物种分布建模领域，当数据以点事件形式生成时，点过程回归模型（Point Process Regression Models）是一类具备可观应用潜力的灵活工具，可用于处理此类问题。
2. 本文针对仅存在数据场景下的点过程模型、其核心优势以及常见的拟合方法展开综述。
3. 其优势包括但不限于：明确建模所针对的响应变量；提供可客观选取正交点（Quadrature Points，通常也被称为伪无数据点（Pseudoabsences）或背景点（Background Points））的数量与位置的标准化框架；清晰阐明模型假设并配套对应的检验工具；支持处理点间空间相关性的模型；以及针对抽样偏差校正等棘手问题的可行解决路径。
4. 点过程模型与部分主流的仅存在数据的物种分布建模方法存在理论关联，这意味着可通过多种不同的软件工具完成模型拟合，其中包括MAXENT软件以及广义线性建模（Generalised Linear Modelling）相关工具链。