Doctoral Dissertation Research: Species Distribution Modeling Using Land-use Variables in Support of Habitat Conservation for Reptiles and Amphibians

物种分布模型（SDMs）是生物多样性评估、生态系统管理和保护规划、气候变化和土地利用加速变化对物种分布的影响评估以及入侵物种监测的重要工具。这些模型通过量化已知地点的物种分布与环境特征之间的关系来预测植物或动物物种在景观中的空间分布。SDM中采用了一系列统计和机器学习建模方法，但仍然存在不确定性，即哪种模型类型在不同的建模情况下表现最好，哪些输入变量是最重要的预测变量。本论文研究的目的是对马萨诸塞州（美国东北部）一系列植物和动物物种分布建模的一套不同SDMs的性能进行系统评估。研究区域提供了一个独特的机会来评估模型在一个以过去人类土地利用的重要生态遗产为特征的地区的表现。sdm通常应用于具有陡峭生态梯度特征的生态系统，在这些生态系统中，物种通常达到生理耐受或资源极限，环境变量与物种分布之间存在高度相关性。研究区环境梯度适中，但过去土地利用与动植物物种分布之间存在较好的关系。该研究的结果将提供见解，为什么不同的模型在不同的建模情况下表现不同，包括植物与动物分布，高度栖息地特定物种与通才。六种森林植被的分布将使用统计建模方法（广义线性模型）和两种机器学习方法（决策树和用于规则集预测的遗传算法）进行建模。将使用三种机器学习方法（生态位因子分析、最大熵和用于规则集预测的遗传算法）对六种稀有爬行动物（三种蝾螈和三种海龟）进行栖息地分布建模。这项研究将改善研究地区的森林植被和稀有爬行动物和两栖动物栖息地的地图。通过模拟工作产生的潜在物种分布将与1971年、1985年和1999年的土地覆盖图相结合，以评估土地覆盖变化对物种分布的影响。此外，这项研究将为推广项目提供基础，这些项目将向社区学校的公民科学家和学生介绍地图绘制技术，以及监测该地区景观变化影响的极端重要性。

Species distribution models (SDMs) are an important tool for biodiversity assessments, ecosystem management and conservation planning, assessments of the impacts of climate change and accelerated land-use change on species distributions, and invasive species monitoring. These models predict the spatial distribution of plant or animal species across a landscape by quantifying the relationship between species distributions and environmental characteristics at known locations. A range of statistical and machine-learning modeling approaches have been employed in SDM but there remains uncertainty about which model types perform best in different modeling situations and which input variables are the most important predictor variables. The objective of this dissertation research is to perform a systematic assessment of the performance of a suite of different SDMs for modeling a range of plant and animal species distributions in Massachusetts (northeastern USA). The study area presents a unique opportunity to assess the performance of the models in a region that is characterized by significant ecological legacies of past human land uses. SDMs are commonly applied in ecosystems characterized by steep ecological gradients, where species reach physiological tolerances or resource limits commonly and there is a high correlation between environmental variables and species distribution. Environmental gradients in the study region are moderate, yet there is a well-established relationship between past land-use and the distribution of plant and animal species. The results of the study will provide insights into why different models perform differently in different modeling situations, including plant versus animal distributions and highly habitat specific species versus generalists. The distribution of six forest vegetation species will be modeled using a statistic modeling approach (generalized linear model) and two machine-learning approaches (decision trees and the genetic algorithm for rule-set prediction). Habitat distribution modeling will be performed for six rare herptiles (three salamanders and three turtles), using three machine-learning approaches (ecological niche factor analysis, maximum entropy, and genetic algorithm for rule-set prediction). The study will result in improved maps of forest vegetation and rare reptiles and amphibian habitats in the study area. The potential species distributions produced through the modeling exercises will be combined with land-cover maps dating from 1971, 1985, and 1999 to assess the impact of land-cover changes on species distributions. Additionally, the research will provide the basis for outreach programs that will introduce citizen scientists and students at a neighborhood school to mapping techniques and to the extreme importance of monitoring the impacts of landscape change in the region.