Experts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations. Models based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species. Fuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning.

中文翻译：

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使用模糊逻辑比较基于专家知识和抽样记录开发的物种分布模型

专家根据物种与环境之间关系的模糊逻辑假设，利用知识来推断物种的分布。因此，专家知识适合模糊逻辑建模，它为命题提供 0 到 1 之间的连续真值。在物种分布建模中，模糊逻辑还可以用于根据大量记录来建模条件满足的程度。有利于物种的发生。因此，模糊逻辑运算可用于比较和组合基于专家知识和物种记录的模型。在这里，我们将模糊逻辑模型应用于乌拉圭两栖动物的分布，根据专家知识和观察记录推断出有利的位置，有利度是两种数据源的可通约单位。我们比较了受威胁物种、专家认为普遍存在的物种以及非受威胁、非普遍存在的物种的结果。我们基于两个知识源计算模型的模糊交集，以获得有利位置的统一预测。基于专家知识的模型涉及更多的变量，并且受抽样偏差的影响较小。基于专家的模型对这三种物种的高预测率相同，而基于物种记录的模型对普遍存在的物种的预测率较低。基于专家知识的模型与基于受威胁物种的物种记录的相应模型表现相同或更好，即使它们必须对用于构建基于物种记录的模型的同一组记录进行区分和分类。对于受威胁物种，专家模型预测的有利领土比根据记录预测的更具限制性。观察记录为非受威胁的非普遍存在的物种和普遍存在的物种生成了最适合的模型。模糊建模允许对专家知识和不完整的分布记录的潜力进行客观比较，以推断出对不同物种有利的领土。受威胁物种的分布能够通过主观专家知识更好地解释，而基于观测数据的通才物种模型则更加准确。这些结果对于在保护规划中正确使用专家知识具有重要意义。