Mair Louise, Harrison Philip J, Jönsson Mari, Löbel Swantje, Nordén Jenni, Siitonen Juha, Lämås Tomas, Lundström Anders, Snäll Tord
Swedish Species Information Centre Swedish University of Agricultural Sciences (SLU) Uppsala Sweden.
Swedish Species Information Centre Swedish University of Agricultural Sciences (SLU) Uppsala Sweden; Department of Environmental System Analysis Institute of Geoecology Technical University Braunschweig Braunschweig Germany.
Ecol Evol. 2016 Dec 20;7(1):368-378. doi: 10.1002/ece3.2601. eCollection 2017 Jan.
The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km) by comparison against forecasts from a model based on systematically collected colonization-extinction data. We fitted species distribution models using citizen science observations of an old-forest indicator fungus . We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020-2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization-extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set-aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization-extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization-extinction model. All models thus highlighted the importance of protected old forest for persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization-extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.
许多公民科学数据集（CSD）广泛的时空覆盖范围使其在物种分布建模和预测中颇具吸引力。然而，一个常见的限制是无法验证结果。在此，我们旨在通过与基于系统收集的定殖 - 灭绝数据的模型预测进行比较，评估CSD在响应国家森林管理预测（代表160,366平方公里）时预测物种出现情况的可靠性。我们使用公民科学对一种老龄森林指示真菌的观测数据拟合了物种分布模型。我们应用了五种建模方法（广义线性模型、泊松过程模型、贝叶斯占有模型和两种最大熵模型）。这些模型用于预测2020 - 2110年响应国家森林管理时物种出现情况的变化。基于CSD的模型对物种出现情况的预测与基于系统收集数据的定殖 - 灭绝模型的预测一致，尽管不同的建模方法显示出不同程度的变化。所有模型都预测2020年至2110年预留森林中的物种出现情况会增加：基于CSD的模型预测增加幅度在125%至195%之间，相比之下，根据定殖 - 灭绝模型增加了129%。除一个基于CSD的模型外，所有模型都预测生产林中的物种出现情况会下降，下降幅度在11%至49%之间，而定殖 - 灭绝模型的下降幅度为41%。因此，所有模型都强调了保护老龄森林对物种存续的重要性。我们得出结论，基于CSD的模型能够重现基于系统收集的定殖 - 灭绝数据的模型的预测结果，从而得出相同的森林管理结论。我们的结果表明，使用一组模型可以使CSD可靠地应用于土地管理和保护决策，这表明广泛可得的CSD可以成为一种有价值的预测资源。
