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Aksu, S., D. Mercan, N. Arslan, Ö. Emiroğlu, P. J. Haubrock, I. Soto, and A. S. Tarkan. 2024. Determining environmental drivers of global mud snail invasions using climate and hydroclimate models. Hydrobiologia. https://doi.org/10.1007/s10750-024-05554-x

Climate change and invasive species represent two intertwined global environmental challenges profoundly affecting freshwater ecosystems. This study uses Ecological Niche Modeling along with risk screening to delve into the preferences and potential distribution of Potamopyrgus antipodarum , an invasive species, in relation to climate zones and habitat types, shedding light on the critical importance of coastal wetlands and high soil organic carbon content in shaping habitat suitability. Our findings underscore that P. antipodarum exhibits a distinct affinity for cool temperate, moist climates, as well as temperate floodplain rivers, wetlands, and coastal areas. Notably, coastal wetlands, endowed with elevated soil organic carbon levels, emerged as pivotal habitats for this species. Projections indicated a significant expansion in North America, potentially extending into South America. Türkiye reveals an intriguing alignment between its habitat and the natural distribution areas of P. antipodarum , presenting potential for habitat contraction while still retaining a broader range compared to other regions. These potential expansions were predominantly driven by climate suitability, playing a pivotal role in the invasiveness of P. antipodarum , with anticipated future climate regimes exerting substantial influence on its dispersal capabilities.

随机森林(Random forest)模型在2001年发表后得到广泛的关注。由于随机森林可以进行回归和判别等多种统计分析,而且不受正态性、方差齐性和自变量独立性等参数检验的前提条件的制约,其应用日益普遍,有被看作万能模型的趋势。实际上,随机森林是一种特点鲜明的模型,应用局部优化拟合观察值,在分析有偏效应关系的数据时,其结果往往不准确。本文以蝉科(Cicadidea)物种的分布数据为例,比较了随机森林在回归分析时与多元线性回归、广义可加模型和人工神经网络模型的差别,在判别分析时与线性判别分析的差别,强调了随机森林预测时的碎片化特点。结果显示随机森林在处理有多元共线性和交互作用的数据时,以及在判别…

Li, X., B. Li, G. Wang, X. Zhan, and M. Holyoak. 2020. Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term. MethodsX 7: 101067. https://doi.org/10.1016/j.mex.2020.101067

In multiple regression Y ~ β0 + β1X1 + β2X2 + β3X1 X2 + ɛ., the interaction term is quantified as the product of X1 and X2. We developed fractional-power interaction regression (FPIR), using βX1M X2N as the interaction term. The rationale of FPIR is that the slopes of Y-X1 regression along the X2 gr…