Species distribution modeling and machine learning in assessing the potential distribution of freshwater zooplankton in Northern Italy

To sleep or not to sleep: Dormancy and life history traits in Eucypris virens (Crustacea, Ostracoda)

Dormancy represents an investment with its own costs and benefit. Besides the advantage obtained from the avoidance of harsh environments and from the synchronization of life cycles with seasonal changes, an organism could benefit from a temporary stop in growth and reproduction. To test this hypothesis a transgenerational experiment was carried out comparing the life history traits of clonal females of Eucypris virens from resting and non-resting eggs at two different photoperiods: short day length (6:18 L:D), proxy of favorable but unpredictable late winter-spring hydroperiod, and long day length (16:8 L:D) proxy of dry predictable unfavorable season, inducing resting egg production and within-generation plasticity (WGP). Clonal females that were dormancy deprived showed the highest age at first deposition and the lowest fecundity. Dormancy seems to work as a resetting mechanism of reproduction. Transgenerational plasticity (TGP) had a bounce back pattern: the phenotype of F1 generation was influenced by cues experienced in the F0 generation but the effects of F0 exposure were not evident in the F2. TGP might be adaptive when a mother experiences some kind of seasonality or stochasticity producing both resting and nonresting eggs. A positive relationship between the number of resting eggs and the total number of eggs per females suggested the absence of trade-off between dormancy and reproduction. Both WGP and TGP increase the mother long term fitness with important consequences on population dynamics, on the way a species spread throughout space and time and might respond to climate change.

Machine learning prediction of tree species diversity using forest structure and environmental factors: a case study from the Hyrcanian forest, Iran

The Hyrcanian forest is a global biodiversity hotspot that harbors many endemic and endangered tree species, but its tree diversity is threatened by various human-induced disturbances, such as logging, grazing, and urbanization. To address this issue, we conducted a study using three machine learning methods, i.e., linear regression (LR), random forest (RF), and support vector machine (SVM), to assess and predict tree species diversity within the forest. To do so, we collected an extensive dataset of forest structure and environmental factors from 2725 sample plots located throughout the forest. The Shannon-Wiener diversity index was used to quantify the tree species diversity for each plot. We found that basal area, tree density, and height of trees were the most important predictors of tree diversity, followed by diameter at breast height, elevation, slope, and aspect. We measured the performance of the models using the coefficient of determination (R2), root mean square error (RMSE), and percent of relative error index (PREI), and found RF as the best-performing model in both the training (RMSE = 0.143, R2 = 0.94, and PREI =  - 0.09) and validation (RMSE = 0.15, R2 = 0.94, and PREI =  - 0.09) phases. RF was able to generalize effectively to new data without losing much accuracy or explanatory power. SVM demonstrated a moderate performance training (training phase: RMSE = 0.23, R2 = 0.57, and PREI =  - 0.17) and (validation phase: RMSE = 0.36, R2 = 0.34, and PREI =  - 0.21) among the models, while LR performed the worst (training phase: RMSE = 0.41, R2 = 0.13, and PREI =  - 0.19) and (validation phase: RMSE = 0.41, R2 = 0.11, and PREI =  - 0.36). These findings have broad applications beyond this specific region and can contribute to promoting sustainable land use practices and conservation efforts in other ecosystems facing similar challenges.