In semi-arid landscapes, water regimes play a critical role in shaping patterns of vegetation regeneration. In riparian and floodplain habitats, however, the importance of flooding versus rainfall is poorly established for many species and habitats. Here, we present the results of a field experiment designed to investigate the establishment responses of both Eucalyptus camaldulensis (river red gum) seedings and understory vegetation to different hydrological conditions within two contrasting habitat types. We ran a field experiment in these habitats along the Condamine River in the northern Murray-Darling Basin in eastern inland Australia. We imposed flooding, rainfall, and drought treatments on 180 seedlings of E. camaldulensis and extant understory vegetation in 18 experimental plots to examine seedling survival and the establishment and dynamics of understory plant assemblages over nine months. Although there was very high seedling mortality overall, our results were consistent with findings from elsewhere in the Murray-Darling Basin indicating that flooding is a critical factor driving the survival of E. camaldulensis seedlings and the resilience of understory vegetation cover and diversity. Although the chance of seedling survival up until ten weeks was higher in the riparian habitat than in the floodplain old-field, the effect of habitat type was reduced under flooded conditions. Despite the low numbers of surviving E. camaldulensis seedlings, the value of a few successfully established trees on old-fields should not be underestimated, nor the potential effects of flooding on restoring the understory. This research highlights that rainfall is unlikely to provide sufficient watering in these habitats for vegetation regeneration.

Maxent is commonly used species distribution modelling (SDM) program due to its better performance over other SDM programs. But model complexity and selecting optimal models are two important concerns for Maxent users. In order to help advance the field we built 44 sets of models by combining 11 regularization multipliers and four feature classes for 10 fish and 28 odonate species of Bhutan with small occurrence data. We then selected optimal models using four sequential optimal model selection approaches: two ORTEST approaches which combined threshold dependent test omission rate (OR) followed by area under receiver operating curve for test data (AUCTEST), and two AUCDIFF approaches that combined OR followed by difference between training AUC and AUCTEST (AUCDIFF) and then AUCTEST. We then screened for ecologically plausible binary suitable/unsuitable model for each species among the optimal models selected by the sequential approaches or from the remaining models using expert knowledge (EXP approach). We then compared different model features and the predicted binary habitat of the optimal models selected by the five approaches. Models selected by ORTEST approaches matched better with ones selected by EXP approach despite them selecting more complex models compared to AUCDIFF approaches. Further, models selected through AUCDIFF approaches overpredicted the habitat more often than the models selected through ORTEST approaches when compared to models chosen by EXP approach. We recommend use of ORTEST approaches for model selection either as the first line of model screening or by their own when less restrictive thresholds are used to produce binary habitat maps as we did here. First, this would reduce time required for expert screening of multiple models for ecologically plausible models when many species are studied. Second, when used alone, ORTEST approaches can avoid either selecting models that under predict or over predict the suitable habitat.