The impact of forest roads on understory plant diversity in temperate hornbeam-beech forests of Northern Iran.

Forest roads alter the biotic and abiotic components of ecosystems, modifying temperature, humidity, wind speed, and light availability that, in turn, cause changes in plant community composition and diversity. We aim at investigating and comparing the diversity of herbaceous species along main and secondary forest roads in a temperate-managed hornbeam-beech forest, north of Iran. Sixteen transects along main and secondary forest roads were established (eight transects along main roads and eight along secondary roads). To eliminate the effect of forest type, all transects were located in Carpinetum-Fagetum forests, the dominant forest type in the study area. The total length of each transect was 200 m (100 m toward up slope and 100 m toward down slope), and plots were established along it at different distances from road edge. The diversity of herbaceous plant species was calculated in each plot using Shannon-Wiener index, species richness, and Pielou's index. The results showed that diversity index decreased when distance from road edge increases. This decreasing trend continued up to 60 m from forest road margin, and after this threshold, the index slightly increased. Depending on the type of road (main or secondary) as well as cut or fill slopes, the area showing a statistical different plant composition and diversity measured through Shannon-Wiener, species richness, and Pielou's index is up to 10 m. The length depth of the road edge effect found in main and secondary forest roads was small, but it could have cumulative effects on forest microclimate and forest-associated biota at the island scale. Forest managers should account for the effect of road buildings on plant communities.

Anticipating species distributions: Handling sampling effort bias under a Bayesian framework.

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.