Marcos Jiménez de la Espada

The popularization of citizen-science platforms has increased the amount of data available in a fine spatial and temporal resolution, which can be used to fill distribution knowledge gaps through probabilistic maps. In this study, we gathered expert-based information and used species distribution models to produce two independent maps of the greater rhea ( Rhea americana , Rheiformes, Rheidae) distribution in the state of Rio Grande do Sul, Brazil. We integrated municipality level detection/non-detection data from five citizen-science datasets into a Bayesian site occupancy model, accounting for false negatives, sampling effort, habitat covariates, and spatial autocorrelation. We addressed whether habitat (grassland and crop field cover, number of rural properties) and spatial autocorrelation explains the realized occurrence of the species and compared model-based and expert-based occurrence maps. The mean estimated percentage of occupied municipalities was 48% (239 out of 497 municipalities), whereas experts declared 21% of the municipalities (103) as occupied by the species. While both mapping approaches showed greater rhea presence in most municipalities of the Pampa biome, they disagreed in the majority of the municipalities in the Atlantic Forest, where more fieldwork must be undertaken. The greater rhea distribution was exclusively explained by the spatial autocorrelation component, suggesting that the species expanded its distribution towards the north of the state, reaching the Atlantic Forest, following deforestation and agriculture expansion.

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families – Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) – and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms – Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks – and compare these to two ethnobotanical selection approaches – based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best – attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

The region of central Veracruz is considered a biodiversity hotspot due to its high species richness and environmental heterogeneity, but only 2% of this region is currently protected. This study aimed to assess the current protected area system’s effectiveness and to identify priority conservation areas for expanding the existing protected area system. We used the distribution models of 1186 species from three kingdoms (Animalia, Plantae, and Fungi) together with ZONATION software, a conservation planning tool, to determine areas that could help expand the current network of protected areas. We applied three different parametrizations (including only species, using the boundary quality penalty, and using corridor connectivity). We found that protecting an additional 15% of the area would increase, between 16.2% and 19.3%, the protection of the distribution area of all species. We propose that the regions with a consensus of the three parametrizations should be declared as new protected areas to expand 374 km2 to the 216 km2 already protected. Doing so would double the protected surface in central Veracruz. The priority areas identified in this study have more species richness, carbon stock values, natural vegetation cover, and less human impact index than the existing protected areas. If our identified priority areas are declared protected, we could expect a future recovery of endangered species populations for Veracruz. The proposed new protected areas are planned and designed as corridors connecting currently isolated protected areas to promote biodiversity protection.

The ever-increasing human population, globalisation, and desire to keep pets have resulted in the translocation of many species into non-native environments. As a result, some of the non-native reptile species have been introduced to South Africa through the pet trade. However, little is known about…

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

The tropical forests of Central America serve a pivotal role as biodiversity hotspots and provide ecosystem services securing human livelihood. However, climate change is expected to affect the species composition of forest ecosystems, lead to forest type transitions and trigger irrecoverable losses…

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…

Carnivorous plants (CPs)—those possessing specific strategies to attract, capture and kill animal prey and obtain nutrition through the absorption of their biomass—are harbingers of anthropogenic degradation and destruction of ecosystems. CPs exhibit highly specialised and often very sensitive ecolo…

Niche shifts and environmental non-equilibrium in invading alien species undermine niche-based predictions of alien species’ potential distributions and, consequently, their usefulness for invasion risk assessments. Here, we compared the realized climatic niches of four alien amphibian species (Hyla…

The continental and marine territories of Uruguay are characterised by a rich convergence of multiple biogeographic ecoregions of the Neotropics, making this country a peculiar biodiversity spot. However, despite the biological significance of Uruguay for the South American subcontinent, the distrib…