MODELING THE DISTRIBUTION OF THE SOUTHERN YELLOW-CHEEKED GIBBON (NOMASCUS GABRIELLAE) USING MAXENT

The Southern Yellow-cheeked Gibbon (Nomascus gabriellae) is an endangered species found only in a small region of Indochina, and its populations have declined in most known sites. In this study, we use Maxent, a robust and widely used species distribution modeling approach, to predict the current and future distributions of the Southern Yellow-cheeked Gibbon over its entire range based on an extensive review of published records. In total, we compile and provide a comprehensive set of known distribution records of the species from Cambodia, Laos, and Vietnam. The model results show that N. gabriellae potentially occurs in much of area around the Central Highlands in both Vietnam and Cambodia sides and the southern end of Laos. Our study suggests that protected areas in this region will play a key role in conservation actions for the gibbons. In addition, the distribution of the gibbon in future climate conditions, even in the best-case scenario, is likely to shrink significantly, as the species would have to move upwards to higher elevations. Under such impact, the populations will become more fragmented and restricted to a few areas with higher elevations. Our study also confirms that the climatic difference in distribution ranges may not be fully responsible for the speciation and biogeography of the N. annamensis/gabriellae complex.

Matching Data Types to the Objectives of Species Distribution Modeling: An Evaluation With Marine Fish Species

Species distribution model (SDM) is a crucial tool for forecasting ranges of species and mirroring habitat references and quality. Different types of species distribution data have been commonly used in SDMs regarding different purposes and availability, whereas, the influences of data types on model performances have not been well understood. This study considered three data types characterized by different levels of organism information and cost in data acquisitions, namely presence/absence (P/A), ordinal data, and abundance data. We developed a range of distribution models for nine demersal species in the coastal waters of Shandong Peninsula, China, using two modeling algorithms [the Generalized Additive Model (GAM) and Random Forest]. Firstly, we evaluated the performances of all models on predicting species occurrence (i.e., habitat suitability or range boundaries), and then compared the models built with ordinal data and abundance data on projecting ordinal predictions (i.e., relative density or habitat quality). Their predictive abilities were assessed through cross-validation tests with diverse performance measurements. Overall, no data type is superior in all situations, but combined with two algorithms, the abundance data slightly outperformed the ordinal data and P/A data unexpectedly exerted reliable performances. Specifically, the effectiveness of data type for two application purposes of SDMs substantially varied with modeling algorithms, revealing that GAMs always benefit most from ordinal data and the opposite was true for Random Forest. For some small resident organisms with moderate prevalence, rough distribution data might be adopted for providing reliable projections. Our findings highlight the importance of clarifying the objectives of SDMs when choosing data types for species distribution modeling.

Flora of Ferruginous Outcrops Under Climate Change: A Study in the Cangas of Carajás (Eastern Amazon)

Climate change has impacted biodiversity, affecting species and altering their geographical distribution. Besides understanding the impact in the species, it has been advocated that answering if different traits will be differently impacted could allow refined predictions of how climate change will jeopardize biodiversity. Our aim was to evaluate if climate change will potentially impact plant species differently, considering their traits. We evaluated 608 plant species that occur in the naturally open areas of ferruginous outcrops (namely, cangas) in the National Forest of Carajás (Eastern Amazon). Firstly, we estimated the effects of climate change on each species using species distribution modeling, and analyzed this impact in the set containing all species. Secondly, we classified plant species considering the following traits: (i) pollination syndromes (melittophily, phalaenophily, psychophily, cantharophily, entomophily, ornithophily, chiropterophily, anemophily); (ii) habit (tree, shrub, herb, liana, parasite); and (iii) the main habitat of occurrence (open areas and forests). Thirdly, we investigated if the effects of climate change could be significantly more intense considering all the different traits quoted. Our results showed that most plant species will potentially face reduction of suitable habitats under future climate and the scenarios showed that 42% of them may not find suitable areas in the cangas of Carajás. We found no significant difference within each analyzed trait, considering the potential impact of climate change. The most climatically suitable areas (i.e., areas with high probability of species occurrence in the future) are those in the southwest of the study area. These areas can be considered as priority areas for species protection against climate change.