Spatial Assessment of the Bioclimatic and Environmental Factors Driving Mangrove Tree Species' Distribution along the Brazilian Coastline
Brazil has one of the largest mangrove surfaces worldwide. Due to a wide latitudinal distribution, Brazilian mangroves can be found within a large range of environmental conditions. However, little attention has been given to the description of environmental variables driving the distribution of mangrove species in Brazil. In this study, we present a novel and unprecedented description of environmental conditions for all mangroves along the Brazilian coast focusing on species limits. We apply a
... limits. We apply a descriptive statistics and data-driven approach using Self-Organizing Maps and we combine data from terrestrial and marine environmental geodatabases in a Geographical Information System. We evaluate 25 environmental variables (21 bioclimatic variables, three sea surface temperature derivates, and salinity). The results reveal three groups of correlated variables: (i) air temperature derivates and sea surface temperature derivates; (ii) air temperature, potential evapotranspiration and precipitation derivates; and (iii) precipitation derivates, aridity and salinity. Our results unveil new locations of extreme values of temperature and precipitation. We conclude that Rhizophora harrisonii and Rhizophora racemosa are more limited by precipitation and aridity and that they do not necessarily follow a latitudinal gradient. Our data also reveal that the lowest air temperatures of the coldest month are not necessarily found at the southernmost limits of mangroves in Brazil; instead they are localized at the Mesoregion of Vale do Itajaí. However, the minimum sea surface temperature drops gradually with higher latitudes in the Brazilian southern hemisphere and is probably a better indicator for the decrease of species at the latitudinal limits of mangroves than the air temperature and precipitation. climatic variability, the environmental conditions driving mangrove characteristics are still not fully understood [2, 3] . Strikingly, only one study carried out by Schaeffer-Novelli et al.  has so far assessed the environmental factors on the coastline range of the Brazilian mangroves that was instrumental in improving our understanding of mangrove ecosystems. At the time of the study , several bottlenecks limited a more comprehensive and detailed assessment to understand the main climate pattern for Brazilian mangroves. However, the climatic database used by the previous study  is over 40 years old (Brazilian Ministry of Agriculture, 1972) with few climatic data samples coming mainly from states' capitals. Given these bottlenecks, the spatial variability of environmental conditions in the major Brazilian coastal mangroves is still unknown, considerably increasing uncertainties on the characterization of mangrove structure and species' composition. Currently, the availability of satellite data for ocean, meteorological stations and spatially interpolated climate surfaces with high resolution have improved environmental information in sites where there is a lack of local data [4-6] and have integrated them in Geographical Information Systems (GIS). These remote sensing products and spatially interpolated surfaces enable us to obtain information that a few decades ago was not available. Thus far, the most recent environmental databases [4-6] and mangrove mappings [7-9], have not yet been used to update the environmental characterization of Brazilian mangroves. Therefore this large quantity of data creates an opportunity for scientists that requires new big data analysis techniques and tools    . Responding to this need, science that takes a "data-driven" approach is now emerging  in which the information is extrapolated from the data. In this context, the Self-Organizing Maps (SOM)  provide a "data-driven" approach that it is supported by tools of data representation, mainly characteristic of data abstraction enhanced by visualization techniques [14, 15] . The SOM have been used in several applications, such as mapping ecological and biogeographical features      , determination of the most suitable sites for forest restoration  and selecting bioclimatic variables for species distribution modeling in the Brazilian north region  . The main advantage of the SOM algorithm as a data-driven approach is that it does not need to assume any a priori hypotheses [14, 23] ; it still has a robustness when data behavior is unknown and it shows the multivariate data cloud through visualization tools [14, 23] or rather as geovisualization tools  . The SOM's ability to preserve the topological structure of the input data [14,23] provides a powerful advantage in studies with geospatial analysis  . Giraudel and Lek  compared the SOM with Principal Component Analysis (PCA) and Correspondence Analysis and they found similar results, with the latter two validating the SOM methods. Hence, SOM or a combination of SOM and ordination analysis seems to be a promising technique in ecological studies to explore multivariate data. This study focuses on the ecological biogeography of finding patterns in the distribution of species that are constrained by bioclimatic and environmental variables  , and due to the wide latitudinal and longitudinal range of the Brazilian mangroves, climate has a strong influence on the delimitation of species limits. In short, we address the following questions: Which environmental variables influence the spatial distribution of mangrove species? Can the bioclimate and environmental data at higher temporal and spatial resolution improve the characterization of the Brazilian mangrove ecosystems? To answer these questions, we have set out four objectives: (i) to update the information on climatic and salinity conditions in Brazilian mangroves using the most recent environmental databases; (ii) to overcome data gaps found in the previous studies; (iii) to cluster relevant environmental variables according to their spatial dependence; and (iv) to provide a better understanding of the fundamental niche  of the mangrove plant species. With that in mind, we present an unprecedented data-intensive approach for the assessment of the environmental variables that drive species composition/distribution in mangroves along the entire Brazilian coastline.