DOI: 10.5061/dryad.jsxksn097
title: Temporal changes in the potential geographic distribution of Histiotus velatus (Chiroptera, Vespertilionidae), the “decade effect"

description: Also investigate how the potential distribution of this species changes
 with the addition of new records over the decades (decade effect).
 Assuming that (1: hypothesis of the effect of the decade) the addition of
 new occurrence records over time increases the potential size of the
 species distribution; and (2: Wallacean distance hypothesis) over the
 years, the new points added are increasingly distant from the research
 centers. Considering the geographic knowledge gap of Histiotu velatus, our
 objective is to report a new record of this species and estimate its
 potential distribution in South America through ENMs. For this, we
 compiled records of occurrence of species, selected from 1900 to 2015. We
 used 19 bioclimatic variables available in the WorldCLim database to
 estimate the potential distribution of the species and we used three
 modeling algorithms: Maximum Entropy (MXT) Random Forest (RDF) and Support
 Vector Machine (SVM). We selected the main bat research centers in Brazil,
 using the Lattes platform for the Wallacean distance hypothesis, using the
 Euclidean distance calculation. To test the hypothesis of the decade
 effect, we used beta regression analysis, taking conservative and
 non-conservative approaches. The results showed that the predicted area
 expanded and retracted over the decades, with an improvement in the
 accuracy of the models with the addition of new data. Most of the records
 are located in the southeastern region of Brazil, but the algorithms
 predicted areas in countries where there were no records. Only the
 conservatism approach has had a positive relationship over the decades.
 The distance from new points does not increase over the years of research
 centers.
description: <b>Species
 distribution database and data treatment</b> We
 compiled occurrence records of <i>H. velatus</i> available
 from SpeciesLink (http://www.splink.org.br/index?lang=pt) and GBIF
 (https://www.gbif.org/). We supplemented our geographical distribution
 database with records available in scientific articles using the following
 search code in the Web of Science platform: “bat*” OR “species list” OR
 “<i>Histiotus velatus</i>” OR “<i>H. velatus</i>”.
 We selected only the occurrence records since 1900 because the original
 data were incompatible with the range of the environmental dataset.
 Furthermore, we excluded the following records: (1) undated occurrence
 records; (2) records without coordinates; and (3) outside the Neotropical
 region. Therefore, to investigate the effect of new occurrences over the
 years, we split the data into eight portions: (1) 1900 to 1950; (2) 1900
 to 1960; (3) 1900 to 1970; (4) 1900 to 1980; (5) 1900 to 1990; (6) 1900 to
 2000; (7) 1900 to 2010; and (8) 1900 to 2020 with the addition of the new
 occurrence record localized in Goiânia city.
 <b>Environmental variables and Ecological Niche Models
 (ENMs) </b> We used 19 bioclimatic variables
 (resolution of 9.4 x 9.4 km) for the entire Neotropical realm, available
 in the WorldClim database (http://www.worldclim.org/). These variables are
 derived from monthly temperature and precipitation values sampled
 throughout 1960-1990. Also, these data are often used in ecological
 modeling techniques to estimate the potential distribution of species
 (e.g. Lee et al. 2012; Lisón and Calvo 2013; Sattler et al. 2007). To
 reduce multicollinearity in our dataset, we performed a Principal
 Component Analysis (PCA; Legendre and Legendre 2012) and used the
 eigenvalues as environmental variables. Then, we selected only the axes
 that represent an explanation equal to or greater than 95% (De Marco and
 Nóbrega 2018). We fit models using three algorithms:
 Maximum Entropy (MXT; Phillips et al. 2017; Phillips et al. 2004), Random
 Forest (RDF; Prasad et al. 2006) and Support Vector Machine (SVM; Guo et
 al. 2005). RDF and SVM algorithms require species’ absence data, but we
 this data was not found for <i>H. velatus</i> in the
 literature. Therefore, we created 50 pseudo-absences based on an
 environmental envelope to allocate pseudo-absences only in places
 considered unsuitable for the occurrence of <i>H. velauts
 </i>(Engler et al. 2004). In the case of MXT, models are fitted by
 differentiating between occurrence records and a 10,000 background points
 randomly sampled throughout the study area. We
 evaluated ENMs using a geographical partition (Muscarella et al. 2014,
 Roberts et al. 2017). We divided the study area as a checkerboard, which
 splits the occurrence data in two datasets, and selected each dataset
 alternately to fit and evaluate. This step allows to evaluate model
 predictive capacity, as the geographical partition reduces the spatial
 correlation between datasets used to fit and evaluate the models. Then, we
 measure model predictive capacity by its value for True Skill Statistics
 (TSS), True Positive Rate (TPR) and True Negative Rate (TNR). This
 procedure is considered appropriate in studies on geographic distributions
 of species (Allouche et al. 2006).   We converted the
 suitability models into presence and absence maps using a threshold at
 which the sum of the sensitivity and specificity is highest (Allouche et
 al. 2006). Then, we produced assembled maps using the sum of the binary
 maps derived from the three algorithms. We used the ENMTML package in
 software R for all modeling procedures (Andrade et al. 2020;
 https://github.com/andrefaa/ENM_TheMetaLand). <b>Research
 centers data</b> Brazil is the second country
 with the highest bat richness; however, all of its biomes have a lack of
 information on the occurrence of species distribution (Bernard et al.
 2011). We selected the main research centers that are developing or have
 developed surveys about bats in Brazil. For this, we conducted a search by
 topic in the Lattes platform (http://lattes.cnpq.br/) using the keyword
 “Quiroptera” (in Portuguese). We chose only those researchers that fall in
 one of the CNPq’s Productivity Researchers categories: 1A, 1B, 1C, 1D and
 2. Furthermore, we established as criteria: (1) research projects about
 bats; (2) published articles about bats; and (3) academic guidance in bats
 studies. Included researchers present at least two of these three
 criteria. In situations in which researchers participated in more than one
 research center during their career, we choose the location where those
 professionals spent more time working with bats. We used the Google Earth
 Pro software to consult the geographic coordinates of the research
 centers. <pre style="margin-top:16px;margin-bottom:16px;text-align:justify;"> <b>Statistical analyzes</b></pre> To test the decade effect hypothesis, we performed beta regression analysis (Ferrari and Cribari-Neto 2004) between the number of records over the decades and the proportion of predicted areas, assuming conservatism and non-conservatism approaches. The conservatism approach considers only the areas predicted by all three algorithms, whereas the non-conservatism approach considers all the areas predicted by any algorithm. We chose the beta regression analysis because our response variable is restricted to a range of 0 to 1. We performed this analysis in the <i>betareg</i> package in software R (Cribari-Neto and Zeileis 2010). For the Wallacean distance hypothesis, we calculated the Euclidean distance between each occurrence record to the closest research center using the <i>raster</i> package in the R software (Hijmans et al. 2020). In addition, to reduce a possible forced relationship caused by the excessive number of records, we performed a Weighted Linear Regression considering the total of distances calculated for each year as the weight. Then, we related the maximum distance obtained per unit of time to its respective year. We used the highest values observed per year to find out if further away areas from research centers are sampled over time. We also performed the analysis in the R software, using the lm function of the stats package (R Core Team 2020).