DOI: 10.5061/dryad.d51c5b008
title: Data from: Odonate species occupancy frequency distribution and abundance – occupancy relationship patterns in temporal and permanent water bodies in a subtropical area

description: This paper investigates species richness and species occupancy frequency
 distributions (SOFD) as well as patterns of abundance-occupancy
 relationship (SAOR) in Odonata (dragonflies and damselflies) in a
 subtropical area. A total of 82 species and 1983 individuals were noted
 from 73 permanent and temporal water bodies (lakes and ponds) in the Pampa
 biome in southern Brazil. Odonate species occupancy ranged from 1 to 54.
 There were few widely distributed generalist species and several
 specialist species with a restricted distribution. About 70% of the
 species occurred in less than 10% of the water bodies, yielding a
 surprisingly high number of rare species, often making up the majority of
 the communities. No difference in species richness was found between
 temporal and permanent water bodies. Both temporal and permanent water
 bodies had odonate assemblages that fitted best with the unimodal
 satellite SOFD pattern. It seems that unimodal satellite SOFD pattern
 frequently occurred in the aquatic habitats. The SAOR pattern was positive
 and did not differ between permanent and temporal water bodies. Our
 results are consistent with a niche-based model rather than a
 metapopulation dynamics model.
description: 2.1 Data, methods and study area Originally,
 most of the Brazilian Pampa biome (Fig 1) consisted of grassland (hence,
 the Pampa is treated by many authors as the “Southern Fields”), and of
 sparse shrub and forest vegetation (Overbeck et al., 2009). Many areas
 within this biome have, however, been changed by human activities, mainly
 agriculture, cattle farming and silviculture (Baldi & Paruelo,
 2008; Overbeck et al., 2013). This biome is still one of the least
 protected in Brazil: Oliveira et al. (2017) notes that only 0.8% of the
 Pampa is protected. Data from 53 permanent and 20
 temporal lakes and ponds (water bodies) was used (Figure 1; Renner et al.,
 2018; 2019). We sampled adult dragonflies from March 2011 to April 2017,
 visiting the localities from one (temporary waters) up to seven times
 during this period. We followed the method described by Renner et al.,
 (2018; 2019; cf. original publications for more detailed information),
 collecting dragonflies on sunny days during the peak period of odonate
 activity (between 09:00 h to 16:00 h). Two persons using hand-held insect
 nets walked along the perimeter of the sites, along the water edges and
 marginal zones. The average time spent at each sampling site was 45 min.
 This sampling method is opportunistic, and although its efficiency is
 constant, the probability of detecting the rarest species is reduced.
 Several papers discuss the problem of detecting all species at a given
 site (e.g., Hedgren & Weslien, 2008; Raebel et al., 2010; Bried et
 al., 2012a; 2012b; Hardersen et al., 2017), highlighting the importance of
 detecting also rare species (Cao et al., 1998). Mao & Colwell
 (2005) pointed out that there is only a small chance to detect the rarest
 species at a site, but that modern modelling approaches combined with
 iterative sampling seems to be a way forward (Young et al., 2019). In
 order to ascertain the species occupancy relationship patterns, it is
 crucial to show whether the number of rare species (satellite species; see
 below) is high or low. The impact on the results of a possible
 under-estimate of the number of rare species due to incomplete sampling is
 addressed in the discussion. Another limitation of our
 method is that temporary waters cannot be sampled repeatedly over a number
 of months (as they dry out). Although most of our permanent sites were
 visited repeatedly, it is therefore impossible to test for temporal
 variation among our samples. Renner et al. (2016) showed, for a smaller
 dataset within the same area, that although some of the species were
 seasonal, the species composition remained relatively similar throughout
 the year (Sørensen index 0.73–0.83). 2.2 Statistical
 methods Although blunt compared to more complicated
 hierarchical multispecies models (Iknayan et al., 2014), our opportunistic
 data was better suited for the traditional jackknife method to estimate
 species richness data in temporal, permanent and combined water bodies,
 and a 95 % confidence interval was applied to each type of water body
 separately (see more details in Krebs, 1999). The jackknife species
 richness estimate builds on the frequency of rare species observed within
 the community. Here each odonate species was recorded as present (1) or
 absent (0) in each water body. We also calculated the number of unique
 species, defined as occurring in only one water body. We used the equation
 by Heltsche & Forrester (1983) to calculate the estimated species
 richness: Ŝ = s + ((n-1)/n)*k Where
 Ŝ = Jackknife estimate of species richness, s = Observed total number of
 species present in n water bodies, n = Number of water bodies in total and
 k = Total number of unique species in n water bodies. For each water body
 we also estimated the species richness with Chao1 method in R-package
 ‘vegan’ v.2.4-2. We used the Moran I index to test
 spatial autocorrelation between the faunistic similarity and the
 geographical distance between water bodies. We used both Jaccard
 dissimilarity and Bray dissimilarity, which based on the abundance of each
 individual species, indexes as a distance measure of odonate species
 dissimilarity and community dissimilarity, respectively. The Euclidean
 distance (in km) was used for geographical coordinates of the water
 bodies. The Mantel test was calculated with R-package ‘vegan’ v.2.4-2.,
 and the statistical significance was estimated running 999 permutations.
 As spatial autocorrelation would nullify or affect the results of the
 Mantel test, we tested the spatial independence of species composition at
 the 73 sampling sites using a Moran’s I analysis. We used individual
 species occurrences as variables in a Principal Component Analysis (PCA),
 where the first axis was used as response variable to the Moran’s I with
 coordinate variables for ten different distance classes. The global
 Moran’s I analysis detected no significant spatial structure of the
 species composition for any distance class (minimal distance class
 average: 0.148 degree; Moran’s I = 0.018; p = 0.059). Hence, we can rely
 on the results of the Mantel test. Following McGeoch
 and Gaston (2002), we used classes of 10% occupancy, and the number or
 percentage of odonate species in each class, to demonstrate the variation
 in occupancy frequency distribution between temporal and permanent water
 bodies (see also Korkeamäki et al., 2018). We also tested the relationship
 between water body area (m<sup>2</sup>), length of shoreline
 (m) and species richness, using Pearson correlation with a
 log<sub>10</sub> transformation to compensate for large
 differences in size. We used the same approach as
 Korkeamäki et al. (2018), where the multi-model inference approach was
 applied to regressions of ranked species-occupancy curves (RSOCs as in
 Jenkins (2011). The three data sets (using temporal, permanent, and
 combined data; species in rows and water bodies in columns), were
 processed separately based on occupancy (presence/absence) data for
 individual water bodies. First, we calculated the proportion of water
 bodies occupied by each species (occupancy frequency) using the sum of
 water bodies. Second, we divided the occupancy frequency of each species
 by the number of water bodies, resulting in the number (relative
 proportion) of water bodies occupied by each species. In the following
 step, we arranged the species in decreasing order according to their
 relative occupancy values, setting R<sub>i</sub> as the rank
 value for species i, from which we plotted the relative occupancy of
 species (O<sub>i</sub>) as functions of
 R<sub>i</sub> (RSOC). Finally, we evaluated whether a
 unimodal-satellite dominant, a bimodal symmetrical, a bimodal asymmetrical
 or a random pattern best fitted our odonate community (cf., Jenkins,
 2011). We used the IBM SPSS statistical package version 23 for all
 statistical calculations. As in Jenkins (2011), the Levenberg–Marquardt
 algorithm (with 999 iterations) was used for the nonlinear regressions,
 estimating the parameters (y<sub>0</sub>, a, b and c) of the
 following four equations (by means of ordinary least squares (OLS)) to
 find the best fitting SOFD pattern. The equations are:
 O<sub>i</sub> = y<sub>0</sub>
 + a*exp(-bRi) with initial parameters y<sub>0</sub> = 0.01, a
 = 1.0, b = 0.01; Unimodal-satellite mode (exponential concave)
 pattern. O<sub>i</sub> = a/(1 + exp (-bRi
 + c), initial parameters a = 1.0, b = -0.1, c = -1.0; Bimodal symmetrical
 (sigmoidal symmetric) pattern.
 O<sub>i</sub> = a[1 - exp
 (-bRi<sup>c</sup>)], initial parameters a = 1.0, b = -1.0, c =
 -1.0; Bimodal asymmetric (sigmoidal asymmetric) pattern.
 Oi = aRi + b, initial parameters a = 0.01, b = 0.01; Uniform
 (random) pattern. We also examined the
 regressions graphically for homogeneity of variance, normality of
 residuals and independent error terms, as well as the tails and shoulders
 of the data and models (see more details in Jenkins 2011; Korkeamäki et
 al., 2018). The Akaike Information Criterion for small
 sample sizes (AICc) was used to compare the four
 alternative models, where the one with the smallest AICc would be best,
 based on the Kullback-Leibler distance (Burnham and Anderson 2000). This
 approach works well to detect differences between models when values for
 ΔAICc (= AICc<sub>min</sub> - AICc<sub>i</sub>)
 are higher than 7 (Anderson et al., 2000; Burnham & Anderson,
 2000; Burnham et al., 2011; Jenkins, 2011). We used a
 Generalized Linear Model (GLM) to investigate the relationships between
 number of individuals and occupancy frequency (independent variable) in
 the SAOR-model. The model type was negative binomial distribution with log
 link (type III errors) (O’Hara & Kotze, 2010). Habitat preference
 was divided into three categories: species observed only in (i) temporary
 water bodies, 9 species, (ii) permanent water bodies, 34 species and (iii)
 both types of habitats (hereafter generalist species), 39 species. In
 order to test differences in occupancy frequency and number of individuals
 between three habitat preference categories of odonate species, we applied
 the Generalized Linear Model with negative binomial distribution; log link
 (type III errors), using habitat preference as a factor.