Combining plant and bird data increases the accuracy of an
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Combining plant and bird data increases the accuracy of an
Accepted Manuscript Title: Combining plant and bird data increases the accuracy of an Index of Biotic Integrity to assess conservation levels of tropical forest fragments Author: Hugo Reis Medeiros Gabriela Menezes Bochio Milton Cezar Ribeiro José Marcelo Torezan Luiz dos Anjos PII: DOI: Reference: S1617-1381(15)00009-6 http://dx.doi.org/doi:10.1016/j.jnc.2015.01.008 JNC 25402 To appear in: Received date: Revised date: Accepted date: 19-6-2014 28-1-2015 29-1-2015 Please cite this article as: Medeiros, H. R., Bochio, G. M., Ribeiro, M. C., Torezan, J. M., and dos Anjos, L.,Combining plant and bird data increases the accuracy of an Index of Biotic Integrity to assess conservation levels of tropical forest fragments, Journal for Nature Conservation (2015), http://dx.doi.org/10.1016/j.jnc.2015.01.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. 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Combining plant and bird data increases the accuracy of an Index of Biotic 2 Integrity to assess conservation levels of tropical forest fragments 3 Hugo Reis Medeiros1, Gabriela Menezes Bochio1, Milton Cezar Ribeiro2, José Marcelo 4 Torezan3, and Luiz dos Anjos4 ip t 1 5 1 7 Animal e Vegetal,Universidade Estadual de Londrina, Caixa Postal 6001, 86051-970 8 Londrina, Paraná, Brasil. 2 us Programa de Pós-Graduação em Ciências Biológicas, Departamento de Biologia Laboratório de Ecologia Espacial e Conservação - LEEC, Departamento de an 9 cr 6 Ecologia,Universidade Estadual Paulista - UNESP, Caixa Postal 13506-900 Rio Claro, 11 São Paulo, Brasil. 12 3 13 Departamento de Biologia Animal e Vegetal, Universidade Estadual de Londrina, Caixa 14 Postal 6001, 86051-970 Londrina, Paraná, Brasil. 15 4 16 Vegetal, Universidade Estadual de Londrina, Caixa Postal 10011, 86051-970 Londrina, 17 Paraná, Brasil. te d Laboratório de Biodiversidade e Restauração de Ecossistemas - LABRE, Laboratório de Ornitologia e Bioacústica, Departamento de Biologia Animal e Ac ce p 18 M 10 19 Abstract 20 Rapid ecological assessment methods, such as Rapid Ecological Assessments (REA) 21 and Indexes of Biotic Integrity (IBI) are useful tools for the selection of priority areas 22 for biodiversity conservation. However, the majority of rapid assessment methods are 23 based on data from a single taxonomic group; a multi-taxa index should provide a more 24 integrated evaluation of the response of a disturbed system. In this study, we propose a Page 1 of 28 new, easy-to-follow, integrated Index of Biotic Integrity (IBIint) which combines plants 26 and birds to assess ecological integrity of tropical forest fragments. This integrated 27 index combines the information of two previously developed rapid assessment methods: 28 REA for plants and IBI for birds. These two indexes were built based on key vegetation 29 features and on levels of sensitivity to forest fragmentation of bird species. We applied 30 IBI, REA and the new IBIint indexes on the characterization of 10 forest fragments and 31 in a large continuous forest block (reference area). We also tested the correlation of the 32 proposed index (IBIint), REA and IBI with patch size, forest amount and connectivity at 33 four spatial scales (250, 500, 1000, 1500 meters). Our hypothesis was that IBIint would 34 be more correlated with landscape metrics than the REA and IBI. As expected, IBIint 35 was the more accurate index once it was explained by all landscape variables: area of 36 forest fragments; forest connectivity; and, percentage of forest cover at four spatial 37 scales. REA and IBI were explained only by one of those parameters. We conclude that 38 IBIint can be an excellent tool to aid conservationists and managers for defining 39 conservation strategies in scenarios with fast habitat loss. cr us an M d te Ac ce p 40 ip t 25 Rapid assessment methods; Ecological 41 Keywords: 42 configuration; Avifauna, Vegetation structure, Bioindicators. 43 44 integrity, Landscape Introduction 45 Rapid assessment methods used for environmental evaluation, such as Rapid 46 Ecological Assessments (REA; Sobrevila & Bath, 1992) and Indexes of Biotic Integrity 47 (IBI; Karr, 1981), are important tools in aiding conservationists and decision makers in 48 the selection of conservation strategies (Pont et al., 2006). These techniques are used to 49 quickly assess the environmental value of different localities through evaluation of a Page 2 of 28 finite and highly informative set of ecological indicators collected in the field (Karr, 51 1999; Sayre et al., 2000; Andreasen et al., 2001). Different taxonomic groups, such as 52 plants (Mack, 2004), fishes (Lyons et al., 2000), amphibians (Micacchion, 2002), 53 macroinvertebrates (Roque et al., 2010; Couceiro et al., 2012), birds (Glennon & Porter, 54 2005), as well as landscape structure (Barreto et al., 2010) have been used as indicators 55 of environmental integrity. cr ip t 50 Indexes using plants or birds have been developed for a variety of habitats, mostly 57 found in temperate regions, including riparian (Bryce et al., 2002), wetlands (DeLuca et 58 al., 2004; Rooney & Bayley, 2012), arid (Allen, 2009), grasslands (Coppedge et al., 59 2006), and forests ecosystems (O’Connell et al., 2007). However, the majority of rapid 60 assessment methods are based on data from a single taxonomic group (Diffendorfera et 61 al., 2007), although an index with more taxa should provide a more integrated 62 evaluation of the response of a disturbed system (Nieme et al., 2004). d M an us 56 In this study, we proposed a new integrated Index of Biotic Integrity (IBIint) that 64 combines plants and birds to assess ecological integrity of tropical forest fragments. 65 This integrated index includes the information of two previously developed rapid 66 assessment methods (REA for plants and IBI for birds). These two indexes were built 67 based on key vegetation features (REA; Medeiros & Torezan, 2013) and on levels of 68 sensitivity of bird species to forest fragmentation (IBI; Anjos et al., 2009). To test the 69 accuracy of the proposed index IBIint and both REA and IBI, we evaluated the relative 70 contribution of forest amount and landscape configuration to explaining the three 71 biodiversity indexes. Explanatory landscape structure variables included: (1) area of 72 forest fragments; (2) forest connectivity; and, (3) percentage of forest cover at four 73 spatial scales (250, 500, 1000, 1500 meters). It is known that the decrease of those three 74 variables influences species persistence, leading to a decline in the biological integrity Ac ce p te 63 Page 3 of 28 of a given forest fragment (Fahrig, 2003; Martensen et al., 2008; Martensen et al., 2012; 76 Silva et al., 2014). Our hypothesis is that this decline in the biological integrity could be 77 better captured by IBIint than the indexes based on only one single taxonomic group, 78 such as REA or IBI. ip t 75 Methods 81 Study area us 80 cr 79 This study was carried out in a large forest block (187,000 ha) and in 10 forest 83 fragments, all located in Paraná State, southern Brazil. The predominant vegetation in 84 the region is the Seasonal Semideciduous Forest (SF), a type of Atlantic Forest that 85 occurs mainly in south and southeast Brazil. The SF is considered one of the most 86 threatened ecosystem types of the Atlantic Forest hotspot. Indeed, SF has been 87 systematically converted into monocultures of sugarcane, soybean and eucalyptus, as 88 well as pasture and cities; consequently, only 7% of its original cover still remains 89 (Ribeiro et al., 2009). This ecosystem is characterized by a partial deciduousness with 90 about 20 to 50% of trees losing their leaves during the dry season (Carvalho, 2003). The 91 canopy height reaches 8 to 15 m, though some emergent trees have been known to reach 92 20 m high. In addition to vines and epiphytes, a complex litter layer is also common in 93 SFs (Batalha, 1997). The SF also faces a large carbon loss (~60%) because of forest 94 fragmentation, when compared to control areas (i.e. patches > 10,000 ha; Putz et al., 95 2014). The large forest block cited above is the Iguaçu National Park (hereafter IP), 96 which is the largest and best preserved remaining patch of SF for the entire Atlantic 97 Forest domain (Ribeiro et al., 2009). Therefore, we considered IP as the reference area 98 in this study. The IP and the set of 10 sampled forest fragments are located in the 99 extreme west and in the north of Paraná State, respectively (Fig. 1). The studied forest Ac ce p te d M an 82 Page 4 of 28 fragments ranged from 60 to 876 ha and were located in landscape contexts with 101 different degrees of SF functional connectivity and forest cover (Table 1). Connectivity 102 is the capacity of the landscape to influence biological fluxes (Taylor et al., 1993), and 103 functional connectivity depends on the dispersal ability of fauna to access scattered 104 habitats or resources (Boscolo et al., 2008; Martensen et al., 2012). Please refer to the 105 landscape variables section for details about metric calculations. cr ip t 100 Table 1. Location of 10 forest fragments and the reference area selected for 107 this study. FF = identification of forest fragments, Size (ha) = the size of forest 108 fragments in hectares (ha), FC (%) = amount of forest cover within the radius 109 of 1000 m, and Prox = proximity index, a proxy for functional connectivity. Proxb FC (%) 187,000 10,000 100.0 MG 650 88.1 44.8 MF 876 0.1 47.2 AT 85 9.0 22.9 EI 60 0.0 PI 74 9.6 PQ 542 25.6 CO 564 BU 288 DO 166 SH 85 113 114 25°09’12”S 53°50’15”W 23°26’46”S 51°14’46”W 23°09’37”S 50°34’00”W M 23°20’41”S 51°08’23”W 29.2 22°46’49’’S 51°29’21’’W 9.5 23°30’05”S 51°04’39”W 3.9 40.0 23°28’12”S 51°02’50”W 5,903 39.5 23°24’19”S 51°19’31”W 0.0 0.0 23°18’05”S 50°59’11”W 9.0 21.9 23°24’38”S 51°14’09”W d 23°15’21’’S 51°01’53”W Ac ce p 110 111 112 a Coordinates 0.0 te IP a an Size (ha) FF us 106 b Reference area; Proximity index and forest cover used a search radius of 1000 meters Vegetation structure We characterized the vegetation using the REA method, which was previously 115 proposed by Medeiros & Torezan (2013). The REA is based on the plant community 116 structure and was developed to assess the vegetation quality of forest fragments. REA 117 comprises 10 ecological integrity variables, each of them rated from 1 (lowest integrity) 118 to 5 (highest integrity). The selected ecological variables were: 1 - number of standing 119 dead trees; 2 - presence and cover of exotic grasses; 3 - presence of other exotics; 4 - 120 abundance of vine tangles; 5 - types of eco-units present (light gaps with many vines, Page 5 of 28 light gaps with few vines, low canopy up to 10 m tall, open canopy with up to 60 % of 122 lightness and closed canopy with up to 10 % of lightness); 6 – abundance of vascular 123 epiphytes (except Orchidaceae); 7 – abundance of fig trees (Ficus ssp, Moraceae); 8 – 124 abundance of Orchidaceae; 9 – abundance of Heart-palm trees (Euterpe edulis, 125 Arecaceae); and, 10 – abundance of Peroba Rosa trees (Aspidosperma polyneuron, 126 Apocynaceae). The selection of these ecological variables was based on ease of 127 observation in the field and ability to reflect vegetation structure. The ratings of the 10 128 variables were added together to give the REA values for the assessment. As each 129 variable rating ranged from 1 to 5, the possible REA rating is between 10 and 50. REA 130 scores ≤ 20 indicate very low integrity, ≥ 20 and ≤ 30 indicate low integrity, ≥ 30 and ≤ 131 40 indicate regular integrity, ≥ 40 and ≤ 45 indicate good integrity and ≥ 45 indicate 132 excellent integrity. M an us cr ip t 121 The REA was applied to transects which were located in the same area of bird 134 censuses, equidistant from fragment borders and from other transects, and avoiding the 135 first 100 m from the edge in any direction. Each transect was 100 m long, 30 m wide 136 and contained three assessment points, 30 m apart. The number of transects in each 137 forest fragment varied according to patch size. Eight transects were surveyed in forest 138 fragments larger than 500 ha, six in those of 100 to 500 ha, and four in patches of less 139 than 100 ha. In the IP, eight transects were sampled in a pristine area, avoiding edges in 140 all directions (at least 1 km from any edge). The REA was performed from December 141 2011 to March 2012 in the forest fragments and in January 2013 for the IP. All 142 assessments were performed in the summer to avoid seasonal variations. 143 Bird survey Ac ce p te d 133 144 To characterize the birds, we used the IBI that was previously proposed by Anjos 145 et al. (2009). The IBI is based on the recorded sensitivity of bird species to forest Page 6 of 28 fragmentation in each studied site. We accomplished this using the levels of sensitivity 147 to forest fragmentation presented in Anjos (2006) and Anjos et al. (2011) and followed 148 the procedure presented in Anjos et al. (2009). Three levels of sensitivity were 149 considered: low; intermediate; and, high. Species with low or intermediate sensitivity 150 are those presented in Anjos (2006), and the high sensitivity follow the suggestion of 151 Anjos et al. (2011); the criteria to allocate a given bird species to its sensitivity level 152 was based on their occurrence and relative abundance in forest fragments. In all cases, 153 the species belongs to the same study region. We selected 10 species in each level of 154 sensitivity (total of 30 species; see Appendix A), choosing those most likely to be 155 recorded (i.e., with high detectability) during four consecutive days of survey, following 156 the recommendations of Bochio & Anjos (2012). We replaced Pteroglossus castanotis 157 by Pteroglossus aracari because the border of the former’s range lies adjacent to the IP 158 and, therefore, the population size of P. castanotis is expected to be very low in that 159 region. te d M an us cr ip t 146 To obtain the IBI values, we used the numbers of species recorded in each 161 sensitivity group multiplied by their respective weights. Three different weights were 162 used according to the level of sensitivity of bird species. The highest weight (3) was 163 attributed to bird species grouped with high sensitivity, while the species grouped with 164 medium sensitivity and low sensitivity received the weights (2) and (1) respectively. 165 Thereafter, the sums of the three multiplied values were divided by 60 so that the IBI 166 scores range from 0 to 1 (see Anjos et al., 2009). Ac ce p 160 167 Then, to obtain a bird species list for each studied site, we performed bird surveys 168 on four consecutive days along a 1000 m transect that covered a standard area of 169 approximately 60 hectares. Surveys began at sunrise and finished four hours later, 170 totaling 16h of field observations per study site. Bird sampling was performed during Page 7 of 28 the breeding season in all the selected fragments from October 2010 to January 2011 172 and from October 2011 to December 2011. In the IP, sampling was performed in 173 January 2013. Transects were walked with a constant speed and we only recorded the 174 bird species heard or seen (not their individual numbers) at any distance from the 175 transect. 176 The integrated index (IBIint) cr ip t 171 To calculate the IBIint, different weights were used according to the sensitivity 178 levels of bird species and vegetation structure. We gave the number of high sensitivity 179 species and the REA value a weight of 3, by which we multiplied both values in the 180 formula. The number of bird species with medium sensitivity was multiplied by 2 181 (weight 2) while the number of bird species with low sensitivity was multiplied by 1 182 (weight 1). We varied the weight of REA in the index to ascertain the most appropriated 183 weight (1, 2 or 3), and only the IBIint based on REA with weight 3 was fully explained 184 by landscape metrics. Therefore, we opted to use weight 3 for the final version of the 185 index. Finally, the sum of the four multiplied values (three related to sensitivity level of 186 bird species and one related to REA values) was divided by 210, the sum of the 187 maximum value obtained by each of the four multiplied values as expressed by the 188 equation: 190 an M d te Ac ce p 189 us 177 Where HS is the number of birds species with high sensitivity recorded within a 191 given forest fragment, while MS and LS represent the number of bird species with 192 medium and low sensitivity, respectively. In each of the three bird sensitivity categories 193 the number of species recorded can vary from 0 to 10. The numbers 1, 2 and 3 represent 194 the multiplied weights of the bird sensitivity categories as well as of REA. Page 8 of 28 The IBIint scores ranged from 0 to 1, and were classified as follow: values ≤ 0.25 196 indicate very low ecological integrity; values ≥ 0.26 and ≤ 0.50 indicate low integrity; 197 values ≥ 0.51 and ≤ 0.75 indicate intermediate ecological integrity; and values > 0.75 198 indicate high ecological integrity. 199 Landscape variables ip t 195 Landscape metrics were calculated using a binary forest vs. matrix map as input, 201 which was obtained from SOS Mata Atlântica (2008) and used by Ribeiro et al., (2009). 202 Forest cover and functional connectivity (Metzger et al., 2009; Martensen et al., 2012) 203 were estimated for search radii of 250, 500, 1000 and 1500 meters around a centroid 204 defined within the central area of the patches. Previous studies have indicated that these 205 radii are suitable to encompass the home range sizes of most understory birds (Develey 206 & Metzger, 2006; Boscolo & Metzger, 2009; 2011). Additionally, we used these four 207 scales to simulate species with varying dispersal ability. Functional connectivity, a 208 measure of landscape configuration, was estimated by a proximity index (Uezu et al., 209 2008). This index is based on the size and proximity of all forest fragments situated 210 within a predetermined search radius of the focal patch (Gustafson & Parker, 1994). For 211 the IP reference site, we assumed 100% of forest cover and a proximity index higher 212 than the highest value among forest fragments at all spatial scales. Area and proximity 213 indexes were log-transformed in order to allow linear regression analysis. Landscape 214 metrics were calculated using ArcGIS (ESRI, 2005) and V-LATE extension (LARG, 215 2005). 216 Data analysis Ac ce p te d M an us cr 200 217 A model selection approach was used to estimate the relative contribution of the 218 various competing, ecologically meaningful models (Burnham & Anderson, 2002). To 219 fit the models, we used Beta Regressions that are useful for models which the dependent Page 9 of 28 variable is continuous and restricted to the standard unit interval (0, 1) (Ferrari & 221 Cribari-Neto, 2004). Beta regression naturally incorporates features such as 222 heteroskedasticity or skewness which are commonly observed in data such as rates or 223 proportions (Cribari-Neto & Zeileis, 2010). Furthermore, similarly to generalized linear 224 models (GLM), the inference of Beta regression is based on likelihood (Ibáñez et al., 225 2011). cr ip t 220 For each dependent variable (IBIint, REA and IBI), we analyzed three competing 227 models and a null model (Table 2). We performed a preliminary analysis on forest cover 228 to determine the best spatial scale (250, 500, 1000 or 1500 m) to explain the response 229 variable, which we then compared with other explanatory variables. The same was done 230 for connectivity. In our case, the null model represents the absence of effect (Martensen 231 et al., 2012). M an us 226 Table 2. Competing models used to identify the 233 most accurate index to infer the ecological 234 integrity of Atlantic Forest hotspot fragments, 235 Brazil. Connectivity was based on Proximity 236 Index. Forest cover and connectivity were 237 calculated in four spatial scales (250, 500, 1000, te Ac ce p 238 d 232 1500 meters) Models Number of parameters IBIint = Area 3 IBIint = Forest cover 3 IBIint = Connectivity 3 IBIint = null 3 REA = Area 3 REA = Forest cover 3 REA = Connectivity 3 REA = null 3 IBI = Area 3 IBI = Forest cover 3 IBI = Connectivity 3 IBI = null 3 239 Page 10 of 28 The best models were identified based on the Akaike Information Criterion (AIC; 241 Burnham & Anderson, 1998) with the small sample correction (AICc; Hurvich & Tsai, 242 1989). We also used the AICc weight (wAICc), which represents the weight of evidence 243 in favor of a given model being the best one (Burnham & Anderson, 2002). To further 244 guide model selection within the best concurrent models, we used the AICc, which is 245 the difference between the AICc of a considered model and the model with the lowest 246 AICc value (Martensen et al., 2008). Thus, models with wAICc ≥ 0.10 and AICc ≤ 2.5 247 were considered as equally plausible to explain the dependent variables. All analyses 248 were performed using R version 3.0.2 (R Development Core Team, 213). The “betareg” 249 package (Cribari-Neto & Zeileis, 2010) was used to fit beta regression models by using 250 the betareg function. M an us cr ip t 240 Since REA and IBI have already been biologically evaluated, we considered the 252 index with greater accuracy, which was explained by the larger number of explanatory 253 variables. te 254 d 251 Results 256 Ecological integrity of forest fragments 257 Ac ce p 255 As we expected, the IP (reference area) obtained the highest IBIint values, 258 followed by the forest fragment MG. Among the 10 forest fragments, IBIint values 259 ranged from 0.39 to 0.95 (Table 3). 260 261 262 263 264 Page 11 of 28 Table 3. Estimated ecological integrity based on IBIint, REA and IBI values for the 266 10 forest fragments and the continuous area (reference area) within fragmented 267 landscapes of Atlantic Forest hotspot. The scores of IBIint and IBI ranged from 0 to 268 1, while the REA score ranged from 10 to 50. IBIint and IBI were classified as low, 269 intermediate and high ecological integrity, REA were classified as low, regular, 270 good and excellent integrity. Integrity REA Integrity IBI Integrity 0.95 High 48 Excellent 0.93 High MG 0.84 High 44 Good 0.75 High MF 0.62 Intermediate 33 Regular 0.51 Intermediate AT 0.50 Low 30 Low 0.23 EI 0.39 Low 23 Low 0.20 PI 0.47 Low 29 Low PQ 0.59 Intermediate 28 Low CO 0.58 Intermediate 27 Low BU 0.78 High 44 DO 0.51 Intermediate 30 SH 0.70 Intermediate 35 272 Low 0.20 Low 0.67 Intermediate 0.57 Intermediate Good 0.52 Intermediate Low 0.28 Low Regular 0.70 High M 271 Low us IP cr IBIint a an FF ip t 265 Index models According to the best models, the IBIint was the more accurate index once it was 274 explained by a larger number of explanatory variables than REA and IBI at all four 275 spatial scales. IBIint was explained by all explanatory variables at 500 and 1000 meters 276 and by forest cover and connectivity at 250 and 1500 meters, while REA and IBI were 277 explained only by connectivity and patch size, respectively, at all spatial scales. We then 278 used the radius of 1000 meters to represent the general results that were similar among 279 the four scales (Table 4; Fig. 2). te Ac ce p 280 d 273 281 282 283 284 285 Page 12 of 28 Table 4. Best models to explain the dependent variables (Dep. Var.): REA, IBI and 287 their derived index IBIint. Pseudo R² indicates the goodness-of-fit of the estimated 288 models. AICc = Akaike Information Criterion with the small sample correction, AICc 289 = Delta value of AICc and wAICc = weight of evidence of the models. Connectivity 290 was based on proximity index. Lines highlighted in gray indicate the plausible models. 296 AICc IBIint IBIint IBIint IBIint REA REA REA REA IBI IBI IBI IBI 1 2 3 4 1 2 3 4 1 2 3 4 IBIint = Forest cover** IBIint = Connectivity** IBIint = Area** IBIint = Null REA = Connectivity ** REA = Forest cover REA = Area REA = Null IBI = Area** IBI = Forest cover IBI = Connectivity IBI = Null 0.69 0.68 0.62 0.06 0.75 0.64 0.46 0.17 0.64 0.46 0.37 0.00 0 0.2 2.3 10.3 0 4.8 8.9 13.9 0 3.7 5.9 11.6 us an M wAICc 0.45 0.41 0.14 <0.001 0.90 0.08 0.01 <0.001 0.82 0.12 0.04 0.002 cr Pseudo R² ** indicates the equally plausible models based on AICc and wAICc values. For each index, models are ranked from best to worst according, to AICc and wAICc. d 295 Model te 294 Rank Ac ce p 291 292 293 Dep. var. ip t 286 Page 13 of 28 ip t cr us an M d te Ac ce p 297 298 Fig. 2 Best supported models of the relation between the indexes and the three landscape explanatory 299 variables: area of forest fragments (ha), connectivity (proximity index) and forest cover (%). (A), (B) and 300 (C) the best models for IBIint (plants and birds), (D) the best model for REA (only plants) and (E) the 301 best model for IBI (only birds). wAICc = weight of evidence of the model. 302 303 304 305 Page 14 of 28 306 Discussion IBIint was the more accurate index once it was explained by all landscape 308 variables, while REA and IBI were explained only by connectivity and fragment size, 309 respectively. In fact, habitat amount and landscape connectivity regulate important 310 multiscale ecological processes and patterns that were best reflected by IBIint. For 311 example, the habitat amount and connectivity are tightly related to colonization 312 dynamics, population size, local extinction and rescue effects (Brown & Kodrick- 313 Brown, 1977; Ricketts, 2001; Fahrig, 2003). Large fragments support large populations 314 which are less prone to local extinction (Temple & Cary, 1988; Jules, 1998). an us cr ip t 307 REA results indicate that vegetation integrity increases with increasing 316 connectivity regardless of patch size and forest cover context. Connectivity may 317 increase vegetation resilience by enhancing seed dispersal and recolonization 318 opportunities for species with high and low dispersal capacities. Indeed, greater 319 proximity between habitat patches decreases the distances from propagule sources to 320 colonization targets (Lavorel, 1999; Cayuela et al., 2006; Minor et al., 2009). 321 Furthermore, these findings can be associated with anthropic impacts that do not depend 322 on landscape features and patch size. For instance, MF forest fragment (the largest 323 fragment with 876 ha), suffered large fires and intense logging in 1970s and 1980s and 324 achieved a lower value of REA than smaller fragments such as BU (288 ha) and SH (85 325 ha) which suffered fewer human disturbances. Therefore, the vegetation structure seems 326 to be more strongly moderated by the synergy between historical human impacts and 327 the current spatial arrangement of habitats than by habitat amount and patch size. Ac ce p te d M 315 328 IBI had correlation with fragment size, probably due to amount and quality of 329 food availability for birds. Uezu et al. (2005) noted that the abundance of frugivorous 330 bird species are affected more by fragment size than by connectivity, and the opposite is Page 15 of 28 true for the insectivores. Martensen et al. (2008) demonstrated that both fragment size 332 and connectivity influenced differently functional groups of birds. In our 30 bird species 333 selection for IBI, we included both frugivorous and insectivorous birds. But it is 334 possible that another combination of species could indicate correlation with both 335 fragment size and connectivity. In our study, however, a balance between metrics 336 related to connectivity (REA) and fragment size (IBI) should improve balance for the 337 IBIint. cr ip t 331 Here, we found that a multi-taxa approach improves the accuracy of rapid 339 assessment methods at identifying priority areas for biodiversity conservation in 340 fragmented landscapes such as our study region. Rapid assessment methods cannot, and 341 do not intend to replace long-term studies and scientific inventories, but they can be 342 useful tools to extend the geographical application of those studies and inventories with 343 a considerable reduction in time and cost (Abate, 1992; Sutula et al., 2006; Medeiros & 344 Torezan, 2013; Álvarez-Berastegui et al., 2014). These methods are especially relevant 345 in tropical regions such as the Brazilian Atlantic Forest, which has undergone a huge 346 habitat loss and is considered one of the most threatened biodiversity hotspots in the 347 world (Myers et al., 2000). Recent estimative reports that only 11.4% to 16% of the 348 Atlantic forest’s original cover remains, and are mainly distributed in isolated and small 349 fragments < 50 ha (Ribeiro et al., 2009). In this fast and wide habitat loss scenario, rapid 350 assessment methods based on multi-taxa indicators can be excellent tools to aid 351 conservationists and managers in defining conservation strategies at local and landscape 352 scales. Ac ce p te d M an us 338 353 354 355 Page 16 of 28 Acknowledgements 357 We are grateful to Odair C. Pavão for field assistance, Calebe P. Mendes and Juliana 358 Silveira dos Santos for statistical and GIS support. We thank the the government 359 agencies of environment protection (ICMbio and IAP) and the owners of the private 360 lands where some sites are located for permission to conduct our studies. We also thank 361 the editors and the two anonymous referees who helped us to improve substantially the 362 manuscript. CNPq (Brazilian Government Research Council) provided research grants 363 for Luiz dos Anjos (302694/2010-2), Milton Cezar Ribeiro (312045/2013-1), José 364 Marcelo D. Torezan (305854/2012-7) and a technical grant for O. C. Pavão 365 (503836/2010-9). Gabriela Menezes Bochio received a research grant from CAPES 366 (Coordination for the Improvement of Higher Education Personnel). MCR also thanks 367 the financial support by FAPESP (process 2013/50421-2). M an us cr ip t 356 d 368 References 370 Abate, T. (1992). Environmental Rapid-Assessment Programs Have Appeal and Critics. Ac ce p 371 te 369 BioScience,42, 486-489. 372 Allen, C. D. (2009). 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The 559 species are arranged according to their levels of sensitivity to forest fragmentation. us cr 557 an High Sensitivity Tinamus solitarius; Micrastur semitorquatus; Triclaria malachitacea; Pionopsitta M pileata; Trogon rufus; Pteroglossus castanotis; Sirystes sibilator; Trichothraupis te Medium Sensitivity d melanops; Automolus leucophthalmus; Grallaria varia Aratinga leucophthalma; Trogon surrucura; Baryphthengus ruficapillus; Pyrrhura Ac ce p frontalis; Selenidera maculirostris; Melanerpes flavifrons; Cacicus haemorrhous; Sittasomus griseicapillus; Scytalopus indigoticus; Chiroxiphia caudata Low Sensitivity Crypturellus obsoletus; Pionus maximiliani; Dysithamnus mentalis; Pyriglena leucoptera; Leptopogon amaurocephalus; Piaya cayana; Cyclarhis gujanensis; Dendrocolaptes platyrostris; Basileuterus culicivorus; Basileuterus leucoblepharus. 560 561 Page 26 of 28 Figure (D) (B) an us cr (A) (E) ip t (C) (F) Ac ce pt ed M Fig 1. Location of studied sites. (A) Location of Paraná State in southern Brazil. (B) Ecosystem domains of Paraná State (gray scale) SF domain highlighted in white; the square 1 indicates the location of IP - reference area and the square 2 indicates the location of the set of forest fragments. (C), (D), (E) and (F) are landscape sectors where the 10 studied forest fragments (highlighted in black) are situated. Page 27 of 28 ip t cr us an M ed ce pt Ac Fig. 2 Best supported models of the relation between the indexes and the three landscape explanatory variables: area of forest fragments (ha), connectivity (proximity index) and forest cover (%). (A), (B) and (C) the best models for IBIint (plants and birds), (D) the best model for REA (only plants) and (E) the best model for IBI (only birds). wAICc = weight of evidence of the model. Page 28 of 28