Modeling Distribution of Phoneutria Bahiensis (Araneae: Ctenidae): an Endemic and Threatened Spider from Brazil

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ZOOLOGIA 28 (4): 432–439, August, 2011 doi: 10.1590/S1984-46702011000400004 Modeling distribution of Phoneutria bahiensis (Araneae: Ctenidae): an endemic and threatened spider from Brazil

Marcelo A. Dias1,2; Miguel Simó1,4; Ismael Castellano1 & Antonio D. Brescovit3

1 Sección Entomología, Facultad de Ciencias, Universidad de la República. Iguá 4225, CP 11400, Montevideo, Uruguay. 2 Centro de Ecologia e Conservação Animal, Universidade Católica de Salvador. Avenida Pinto de Aguiar 2589, Pituaçu, 41740-090 Salvador, BA, Brazil. 3 Laboratório de Artrópodes, Instituto Butantan. Avenida Vital Brasil 1500, 05503-900 São Paulo, SP, Brazil. 4 Corresponding author. E-mail: [email protected]

ABSTRACT. Phoneutria bahiensis Simó & Brescovit, 2001 is a large ctenid spider inhabiting the states of Bahia and Espírito Santo, Brazil. Considering that it is probably endemic, this species was included in the Brazilian red book of threatened species. Here, we predict the distribution range of P. bahiensis using 19 bioclimatic variables in the model design. The most septentrional record for this spider was indicated for northern Bahia. The model predicts that the distribution range covers the Atlantic Forest from the state of Paraíba to Rio de Janeiro, with the best suitable area in the Atlantic Forest of the state of Bahia. The bioclimatic variable with the best contribution to the model was precipitation in the driest quarter. Based on collected data, the species inhabits Ombrophilous Forests and Restinga vegetation, two ecosystems of the Atlantic Forest biome. In the best-predicted area of distribution, eleven Conservation Units were included. This information could be considered for future conservation plans of this species. KEY WORDS. Atlantic Forest; niche modeling; bioclimatic variables; spider distribution.

Species distribution models (SDMs) have been used to ance of a species, it is possible to predict the potential distribu- predict the potential distribution of living organisms, linking tion area for that species when considering all field sites with records or species abundance with environmental constrains similar climate conditions (ACOSTA 2008). Furthermore, these or spatial characteristics (GUISAN & ZIMMERMANN 2000, ELITH & models can be used for species with few geographic records LEATHWICK 2009). The models can be used to provide under- and scarce information on natural history (GUISAN & standing and generate predictions about species distributions ZIMMERMANN2000, BRITO et al. 2009, BAASCH et al. 2010) and are across a landscape. It has been successfully applied to terres- therefore very important when making decisions regarding the trial, freshwater and marine organisms in studies investigating conservation of threatened species and preservation of the conservation and management of species, conservation of biodiversity (BROOKS et al. 1999). areas with high biodiversity, and studies on biogeography, in- Although the evaluation of climate constraints do not take vasive species and global climate change (CARROLL 2010, GALLIEN into consideration other variables that may restrict geographic et al. 2010, FRANKLIN 2010, BENITO et al. 2009, DE MAS et al. 2009, distributions, for instance habitat accessibility, vegetation, com- ELITH & LEATHWICK 2009, FERRIER et al. 2002, GUISAN & ZIMMERMANN petition, predation, parasitism, dispersal and population genet- 2000). The great demand for these kinds of ecological studies ics, the limitations to the distribution of many species are often has promoted the development of several SDMs computer-based significantly associated with climate (PARMESAN 2006, SEXTON et programs using different algorithms that search for accuracy al. 2009). Moreover, climate constraints represent one of the of a given model and the best prediction power (FIELDING & BELL most consistent predictors for the successful establishment of 1997). While knowledge on the distributional ranges of spe- diverse taxa of plants and poikilothermic animals (ANDREWARTHA cies is clearly needed, the ranges of most species either remain & BIRCH 1984, WOODWARD 1987, VINK et al. 2011). largely unknown or are incomplete, and most of the available Sample size has been an important limitation for the studies are based on records from museum specimens, which performance evaluation of distribution models of species. The may be biased (STOCKMAN et al. 2006). accuracy of the general model increases as the sample size be- Regarding environmental constrains, SDMs based on cli- comes larger (WISZ et al. 2008). Studies based on small samples mate variables are considered useful tools for proposing crite- bring a greater degree of uncertainty to the estimation of pa- ria to select strategic areas for the conservation of species (ACOSTA rameters and consider fewer biological factors that may influ- 2008, RUBIO et al. 2010). Based on the expected climate toler- ence the relationships between species and their environment

(CILIBERTI et al. 2009). Still, relatively few records may be enough The distribution patterns of Phoneutria spp. have not been to characterize the distribution of species with narrow envi- studied. Similarly, the factors which have restricted their dis- ronmental tolerances (KADMON et al. 2003). In this sense, good tributional range have not been investigated. As demonstrated predictions were obtained with modeling algorithms based on in previous studies on spiders and other invertebrates, tem- few geographic records and scarce information on natural his- perature and humidity are likely the two most important vari- tory (PEARSON et al. 2007, BRITO et al. 2009, KUMAR & STOHLGREN ables that constrain ecological niches. Taking into account the 2009, BAASCH et al. 2010). These models have been applied suc- important qualities of the tools used by SMDs in several stud- cessfully in the design of conservation plans for rare species ies, and the scarce application of this methodology in spiders, (GUISAN et al. 2006). the main objectives of this study were to predict the geographi- Published studies on the distribution of species of spi- cal distribution of P. bahiensis based on climatic variables and ders using SDMs are scarce. STOCKMAN et al. (2006) used the pro- to analyze its potential occurrence in Conservation Units in gram GARP to predict the spatial distribution of three species Brazil, an important contribution to the future management of trapdoor spiders of Promyrmekiaphila Schenkel, 1950 plans for this species. (Cyrtaucheniidae) in California, USA. They used several types of variables including soil, temperature, altitude, precipitation MATERIAL AND METHODS and vegetation. Studies based only upon climatic variables have also been successfully applied to spiders. VINK et al. (2011) stud- Occurrence data ied the current and world potential distribution and impacts Twenty-three records of P. bahiensis were analyzed: 22 of the invasive species Latrodectus hasseltii Thorell, 1870 (The- from the state of Bahia and one from the state of Espírito Santo. ridiidae) using the program CLIMEX. RUBIO et al. (2010) ana- The records were obtained from the literature (SIMÓ & BRESCOVIT lyzed the potential distribution range of Dubiaranea difficilis 2001, MARTINS & BERTANI 2007, BRAZIL et al. 2009) and from re- (Mello-Leitão, 1944) (Linyphiidae) in Argentina based on rela- cently collected specimens (by M.A.D.) in Mata de São João, in tively few locality records using Maxent. the Northern coast of the state of Bahia, Brazil. The specimens The Neotropical Phoneutria Perty, 1833 includes eight were deposited in the following institutions: Instituto Butantan, species of large wandering spiders which are considered to be São Paulo, São Paulo (IBSP), Universidade Federal of Bahia, Sal- of medical importance due to the neurotoxic effects of their vador, Bahia (UFBA) and Centro de Ecologia e Conservação venom (SIMÓ & BRESCOVIT 2001, MARTINS & BERTANI 2007). The Animal of the Universidade Católica do Salvador, Bahia (ECOA), genus is distributed from Central America to the northern all in Brazil. Argentina (SIMÓ & BRESCOVIT 2001, PLATNICK 2010) in three main Records areas: one from Central America to northeastern South-America, BRASIL, Bahia: Ilhéus 14°47’51”S, 39°02’13”W, 1 male (IBSP through the Andes, which includes Colombia, Ecuador, Peru, 19027); 1 female (IBSP 19040); 1 male (IBSP 19344); 1 female and Bolivia; a second area, situated in the Amazon region; and (IBSP 24057); 1 female (IBSP 22076); 1 female (UFBA/LAP-A a third covering northeastern and southern Brazil, northern 172); Ilhéus, 14°47’20”S, 39°02’58”W, 1 male (UFBA/LAP-A Argentina and Paraguay (MARTINS & BERTANI 2007). 173); 1 female (UFBA/LAP-A 1317); Itapebi 15°57’S, 39°32’W, 1 Phoneutria bahiensis Simó & Brescovit, 2001 was described male (IBSP 58193); 2 males 2 females (ECOA 00709); Porto from specimens collected from different sites of the Atlantic Seguro 16°26’59”S, 39°03’53”W, 1 female (IBSP 9516); Una, Forest biome in the state of Bahia, northeastern Brazil. DIAS et Reserva Biológica de Una, 15°16’20.08”S, 39°03’54.88”W, 1 fe- al. (2005a) and BRAZIL et al. (2009) reported new field records male (IBSP 45022); 1 male (IBSP 45023); 1 male (IBSP 45024); from Bahia. MARTINS & BERTANI (2007) indicated the southern- Lomanto Júnior, 14°81’S, 39°47’W, 1 female (IBSP 2427). Sal- most record in the state of Espírito Santo. Compared to conge- vador: 12°58’S, 38°30’W, 1 male (IBSP 58194); Parque ners, P. bahiensis has the most restricted distribution and is Zoobotânico (13°00’31”S, 38°30’15”W), 1 female (UFBA/LAP- represented by relatively few specimens in arachnological col- A 1098); Parque Metropolitano da Lagoa e Dunas do Abaeté lections. Owing to the few literature records for this species (12°56’41”S, 38°21’22”W) 1 female (UFBA/LAP-A 872). Espírito (DIAS et al. 2005a), the knowledge about the natural history of Santo: Linhares, Reserva Particular do Patrimônio Natural P. bahiensis is scarce. This spider has nocturnal activity and in- Recanto das Antas, 19°23’S, 40°04’W, 1 female (IBSP 7585). habits shrubs, litter and bromeliads (MACHADO et al. 2008). Its New record: Bahia: Mata de São João, Praia do Forte, 12° restricted geographic distribution suggests that this spider is 33’S, 38°59’W, 1 female (ECOA 01061); 1 female (ECOA 01062); endemic to only a part of the Atlantic Forest biome. This char- 1 male (ECOA 01063); (12°52’S, 38°29’W), 1 female (ECOA acteristic has promoted the inclusion of P. bahiensis in the Bra- 01064). Three females were collected with pitfall traps and one zilian Environmental Ministry threatened species Red List male was captured by diurnal hand collection, during the fauna (MMA 2008). However, the scarce data on the geographic dis- rescue program before the construction of a hotel complex, in tribution and biology of P. bahiensis is a rather weak support Praia do Forte, Mata de São João city, at the Northern coast of for its conservation status. Bahia.

ZOOLOGIA 28 (4): 432–439, August, 2011 434 M. A. Dias et al.

Climatic variables RESULTS We used the climatic database Wordclim 2.5 arc minutes with a resolution of 4.4 km (HIJMANS et al. 2005a) that com- Bioclimatic analysis and habitat prises nineteen different bioclimatic variables related to tem- Table I summarizes the bioclimatic tolerance range of P. perature and precipitation, from 1950 to 2000. These variables bahiensis, where only four bioclimatic variables influenced the can be used for mapping and spatial modeling with GIS pro- model distribution. The variable with the highest contribution grams, since they are potentially useful to represent the eco- to the predicted range shape was “precipitation in the driest physiological tolerances of a species in the habitat (GRAHAM & quarter” (BIO 17, 80,1%), which occurs in the study area be- HIJMANS 2006). The dataset was analyzed with the geographic tween August and October (end of Winter and beginning of information system Diva Gis 7.1.7 (HIJMANS et al. 2005b). Spring). The other three variables were related to temperature, Modeling procedure but with lower contribution to the model. The “mean monthly The model was elaborated with Maxent, a maximum temperature range” (BIO 2) in the predicted core area was be- entropy based program that estimates the probability of a spe- tween 21 and 29.4°C. The species lives within an area where cies occurrence based on the actual presence points and de- seasonality is not very marked, mainly with warm climate and with an annual mean temperature (BIO 1) that reaches 25.2°C fined environmental constraints (PHILLIPS et al. 2006). The output of Maxent model is a continuous map and a threshold must be (INMET 2009). Considering the collection data, this species lives set in order to determine the probability of a species’ presence. in Restinga and Ombrophilous Forest, two Atlantic Forest eco- To estimate the major incidence variables within the model we systems (RIZZINI 1997). No records were from other areas with carried out a jackknife analysis to measure the weight of each high anthropic influence such as urban environments or variable. This method evaluates the importance of each vari- agroecosystems or activities related with transportation. able when compared to all other variables. Maxent has been used in the prediction and mapping of Table I. Contribution of the 19 bioclimatic variables to the potential distribution areas for threatened species, where moni- distribution model of P. ba hie ns is . (PC) Percentage of contribution. toring and restoration of native population in the natural habi- Bioclimatic variables PC tat are necessary (GASTON 1996). Furthermore, this program works well with small sample sizes (HERNÁNDEZ et al. 2006, PEARSON BIO 17 Precipitation of driest quarter 80.1 et al. 2007, BENITO et al. 2009, KUMAR & STOHLGREN 2009). BIO 2 Mean monthly temperature range 14.2 The program was used applying the “equal training sen- BIO 1 Annual mean temperature 3.0 sitivity plus specificity” threshold rule (LIU et al. 2005) and the BIO 3 Isothermality 2.7 following settings: 1000 maximum interactions, random test BIO 4 Temperature seasonality 0.0 percentage of 25%, logistic output formatted, remove dupli- BIO 5 Maximum temperature of warmest month 0.0 cates from the same gridcell. The total area modeled was situ- BIO 6 Minimum temperature of coldest month 0.0 ated between the following coordinates: latitude 2°48’19.46”S BIO 7 Temperature anual range 0.0 to 23°18’12.89”S, longitude 32°20’41.51”W to 44°32’03.42”W. Mapping was obtained by importing the model from the geo- BIO 8 Mean temperature of wettest quarter 0.0 graphic information system DIVA-GIS, version 7.1.7 (HIJMANS BIO 9 Mean temperature of driest quarter 0.0 et al. 2005a). We selected the option auto features that allow BIO 10 Mean temperature of warmest quarter 0.0 automatic limiting of feature types for small sample sizes. BIO 11 Mean temperature of coldest quarter 0.0 For model evaluation we performed the Maxent function BIO 12 Annual precipitation 0.0 Receiver Operating Characteristic (ROC) (HANLEY & MCNEIL 1982), BIO 13 Precipitation of wettest month 0.0 frequently used in the evaluation of distribution models based BIO 14 Precipitation of driest month 0.0 in presence-absence algorithms (BENITO DE PANDO & PEÑAS DE GILES BIO 15 Precipitation seasonality 0.0 2007). This analysis considers the entire sample (training data BIO 16 Precipitation of wettest quarter 0.0 as the 75% of the sample) and the 25% of the random sample (test data). The area under the receive operating characteristic BIO 18 Precipitation of warmest quarter 0.0 curve (AUC) is an unbiased measure of prediction accuracy cal- BIO 19 Precipitation of coldest quarter 0.0 culated from the ROC and represents the average sensitivity over all possible specificities, where AUC values close to 1 indicate the best discrimination (ZHONGLIN et al. 2009). Another evalua- Distribution climatic model tion of the model was performed in Diva Gis by calculating the The predicted area of distribution for P. bahiensis indi- AUC in a ROC plot. This analysis was based on 70% of the origi- cated by the model covered the Atlantic Rainforest biome be- nal points that were randomly re-sampled as training data to tween states of Paraíba (07°06’41.47”S, 34°51’48.08”W) and Rio perform 20 iterations of the model (ACOSTA 2008, RUBIO et al. 2010). de Janeiro (22°45’42.01”S, 42°54’16.38”W). In the distribution

ZOOLOGIA 28 (4): 432–439, August, 2011 Modeling distribution of Phoneutria bahiensis: an endemic and threatened spider from Brazil 435 model, the presence was set over the 0.227 logistic thresholds, Figure 2 shows that P. bahiensis was previously collected in and the algorithm converged after 100 iterations. The AUC test four Conservation Units from the state of Bahia: Parque for training Data performed in Maxent was 0,971. The AUC Metropolitano da Lagoa e Dunas do Abaeté and Parque values obtained in DIVA-GIS after 20 iterations fluctuated from Zoobotânico in Salvador, Área de Proteção Ambiental do Litoral 0.934 to 0.980 (mean 0.9684). Norte, Mata de São João, and Reserva Biológica de Una. Based on The highest suitability occurrence was concentrated be- the predicted model, six Conservation Units are included in the tween two sites in the state of Bahia, on the North, the city of most suitable distribution areas for the species: Parque Salvador, and on the South, the Reserva Biológica de Una, be- Metroplitano de Pituaçu and Parque Joventino Silva in Salvador; tween 13 degrees and 15 degrees South of latitude and between Reserva da Vida Silvestre de Una, Parque Estadual Serra do 38 degrees and 39 degrees West of longitude along 260 km of Conduru, Reserva Extrativista de Canavieiras and Parque Nacional the Atlantic Rainforest (Fig. 1). Salvador has Ombrophilous For- Marinho de Abrolhos in southern Bahia. In Espírito Santo, the ests and Restinga vegetation, while Una has only the former. species was collected in Linhares, and the record was included in Other potential areas with good predictions of distribution were the Reserva Particular do Patrimônio Natural Recanto das Antas. located on Restinga vegetation. They are situated to the South of the state of Bahia, in the islands of the Parque Nacional Marinho de Abrolhos (17°54’19.94”S, 38°52’19.92”W) and the city of Caravelas (17°42’47.69”S, 39°14’53.70”W) and to the North, on the coastal region of the state of Sergipe, near Aracajú (10°54’34.25”S, 37°04’29.31”W) and the metropolitan region of Recife (state of Pernambuco) (08°03’14”S, 34°52’51”W). The predicted area in Espírito Santo was characterized by the Ombrophilous Forest. Considering the climatic variables, the unsuitable areas appear to be those outside of the coastal region and towards the center of the state, which present different climatic conditions in comparison with the Atlantic Rainforest, as well other types of vegetation such as Caatinga or Cerrado.

Figure 2. Conservation Units in the predicted distribution range of Phoneutria bahiensis. Bahia: (A) Área de Proteção Ambiental do Litoral Norte; (B) Fragmentos Florestais de Salvador: Parque Metropolitano da Lagoa e Dunas do Abaeté, Parque Metropolitano de Pituaçu, Parque Zoobotânico de Salvador; (C) Parque Estadual Serra do Conduru; (D) Reserva da Vida Silvestre de Una; (E) Reserva Biológica de Una; (F) Reserva Extrativista de Canavieiras; (G)= Parque Nacional Marinho de Abrolhos. Espírito Santo: (H) Reserva Particular do Patrimônio Natural Recanto das Antas.

DISCUSSION

Figure 1. Potential predicted range of P. bahiensis in the Atlantic Phytosociology studies indicate that annual precipitation Forest biome, Eastern Brazil. Circles indicate the known records. has a direct effect on the primary productivity of ecosystems (BA) Bahia, (ES) Espírito Santo, (RJ) Rio de Janeiro, (SE) Sergipe, associated to the Atlantic Forest (LONDSDALE 1988, PIRES et al. 2006, (AL) Alagoas, (PE) Pernambuco, (PB) Paraíba. SCHEER et al. 2009). The countryside of the state of Bahia features

ZOOLOGIA 28 (4): 432–439, August, 2011 436 M. A. Dias et al. a total annual accumulated precipitation of 500 mm, contrast- The Atlantic forest is highly fragmented, and a large num- ing with the coast of the Atlantic Forest, where 1200 mm of rain ber of its endemic species are considered to be threatened with fall per year (MARENGO 2008). Furthermore, in the countryside of extinction mainly because more than 80% of the fragments Bahia the temperature ranges from 16° to 40°C (mean 28°C) in are less than 50 ha and almost 50% of the forest are highly the driest quarter, but on the coast the temperature fluctuates affected by anthropogenic activities (METZGER 2009). The At- between 21° and 30°C (mean 25.5°C) (INMET 2009). Regarding lantic Forest fragments in the city of Salvador are considered climate issues, the distribution of P. bahiensis appears to be af- important refuges for different kinds of organisms (RODRIGUES fected by precipitation patterns, because the climatic variable et al. 1993, BROWN JR & FREITAS 2003). Following the pattern of with highest contribution to the model was precipitation in the other regions of the Brazilian Atlantic coast, these fragments driest quarter. The association of this variable with the mean differ in size, conservation and human disturbance (PICKETT & monthly temperature range (BIO 2 = 14.2%) establishes a po- CADENASSO 2006). The adequate conservation actions for reduc- tentially smaller predicted area of higher occurrence for P. ing the negative effects of the loss of Atlantic forest and its bahiensis. These factors could explain the restricted distribution fragmentation emphasizes the need for having good ecologi- area of the species in the Atlantic Rainforest biome. This assump- cal indicators for the establishment of conservation manage- tion could be better confirmed with the inclusion of other types ment plans (METZGER 2009, PARDINI et al. 2009). Furthermore, of environmental variables in the SMD analysis. remnant size and connectivity are considered the most impor- The outcome values represented a good evaluation of the tant factors affecting the survival of species in fragmented land- model prediction and are consistent with comparative evalua- scapes, where groups with low dispersal capacity are particularly tions of arachnids made by ACOSTA (2008) and RUBIO et al. (2010). sensitive to the reduction of the fragmented area or to connec- In addition, considering the known records, the predicted range tion by thin corridors (METZGER et al. 2009). looks quite similar to the expected distribution, mainly related Modeling refuges are considered effective for predictions to the restricted distribution area and the endemism of P. of the distribution patterns of species and genetic endemism bahiensis in the Atlantic Forest biome. in Atlantic forest taxa (e.g., sloths, lizards, pitvipers, This study also extends the known distribution range of P. woodcreepers), as well as for low-dispersal Rainforest specialist bahiensis from the Northern coast of Bahia to the north of Espírito taxa elsewhere (GRAHAM et al. 2006). CARNAVAL & MORITZ (2008) Santo, in Brazil, between 12°33’04.75”S – 37°59’16.98”W and studied the modeling of Pleistocene Atlantic Rainforest and 19°23’00.67”S – 40°04’33.27”W. The habitats where this species proposed two important refuges situated in the states of Bahia lives have been drastically reduced and modified by human ac- and Pernambuco. They predicted that sampling efforts of the tivities during the last century. The Restinga has been mainly underexplored Bahia refuge will reveal high endemism at both modified by urbanization and tourism and the Ombrophilous species and generic levels. Phoneutria bahiensis could be con- Forest mostly by farming (NASCIMENTO 1993, AB’SABER 2003). The sidered part of the relic species group of living organisms from results obtained herein indicate that P. bahiensis is distributed in both Pleistocene refuges, supporting its condition as an en- a strategic conservation area of Atlantic Forest, and its range demic species. covers 11 Units designed for the conservation of animals, plants Considering our results, the expected distribution of P. and traditional communities. bahiensis is consistent with the known records and its range agrees Other known collecting surveys of spiders in the predicted with our predictions that this rare species is mostly restricted to distribution area of P. bahiensis were carried out in fragments the Atlantic Rainforest in northeastern Brazil. In Salvador, there of Atlantic Rainforest in Ilhéus and Reserva Biologica de Una are large remnants such as Parque Metropolitano da Lagoa e (DIAS 2004, DIAS et al. 2005b) and in Salvador (BENATI et al. 2010, Dunas do Abaeté (1800 ha) and Parque Metropolitano de Pituaçu PERES et al. 2010, MENDES CARVALHO et al. 2010). These field sur- (425 ha), which may have better conditions for the establish- veys generally studied soil and leaf litter spiders using pitfall ment of management plans to preserve endemic and threatened traps, Berlese funnel or Winkler extractor. PINTO-LEITE et al. (2008) species as P. bahiensis. sampled spiders in Mata de São João by beating on bushes and The importance of arthropods as indicators of small dis- by performing visual searches followed by hand collecting dur- turbance has been studied in the Brazilian Atlantic Rainforest ing the day. OLIVEIRA-ALVES et al. (2005) sampled spiders in Parque (UEHARA-PRADO et al. 2009, BRAGAGNOLO et al. 2007). Other spe- Metropolitano de Pituaçu, an Atlantic forest remnant in Salva- cies of the genus, for instance P. nigriventer (Keyserling, 1891) dor using nocturnal hand collection and foliage beating. Each and P. keyserlingi (F.O.P.-Cambridge, 1897) are both recorded of these sites has records of P. bahiensis, but those studies did from urban areas or habitats with human activities (SIMÓ & not record the species, probably due to their rarity or because BRESCOVIT 2001). Field observations indicate that P. bahiensis is of the sampling methods used. Ctenids such as P. bahiensis and not synanthropic and is very sensitive to human impact. This other wandering spiders are mainly collected by nocturnal hand species only occurs in protected fragments of the Atlantic collection when they are foraging (JOCQUÉ & DIPPENAAR-SCHOEMAN Rainforest (DIAS et al. 2005a). The high vulnerability of this 2007). species to changes that affect the natural habitat and its ende-

ZOOLOGIA 28 (4): 432–439, August, 2011 Modeling distribution of Phoneutria bahiensis: an endemic and threatened spider from Brazil 437 mism suggest that P. bahiensis is a promising bioindicator of depletion caused by the spread of greenhouses. Biodiversity human impact over the Atlantic Rainforest (Ombrophilous Conservation 18: 2509-2520. Forest and Restinga). The fast fragmentation of this biome re- BRAGAGNOLO, C.; A.A. NOGUEIRA; R. PINTO-DA-ROCHA & R. PARDINI. quires the urgent implementation of proposals for the conser- 2007. Harvestmen in an Atlantic forest fragmented vation and future survival of this species. In this sense, further landscape: Evaluating assemblage response to habitat quality studies on this species should focus on ethological and eco- and quantity. Biological Conservation 139: 389-400. logical aspects, and additional new field surveys that increase BRAZIL, T.K.; C.M. PINTO-LEITE; L.M. ALMEIDA-SILVA; R.M. 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Submitted: 27.X.2010; Accepted: 17.VI.2011. Editorial responsibility: Pedro Gnaspini

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