In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae)

BIOC-06901; No of Pages 12 Biological Conservation xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

Biological Conservation journal homepage: www.elsevier.com/locate/bioc

In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae) Guarino R. Colli a,⁎, Jéssica Fenker a, Leonardo G. Tedeschi a, André F. Barreto-Lima a, Tamí Mott b, Síria L.B. Ribeiro c a b c

Departamento de Zoologia, Universidade de Brasília, 70910-900 Brasília, DF, Brazil Instituto de Ciências Biológicas, Universidade Federal de Alagoas, Av. Lourival Melo Mota, s/n, Cidade Universitária, 57072-970 Maceió, AL, Brazil Universidade Federal do Oeste do Pará, Programa de Pós-Graduação em Recursos Naturais da Amazônia, Rua Vera Paz Salé, 68035-110 Santarém, PA, Brazil

a r t i c l e

i n f o

Article history: Received 1 December 2015 Received in revised form 18 July 2016 Accepted 24 July 2016 Available online xxxx Keywords: Amphisbaenia Body size Conservation Description dates Data-deficiency Neotropics Richness

a b s t r a c t Worm lizards remain one of the least known groups of vertebrates, hindering a proper assessment of their levels of threat, especially in tropical regions where worm lizard diversity is highest and habitat destruction is rampant. We examine trends in description dates, conservation status and their correlations with body and range size, and conduct a spatiotemporal analysis of Brazilian worm lizard sampling gaps using socioeconomic and environmental predictors, to identify drivers of discovery rates and prioritize regions for conducting inventories. Brazilian worm lizards exhibit small body sizes and small and isolated geographic ranges. Threatened, Near Threatened, and Data Deficient species have significantly smaller ranges than Least Concern species and are under intense anthropic pressure. The cumulative number of new species being described has grown exponentially since 1990, with no signs of reaching an asymptote, and recently described species tend to be smaller and have more restricted ranges. Present-day figures grossly underrate worm lizard diversity, which currently cannot be estimated. The vast majority of municipalities have no records of worm lizards and sampling is concentrated in regions with greater accessibility and population size, reflecting the historical patterns of colonization of the territory. The combination of very small geographic ranges, a large number of species yet to be discovered, and high rates of habitat loss, especially in the Cerrado of central Brazil, where the diversity of worm lizards is highest, suggests that extinction of undescribed species is commonplace. The discovery of new species of worm lizard can be accelerated by conducting inventories in unexplored regions, as well as by integrative taxonomic approaches assessing genetic variability in the more widely distributed species. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Assessing the extinction risk of living species, both at the global and regional levels, is a critical step towards their conservation. Such assessments can identify target species and ecosystems for future conservation and research programs, as well as the major threats to their long-term persistence. Extinction risk assessments often follow the guidelines of the IUCN Red List Categories and Criteria, which aim to objectively determine whether species are threatened or not and, if threatened, which category of threat they belong to (IUCN Standards and Petitions Subcommittee, 2016). This is based on five criteria that rely on information about population decline (Criterion A), population size (Criteria C and D), geographic range size (Criteria B and D) or quantitative (e.g., population viability) analyses (Criterion E). If insufficient information is available for such assessment, species are placed in the Data Deficient (DD) category. ⁎ Corresponding author. E-mail address: [email protected] (G.R. Colli).

A recent assessment of the global extinction risk of reptiles, based on a sample of 1500 species (ca. 16% of the described species), classified 21% as DD (Böhm et al., 2013). This percentage is similar to the one recorded for amphibians, the terrestrial vertebrate group with the highest level of data deficiency (Hoffmann et al., 2010; IUCN, 2016). The effectiveness of conservation plans and actions derived from such extinction risk assessments can be severely undermined by high levels of data deficiency, because many DD species can be actually threatened and their inclusion in systematic conservation planning may significantly change the spatial configuration of protected areas networks (Howard and Bickford, 2014; Morais et al., 2013; Trindade et al., 2012). Among reptiles, data deficiency is highest in tropical regions and in fossorial species, such as the worm lizards (Amphisbaenia), where 50% of the species assessed were regarded as DD (Böhm et al., 2013). Worm lizards comprise nearly 200 living species distributed primarily in tropical and subtropical regions, including southern Europe, western Asia, Africa, the Americas and the Caribbean (Uetz et al., 2007). Seemingly, they radiated and dispersed in the Palaeogene in response to the K–Pg

http://dx.doi.org/10.1016/j.biocon.2016.07.033 0006-3207/© 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

2

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

extinction, and achieved most of their current geographic distribution through long-distance dispersal across oceans (Longrich et al., 2015). They have a highly modified bauplan, characterized by a cylindrical and elongate body, lack of limbs, short tail, reduced eyes and a solid skull (Brandley et al., 2008; Kearney and Stuart, 2004). A combination of highly specialized muscles, modified head shapes, body scales arranged in rings, and tolerance to low O2 and high CO2 concentrations aid in burrowing, which is accomplished by compression of the substrate against the walls of the tunnels they dig (Gans, 1975, 1978; Navas et al., 2004). They are oviparous and feed on a variety of arthropods (Colli and Zamboni, 1999; Webb et al., 2000). Due to a subterranean lifestyle, worm lizards are poorly represented in biological collections and many species are known from a single specimen or from a single locality (Gans, 1967, 2005). As a consequence, they remain one of the poorest known vertebrate groups, hindering a proper assessment of their extinction risk. Reducing the level of data deficiency among worm lizards is critical, especially in tropical regions where their diversity is highest and habitat destruction is rampant (Böhm et al., 2013). To help filling the gaps in our understanding of worm lizards and aid in global assessment of their extinction risk, we assembled a comprehensive geographic distribution database of Brazilian worm lizards, which represent ca. 35% of all living species, based on the literature and scientific collection records. Next, we identify major patterns in the evolution of knowledge on the group, by examining trends in description dates and correlations with body size and range size. Further, we examine the association of body size and range size with species conservation status. Lastly, we conduct a spatial analysis of worm lizard sampling gaps in the Brazilian territory, using socioeconomic and environmental predictors to identify drivers of discovery rates, as well as to prioritize regions for conducting inventories. 2. Methods 2.1. Data collection We recorded data on the conservation status, date of description, snout-vent length (SVL in mm) and total area of the geographical distribution of all species of worm lizards known to occur in Brazil. We recorded data from across the entire geographical distribution of each species, and did not restrict the records to the Brazilian territory. Our database consisted of 1220 geographic distribution records (Supplementary material Table A1, Fig. A1). Our species list follows the Brazilian Society of Herpetology (Costa and Bérnils, 2014), with the addition of species described until August 2015 (Amphisbaena metallurga, Costa et al., 2015). We regard A. amazonica, A. varia and A. wiedi as subspecies of A. fuliginosa, and A. bolivica as a subspecies of A. camura; thus, our database consisted of 70 species of worm lizards (Table 1). Conservation status follows the Brazilian list of endangered species (Lista Nacional Oficial das Espécies da Fauna Ameaçadas de Extinção, Portaria MMA 444/2014, http://www.icmbio.gov.br/ portal/biodiversidade/fauna-brasileira/estado-de-conservacao/7480anfisbenias.html). The date of description corresponds to the year in which the species was formally described as an independent taxonomic unit. We used the SVL of the holotype or the mean SVL of the type series, if from the same date and location, as a surrogate of body size. When necessary, we used data from the neotype (Amphisbaena camura). When descriptions lacked SVL data, we averaged data available from the literature (A. anaemariae, A. bedai, A. crisae, and A. saxosa). We could not obtain the SVL of A. acrobeles, because it is known from a single, mutilated specimen, missing the posterior third of the body (Ribeiro et al., 2009). To account for possible interspecific differences in body diameter, we also calculated body volume using the formula for a right cylinder. Because only 22 out of the 70 species of Brazilian species of worm lizards (31%) have data on body diameter (or circumference) available in the original description, we used head width, if

available, as a surrogate of body diameter because in most species these two measurements tend to be very similar. Other studies have used the mean or maximum body of the species, in lieu of the size of the holotype or type series, to test the association between description dates and body size (e.g., Gaston, 1991). Even though the mean or maximum size of a species should be correlated with the size of specimens used for taxonomic descriptions, we view such approaches as inaccurate, since they confound the effects of body size with other correlated variables (e.g., range size). Specimens used in descriptions must be used, to inform about the effect of body size on the likelihood of being detected in the field and described. We obtained the total area of distribution of each species (hereafter range size) using the Minimum Convex Polygon (MCP) method (Burt, 1943). Here, we did not bound range size to the limits of the Brazilian territory. Species distribution models were not suitable due to the small number of records available for most species. For species known only from the type locality, we generated a buffer represented by a circle with 10 km radius. For species with two distribution records, we calculated the area of a beam corresponding to two 10 km2 buffers along the smallest distance between them. For Amphisbaena ridleyi, endemic to the island of Fernando de Noronha (Gans, 2005; Vanzolini, 2002), we used the size of the island as range size. Distribution records were obtained from the literature and scientific collections. To identify patterns in the distribution of species richness, we transformed the total land area of Brazil in a one-degree grid, a common choice in macroecology and geographic ecology studies. A finer scale could lead to serious commission errors, because many species have few distribution records. Next, we generated binary arrays by overlapping the distributions, generated using the MPC method described above, of each species in each cell and calculated the species richness in each cell by adding the arrays. We recognize the limitations of this procedure and that species richness may be overestimated. Nevertheless, this is the only feasible option considering the very sparse nature of the data and the lack of even basic knowledge about habitat use of worm lizards, for instance to calculate the area of occupancy (AOO). True richness will be greatly overestimated especially in the presence of widely separated, disjunct records (e.g., in Amazonia and Atlantic Forest, but not in the intervening Cerrado and Caatinga), but there are no such patterns among Brazilian worm lizards. Overall, our results should be viewed as a first approximation of the true richness patterns of Brazilian worm lizards. We also calculated the mean description date, mean range size, and mean body size for each 1° × 1° grid cell, to identify spatial patterns in these parameters. These analyses were conducted in ArcMap 10.1 (ESRI, 2011).

2.2. Data analysis 2.2.1. Influence of body/range size in description rates/conservation statuses To compare SVL and range size among species according to conservation status, we used an analysis of variance (ANOVA) upon the log10 transformed data, followed by Tukey HSD multiple comparison tests whenever needed. We recognize that the SVL of the holotype or type-series may not accurately describe the species/population mean, so these results must be interpreted with caution. To model the association between description dates, body size (or body volume) and range size, we used linear models assuming a Gaussian distribution of residuals. In the two instances this assumption was not met, we used a logarithmic (log10) transformation of the response variable (range size) (Quinn and Keough, 2002). To partition the total variation in description dates among the exclusive contributions of body size and range size, as well as their shared contribution, we assumed their effects are additive and used partial regression (Borcard et al., 1992; Legendre and Legendre, 1998).

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

3

Table 1 Dates of description, snout-vent length (SVL, mm), body diameter (mm) and range size (km2) of Brazilian species of worm lizards. Source refers to the SVL data. Category refers to conservation status according to the Brazilian list of endangered species (“Lista Nacional Oficial das Espécies da Fauna Ameaçadas de Extinção (Portaria MMA 444/2014)”). Species

Description

SVL

Diameter

Source

Range size

Records

Category

Amphisbaena absaberi Amphisbaena acrobeles Amphisbaena alba Amphisbaena anaemariae Amphisbaena anomala Amphisbaena arda Amphisbaena arenaria Amphisbaena bahiana Amphisbaena bedai Amphisbaena bilabialata Amphisbaena brasiliana Amphisbaena brevis Amphisbaena caiari Amphisbaena camura Amphisbaena carli Amphisbaena carvalhoi Amphisbaena crisae Amphisbaena cuiabana Amphisbaena cunhai Amphisbaena darwini Amphisbaena dubia Amphisbaena frontalis Amphisbaena fuliginosa Amphisbaena hastata Amphisbaena heathi Amphisbaena hogei Amphisbaena ibijara Amphisbaena ignatiana Amphisbaena kingii Amphisbaena kraoh Amphisbaena leeseri Amphisbaena leucocephala Amphisbaena littoralis Amphisbaena lumbricalis Amphisbaena maranhensis Amphisbaena mensae Amphisbaena mertensii Amphisbaena metallurga Amphisbaena miringoera Amphisbaena mitchelli Amphisbaena munoai Amphisbaena neglecta Amphisbaena nigricauda Amphisbaena persephone Amphisbaena pretrei Amphisbaena prunicolor Amphisbaena ridleyi Amphisbaena roberti Amphisbaena sanctaeritae Amphisbaena saxosa Amphisbaena silvestrii Amphisbaena slevini Amphisbaena steindachneri Amphisbaena supernumeraria Amphisbaena talisiae Amphisbaena trachura Amphisbaena tragorrhectes Amphisbaena uroxena Amphisbaena vanzolinii Amphisbaena vermicularis Leposternon cerradensis Leposternon infraorbitale Leposternon kisteumacheri Leposternon maximus Leposternon microcephalum Leposternon octostegum Leposternon polystegum Leposternon scutigerum Leposternon wuchereri Mesobaena rhachicephala

2001 2009 1758 1997 1914 2003 1991 1964 1991 1972 1865 2009 2014 1862 2010 1965 1997 2001 1991 1839 1924 1991 1758 1991 1936 1950 2003 1991 1833 1971 1964 1878 2014 1996 2012 2000 1881 2015 1971 1923 1960 1936 1966 2014 1839 1885 1890 1964 1994 2003 1902 1936 1881 2009 1995 1885 1971 2008 1963 1824 2008 1859 2000 2011 1824 1851 1851 1820 1879 2009

242 NA 593 145 256 285 195 190 235 250 264 137 132 368 264 102 118 221 199 250 139 246 383 133 124 112 191 148 186 304 250 343 251 129 125 121 290 159 107 134 134 119 117 138 270 185 250 205 165 290 139 103 140 195 105 285 110 99 141 216 317 384 356 418 311 325 350 432 340 249

9.18 3.90 NA NA 9.50 9.00 3.30a NA NA NA NA 8.50 3.00 NA NA 3.60 NA 5.80 5.36 NA NA 5.00a NA 2.05a 3.50 NA 6.00 2.58a NA NA NA 11.00 6.84 2.40a 2.70 2.62a NA 4.47a NA NA 4.00 NA NA 2.54a NA NA 11.00 4.30a 3.10a NA NA 3.00 NA NA 2.50a NA NA 4.20 NA NA 8.69 NA NA 8.00 16.17b NA NA NA NA 5.50

Type series Holotype Type series SVL average Holotype Holotype Holotype Holotype SVL average Holotype Holotype Holotype Holotype Neotype Holotype Type series SVL average Type series Type series Holotype Holotype Type series Type series Type series Holotype Type series Type series Type series Holotype Holotype Type series Holotype Type series Holotype Holotype Holotype Holotype Type series Type series Holotype Holotype Type series Type series Type series Holotype Holotype Holotype Holotype Holotype SVL average Holotype Holotype Holotype Holotype Holotype Holotype Holotype Holotype Type series Holotype Type series Holotype Holotype Holotype Holotype Holotype Holotype Holotype Holotype Type series

314 314 11,558,100 138,743 251,581 5894 314 6079 2552 29,961 715,566 314 1249 1,623,969 3287 1968 314 30,279 81,395 241,681 75,633 6525 6,006,463 40,044 2663 10,563 74,475 6101 2,378,363 5747 1,180,973 314 175 154 314 188,562 1,033,478 314 188,098 186,929 290,521 14,970 5065 314 1,597,625 718,708 20 2,047,839 314 1338 673,334 112,264 117,598 577 314 490,391 314 348 503,263 4,262,090 314 2,280,655 14,544 2665 4,963,323 578 1,950,248 560 339,132 1886

1 1 169 7 16 1 1 2 3 4 9 1 4 24 2 2 1 6 4 22 5 2 58 6 6 5 8 2 138 2 13 1 1 4 1 10 68 1 5 21 23 2 5 1 41 64 4 37 1 2 19 3 4 2 1 68 1 2 6 72 1 31 3 6 129 6 32 4 10 3

DD LC LC LC LC EN B1ab(iii) DD DD LC LC LC DD DD LC LC NT DD LC LC LC LC EN B1ab(iii) LC LC NT LC LC DD LC LC LC DD DD NT DD LC LC NE LC LC LC DD EN B1ab(iii) NE LC LC LC LC DD NT LC LC LC EN B1ab(iii) DD LC LC EN B1ab(iii) LC LC DD LC VU B1ab(iii) LC LC EN B1ab(iii) LC EN B1ab(iii) LC LC

DD: Data Deficient, EN: Endangered, LC: Least Concern, NE: Not Evaluated, NT: Near Threatened, VU: Vulnerable. a Head width used as surrogate of body diameter. b Body circumference used to calculate body diameter.

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

4

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

2.2.2. Influence of environmental/socioeconomic predictors on sampling intensity To identify drivers of discovery rates, as well as to prioritize regions for future inventories, we conducted a spatial analysis of worm lizard

25

Number of species

A 20

15

10

5

sampling gaps in the Brazilian territory using environmental and socioeconomic predictors. In this analysis, the number of species recorded in each of the 5564 Brazilian municipalities (an indicator of sampling intensity) was the response variable. We acknowledge that variation in the number of species can be affected both by sampling effort and by actual species richness. Unfortunately, data on sampling effort is not readily available; therefore, we assume that most of the variation in species numbers among Brazilian municipalities is due to variation in sampling effort, and not in species richness. As environmental predictors, we calculated for each municipality (1) mean values of altitude and 19 bioclimatic variables from Worldclim (Hijmans et al., 2005), evapotranspiration and the proportion of evapotranspiration (Willmott and Matsuura, 2001); and (2) the most representative class of geology (South America Geologic Map, https://catalog.data.gov/dataset/southamerica-geologic-map-geo6ag) and hydrographic basin (Ottobacias Nível 2, Agência Nacional de Águas, www.ana.gov.br). We reasoned that environmental predictors subsumed both abiotic and, indirectly, biotic characteristics of sampled areas, which in turn may affect rates of discovery (e.g., naturalists may be more fascinated and prone to conduct inventories in the more charming Neotropical rainforests

0 0

100

200

300

400

500

600

3.0

A

Snout-vent length (mm) 15

Number of species

10

log10 snout-vent length

2.8

B

5

2.6

2.4

2.2

2.0

0

DD 1

2

3

4

5

6

LC

NT

T

7 8

log10 Range size (km2)

B

15

6

log10 range size

Number of species

C 10

5

4

2

0 0

1750

1800

1850

1900

1950

2000

2050

Year of description Fig. 1. Frequency distribution of (A) snout-vent length (SVL), (B) range size, and (C) dates of description of Brazilian species of worm lizards.

DD

LC

NT

T

Fig. 2. Boxplots depicting patterns of (A) snout vent-length and (B) range size of species of Brazilian worm lizards according to conservation status. DD: Data Deficient, LC: Least Concern, NT: Near Threatened, T: Threatened (Endangered or Vulnerable).

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

than in Neotropical savannas and dry forests). We grouped socioeconomic predictors according to their presumed effects: 1) Accessibility effect: we predicted that municipalities better served with roads would be better sampled and used the ratio of total road extension (km) by municipality area (data from Instituto Brasileiro de Geografia e Estatística – IBGE). 2) Area effect: we predicted that larger municipalities would be more sampled and used the total area (km2) as provided by IBGE. 3) Knowledge effect: we predicted that municipalities with greater human impacts would be better sampled, due to required environmental impact studies carried out at these places. We used the presence Hydropower Energy Plants in each municipality (data from Agência Nacional de Energia Elétrica – ANAEEL), the proportional cumulative loss of natural habitats until 2009 (data from IBGE), proportion of urban area, and food production (2013 mean agricultural production, net value per agricultural product and flock size; data from IBGE). 4) Population effect: we predicted that more populous municipalities or those with better social indicators would be better sampled. We used the Human Development Index (IDH) for 2010, which consists of three indicators: longevity (opportunity of long and healthy life, calculated by life expectancy at birth), education (access to knowledge, calculated by schooling geometric mean of the adult population and school flow of youth population) and income (standard of living, measured by per capita income). We also used the Municipal Human Development Index, which represents the cube root of the multiplication of the three IDHMs (geometric mean). Finally, we used population estimates (total, urban, rural) provided in 2010 and 2014 by IBGE. 5) Protected areas effect: we predicted that municipalities with more protected areas would be better sampled and used the total and proportional area protected by municipality, taking into account Integral Protection (Unidades de Conservação de Proteção Integral) and Sustainable Use (Unidades de Conservação de Uso Sustentável)

5

protected areas, based on Instituto Chico Mendes de Conservação da Biodiversidade – ICMBio (www.icmbio.gov.br) in 2012. 6) Time effect: we predicted that older municipalities would be better sampled and used the year of creation of each municipality as provided by IBGE (www.cidades.ibge.gov.br/xtras/home.php). 2.2.3. Spatial patterns in worm lizard richness/body-size/range-size/ description dates Before conducting the spatial modelling, we selected the best predictors of the number of species recorded in Brazilian municipalities using the Guided Regularized Random Forest (GRRF) method, with R packages randomForest (Liaw and Wiener, 2002) and RRF (Deng, 2013). Random forests (RF) is an ensemble classification method based on decision trees, i.e., it combines the predictions of several decision trees to improve prediction accuracy and reduce prediction variability (Breiman, 2001). Each decision tree is built from a bootstrap sample of the original data and, at each split of each decision tree, a random set of n predictors (n is often set to the square root of the number of predictors) is chosen for binary partitioning of the data based on the maximum decrease in Gini impurity (Ceriani and Verme, 2012). Decision trees are grown fully, without pruning, and each is used to predict the out-of-bag observations [each bootstrap sample leaves out ~37% of the original observations, called OOB (out-of-bag) data], producing a running unbiased estimate of the classification error as trees are added to the forest. The predicted class of each observation is calculated by majority vote of the OOB predictions for that observation, with ties split randomly. When building decision trees in RF, regularisation penalizes the selection of new features for splitting when the gain (e.g. decrease in Gini impurity or increase in information gain) is similar to that of features used in previous splits, a method known as Regularised Random Forest (RRF). A GRRF is an enhanced RRF in which the importance scores from an ordinary RF are used to guide the feature selection process of RRF (Deng, 2013). We used the percent increase in mean square error (%IncMSE) and increase in node purity (IncNodePurity) in feature selection, and retained those features

World

Cumulative number of species

200

150

World - Brazil

100

Brazil

50

0 1750

1780

1810

1840

1870

1900

1930

1960

1990

2020

Year of description Fig. 3. Cumulative number species of worm lizards described in the world, in Brazil and in the remainder of the world, exclusive of Brazil, until August 2015. The dashed line indicates a greater rate of description in Brazil, relative to the rest of the world, after 1990.

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

3. Results

and range size jointly accounted for 50.2% of the total variation in description dates. Range size had a far greater effect (27.8% of the total variation) than body size (10.4%) or their shared contribution (12%). Nevertheless,

600

A Snout-vent length (mm)

where the average of the scaled %IncMSE and IncNodePurity was greater than one. Species distributional data often display spatial autocorrelation, i.e., when values of a variable measured at adjacent locations tend to be similar, which can lead to violation of the assumption of independence of residuals and inflated Type I error rates (Griffith, 2003). To account for spatial structure in the data, we implemented the spatial modelling with Bayesian hierarchical models using the Integral Nested Laplace Approximation (INLA) in R-INLA (Blangiardo et al., 2013). INLA uses an approximation for inference, avoiding the computationally- and timeintensive demands of Markov chain Monte Carlo (MCMC) methods (Blangiardo et al., 2013). We used a Conditional AutoRegressive (CAR) prior and default hyper-parameters currently implemented in R-INLA (see the R-INLA documentation available on http://www.r-inla.org). Since the Brazilian worm lizard data are counts characterized by many zeroes, we evaluated Poisson and negative binomial distributions, as well as their zero-inflated versions. Zero-inflated models distinguish between structural or “true” zeroes, resulting from the real effect we are trying to determine, and sample or “false” zeroes, resulting simply from chance events (Martin et al., 2005). R-INLA allows for Types 0, 1, and 2 of zero-inflation: Type 0 considers the response variable can only be positive (i.e., does not include zero); Types 1 and 2 are mixture models (2 being an extension of 1, that allows for additional zero probability), which describe the probability of being in an “imperfect state” where positive events may occur but are not certain and, as such, include both zero and nonzero values (see the R-INLA documentation available on http://www.r-inla.org). Before conducting the spatial modelling with R-INLA, we transformed the following variables to improve their distributions: urban population in 2010 and population in 2010 [log10(x)], roads [log10(x + 1)], and year of creation [log10(k − x), where k is max(x) + 1]. We selected best candidate models based on the deviance information criterion (DIC, Spiegelhalter et al., 2002). Further model selection was performed on the best models using the conditional predictive ordinate (CPO, Pettit, 1990) obtained from leave-one-out cross-validation, as implemented in R-INLA (Blangiardo and Cameletti, 2015). We conducted all analyses in R 3.3.0 (R Core Team, 2016).

500

400

300

200

100 1750

1850

1900

1950

2000

2050

8

B 6

4

2

0 1750

Geographic distribution (log10, km2)

3.1. Influence of body/range size in description rates/conservation statuses The SVL of 70 known species of Brazilian worm lizards (as of August 2015) averages 218.8 ± 98.5 mm (99–593 mm) and range size averages 663,870 ± 1,747,025 km2 (20–11,558,100 km2). Amphisbaena alba has both the largest SVL and range size (Table 1). Fourteen species (20%) are known from a single distribution record. Both SVL and range size have highly skewed distributions, with most species being characterized by small SVL and range size (Fig. 1A–B). Thirty-six species (51%) have a SVL smaller than 200 mm, while 34 (49%) have a range size smaller than 10,000 km2. Forty-two species (61.8%) are classified as Least Concern-LC, 14 (20.6%) as Data Deficient-DD, 1 (1.5%) as VulnerableVU, 4 (5.9%) as Near Threatened-NT, and 7 (10.3%) as Endangered-EN (Table 1). There were no differences in SVL among the combined threat categories (EN, VU), NT, LC, and DD (F3,64 = 1.733, P = 0.169), whereas range size was significantly larger for LC species compared to the remainder (F3,64 = 20.15, P ≪ 0.001; Tukey HSD, P b 0.003) (Fig. 2). The number of described species of Brazilian worm lizards has slowly increased until 1990, at a rate much lower than in the remainder of the world, when it reached 39 species; ever since, thirty-one (44%) species of Brazilian worm lizards were described, at an accelerating rate, higher than in the remainder of the world and with no signs of stabilization (Figs. 1C, 3). Species described more recently tend to have a smaller SVL (Fig. 4A, β = −0.747, P b 0.001) and range size (Fig. 4B, β = −0.014, P b 0.001, log10 transformation), and larger species tend to have larger ranges (Fig. 4C, β = 0.004, P b 0.025, log10 transformation). Body size

1800

Description date Geographic distribution (log10, km2)

6

1800

1850

1900

1950

2000

2050

Description date

8

C 6

4

2

0 100

200

300

400

500

600

Snout-vent length Fig. 4. Relationship between (A) snout-vent length and (B) range size versus description date of Brazilian species of worm lizards; (C) relationship between range size and SVL of Brazilian species of worm lizards. The solid line represents the linear regression fit, whereas the red dashed line represents a cubic smoothing spline fit. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

dropping body size from the multiple regression model made it significantly worse (χ2 = 11.26, P b 0.001). When using body volume as a surrogate of body size, we found a significant relationship between body volume and description date (Supplementary material Fig. A2, β = − 0.006, P = 0.014, log10 transformation), but not between body volume and range size (Supplementary material Fig. A2, β = − 0.00002, P = 0.329, log10 transformation). Body volume and range size jointly accounted for 29% of the total variation in description dates. Range size and body volume had similar effects (11% and 16.1%, respectively), but their shared contribution was very small (1.9%). These differences when using either body size or body volume seemingly resulted from the large reduction in degrees-of-freedom in the latter case, since body volume could be calculated for only 33 species (47% of the total), and the nonrandom pattern of data missingness where data on body diameter tended to be more available for recently described species (mean description date of species with data on body diameter: 1979 ± 45, mean description date of species without data on body diameter: 1912 ± 71, t61.9 = 4.78, P b 0.001). 3.2. Spatial patterns in worm lizard richness/body-size/range-size/ description dates Worm lizard richness is concentrated along the southwest-northeast dry diagonal, being highest in the Cerrado of central Brazil and lowest in Amazonia (Fig. 5A). Description dates are younger, body size is smaller, and range sizes are smaller in central and southern Brazil,

7

the opposite occurring in Amazonia (Figs. 5B–C). The vast majority of the Brazilian municipalities (5074 out of 5564; 91%) have no records of worm lizards (Fig. 6).

3.3. Influence of environmental/socioeconomic predictors on sampling intensity The GRRF analysis retained five most important predictors of the number of species recorded in municipalities (in decreasing order of importance): urban population in 2010, population in 2010, roads, BIO15 (precipitation seasonality), and year of creation. DIC values indicated that the Poisson and zero-inflated Poisson 1 models were similar and performed better than the others (Table 2), so they were investigated for further model selection. CPO values indicated the zero-inflated Poisson 1 model had greater predictive performance, so this model was retained as our final model (Table 3). The number of worm lizard species recorded in Brazilian municipalities increased with the length of the road network and total population in 2010, but decreased with precipitation seasonality and year of creation (Table 3). Urban population in 2010 had no effect. Our final model tended to underestimate rare municipalities with large number of species (Fig. 7). Observed and predicted values of species of worm lizards recorded in Brazilian municipalities are mapped in Fig. 8. Model residuals emphasize a concentration of municipalities in central Amazonia, central and southern Brazil where future sampling efforts should produce greater returns (Fig. 8).

B

A AM

AM CA

CA

CE

CE

PN

PN

Species richness

Mean SVL (mm)

1-3

145.5 - 235.0

AF

AF

235.0 - 324.5

4-6

324.5 - 414.0

7-8 9 - 11

414.0 - 503.5

PA

PA

503.5 - 593.0

12 - 13

C

D AM

AM

CA

CA

CE

CE PN

PN

Mean description date

Mean range size (x 1,000 km2)

1758 - 1809

AF

1809 - 1859

2,287.851 - 4,575.683

1859 - 1910 1910 - 1960 1960 - 2011

AF

0.020 - 2,287.851

4,575.683 - 6,863.514

PA

6,863.514 - 9,151.346 9,151.346 - 11,439.177

PA 0

250 500

1,000 km

Fig. 5. Maps depicting the distribution of (A) species richness, (B) mean snout-vent length, (C) mean description date, and (D) mean range size of Brazilian worm lizards across a onedegree grid. AF: Atlantic Forest, AM: Amazon Forest, CA: Caatinga, CE: Cerrado, PA: Pampas, PN: Pantanal.

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

8

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

log10 Municipalities

4

Table 3 Posterior estimates (mean and 95% credibility interval) of parameters in the final hierarchical Bayesian model, using the integral nested Laplace approximations (INLA), with Poisson likelihood. Parameters

3

Poisson (CPO = −6053.15) Roads Year of creation Urban population 2010 BIO15 Total population 2010 Zero-inflated Poisson 1 (CPO = −2312.47) Roads Year of creation Urban population 2010 BIO15 Total population 2010

2

1

0 0

1

2

3

4

5

6

7

8

Species recorded Fig. 6. Frequency distribution of the number of species of worm lizards recorded in Brazilian municipalities (log10 transformed).

4. Discussion Our analysis shows that, in general, Brazilian species of worm lizards are characterized by small body size and range size, and that these two quantities are positively correlated. Such patterns have been observed elsewhere (Meiri, 2008), and ultimately result from size-dependent speciation and extinction rates, mediated by environmental grain, resource distributions, and inter- and intra-specific body size optimization (Gaston and Blackburn, 2000). A right-skewed distribution of body size arises from (1) competitive exclusion among species that have similar demands for resources, (2) differential extinction probabilities of large-bodied species, coupled with (3) greater specialization of smaller organisms resulting from energetic constraints related to body size (Brown, 1995; Brown and Maurer, 1989). As such, small species tend to be restricted to habitats where their more specialized demands for resources can be met and, thus, have smaller geographic ranges (Harcourt, 2006). Indeed, many small-bodied species are apparently confined to relatively small patches of sandy soils (Ribeiro et al., 2009) or semideciduous forests (Costa et al., 2015), and one-fifth of the Brazilian species of worm lizards are known from a single locality. It is likely that, in many cases, this does not represent species actual ranges but results from a lack of data on their geographic distributions; however, a number of species seem truly restricted to very small and isolated ranges. We found that range size is a better predictor of conservation status of Brazilian worm lizards than SVL, with DD, NT and threatened species Table 2 Deviance information criterion (DIC) of all tested hierarchical Bayesian models, using the integral nested Laplace approximations (INLA). Model

DIC

Poisson Zero-inflated Poisson 0 Zero-inflated Poisson 1 Zero-inflated Poisson 2 Negative binomial Zero-inflated negative binomial 0 Zero-inflated negative binomial 1 Zero-inflated negative binomial 2

3301.36 NA 3311.04 NA 3443.01 NA 3453.84 3508.46

Note: The zero-inflated Poisson 0, zero-inflated Poisson 2, and zero-inflated negative binomial 0 models had very large DICs, suggesting these models were inappropriate (NA).

Mean

Q0.025

Q0.975

1.2432 0.6208 0.2467 −0.0136 0.8312

0.9766 0.3000 −0.4365 −0.0182 0.0057

1.5140 0.9459 0.9473 −0.0089 1.6413

1.2405 0.6144 0.2497 −0.0135 0.8184

0.9747 0.2935 −0.4320 −0.0181 −0.0049

1.5110 0.9407 0.9486 −0.0088 1.6272

having significantly smaller ranges than LC species. This largely reflects the use of range-based IUCN criteria for the assessment of conservation status, but also the higher vulnerability of small-ranged species to habitat loss (percent range lost to anthropization was also used in the Brazilian assessment). Even though some of the threatened, microendemic Brazilian worm lizards occur in protected areas (e.g., Amphisbaena ridleyi is confined to the Fernando de Noronha archipelago), many do not and are under intense anthropic pressure (e.g., A. uroxena, Leposternon octostegum, L. scutigerum). Even species that occur in protected areas are not safe, because most protected areas in Brazil allow the sustainable use of natural resources and often undergo similar deforestation rates to those outside protected areas (Françoso et al., 2015). The small ranges of DD species of worm lizards mirror those of Brazilian anurans (Morais et al., 2013) and highlight the need to quickly assemble the data necessary to ascertain their conservation status. Since 1991, new species of Brazilian worm lizards are being described at an accelerating pace, higher than in the remainder of the world. Nevertheless, with few exceptions, these recent descriptions did not result from taxonomic revisions or from the splitting of formerly described species. Instead, most resulted from specimens collected during the filling of hydroelectric dams (Castro-Mello, 2000; Strüssmann and Mott, 2009; Vanzolini, 1996, 1997) or during faunal surveys in less explored parts of the country, such as the Jalapão (Ribeiro et al., 2009, 2011), northern Pará (Hoogmoed et al., 2009), and the fossil sand dunes of the São Francisco river (Vanzolini, 1991a, 1991b). Therefore, it is likely that future exploration of new areas, along with integrative taxonomic studies including molecular assessments of diversity, will boost the discovery of new species of Brazilian worm lizards, as it happened with other groups of the herpetofauna (Domingos et al., 2014; Guarnizo et al., 2016; Werneck et al., 2012). We found that the Cerrado harbors the highest richness of worm lizards in Brazil and this can be partially due to a highly heterogeneous landscape (Colli et al., 2002), deep soils (Motta et al., 2002) and high belowground plant biomass (de Miranda et al., 2014; Durigan et al., 2012). A reliable understanding of the diversity of worm lizards is critical to assess their extinction risk, to prioritize conservation hotspots (e.g., areas of high endemism that are facing unprecedented habitat loss, Myers et al., 2000) and to implement efficient measures for their conservation. Our results suggest that current figures largely underestimate the real diversity of Brazilian worm lizards and that their predicted richness cannot even be estimated, since the cumulative description record is far from reaching an asymptote. Description dates of Brazilian species of worm lizards are negatively correlated with body size and range size, mirroring patterns found in many different taxonomic groups, including British beetles (Gaston, 1991), North American butterflies (Gaston et al., 1995), global carnivores and primates (Collen et al., 2004), Cerrado frogs (Diniz-Filho et al., 2005), New World snakes (Vilela et al., 2014), and global lizards (Meiri, 2008). Thus, Brazilian species of worm lizards yet to be discovered are likely

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

8

Predicted richness

6

4

2

0 0

2

4

6

8

Observed richness Fig. 7. Observed and predicted values of worm lizard species recorded in Brazilian municipalities according to the final hierarchical Bayesian model, using the integral nested Laplace approximations (INLA), with Poisson likelihood. Solid line is the standard linear model with Gaussian distributed errors; dashed line is the expected relationship if the model fit the data perfectly.

small bodied and with small range sizes. This pattern seemingly results from the fact that large species tend to have larger ranges and be easier to find and collect. Not all studies, however, clearly support these relationships. For instance, the relationship between body size and description dates lacked generality among North American and Australian reptiles and amphibians (Reed and Boback, 2002). Further, among South American oscine passerine birds (Blackburn and Gaston, 1995),

9

Australian scarab beetles (Allsopp, 1997), western Palaearctic dung beetles (Cabrero-Sañudo and Lobo, 2003), and global carnivores and primates (Collen et al., 2004), geographical range is a much better predictor of description dates than is body size. Our results also support a more prominent influence of range size than body size upon description dates. Moreover, much like the beetle fauna of Britain (Gaston, 1991), the body size of Brazilian worm lizards described each year does not decline consistently with time, but seems to level approximately around 1950 (Fig. 4A). These relationships can be further complicated by the historical patterns of colonization of the territory (Blackburn and Gaston, 1995; Diniz-Filho et al., 2005; Reed and Boback, 2002). In Brazil, human colonization proceeded from the Atlantic coast inland (east to west) and description dates of Brazilian worm lizards partially support this pattern, since average description dates are younger in central and southern Brazil. The Cerrado region of central Brazil has been colonized more recently than other parts of the country (Jepson et al., 2010; Klink and Moreira, 2002). However, average body size and range size of worm lizards from these regions is smaller than in the rest of the country, suggesting they indeed harbor smaller, narrow-ranged species of worm lizards. Detailed knowledge about species ranges is critical for conservation, both to assess their risk of extinction and to identify areas of high conservation priority. Our results reveal a large gap in the knowledge about the geographic distribution of Brazilian worm lizards: N 90% of the Brazilian municipalities have not a single record of worm lizards. In part, this is a consequence of the difficulty of collecting fossorial animals and the apparent rarity and low density of worm lizards (Gans, 2005; but see Papenfuss, 1982). Still, sampling intensity, as indicated by the number of species recorded in each municipality, has been driven primarily by accessibility, population size and time (year of creation), and to a lesser extent by precipitation seasonality. These patterns arise from the fact that older municipalities are more likely to have been sampled and from the high costs of conducting biological inventories

Fig. 8. Maps depicting (A) observed, (B) predicted, and (C) residual values of worm lizard species recorded in Brazilian municipalities according to the final hierarchical Bayesian model, using the integral nested Laplace approximations (INLA), with Poisson likelihood, of Brazilian species of worm lizards recorded in (D) Brazilian municipalities.

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

10

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

in remote, less populated areas (Funk et al., 2005). Similar biases resulting from a concentration of sampling records, often next to developed areas, were also observed among Brazilian plants (Nelson et al., 1990; Sousa-Baena et al., 2014), Italian ground beetles (Barbosa et al., 2010), and Iberian water beetles (Sánchez-Fernández et al., 2008). The negative association between sampling intensity and precipitation seasonality apparently stems from the fact that the latter is highest in the Caatinga of northeastern Brazil: its herpetofauna is one of the least studied in Brazil (Rodrigues, 2003, 2005). Overcoming the Wallacean shortfall, i.e., the limited knowledge on the geographic distributions of organisms (Lomolino, 2004), can be achieved by improving primary data on species ranges or by using species distribution models (Elith and Leathwick, 2009). Given the sparse nature of the distribution records and their small ranges, investment in obtaining primary distribution data of worm lizards is seemingly a requirement. In this regard, we recommend (1) the use of adequate sampling methods, e.g., pitfall traps with drift fences (Rodrigues, 2003); (2) the maximization of returns during environmental impact studies and “faunal rescue operations” associated with large development projects (e.g., dams, roads, transmission lines); (3) inventories in unexplored regions, especially in habitat patches putatively known to harbor endemic fossorial species (e.g., sandy soils in central and northeastern Brazil, Rodrigues, 1996); and (4) the implementation of citizen science programs (Devictor et al., 2010), still incipient in Brazil. 5. Conclusions Brazilian worm lizards as a group exhibit small body sizes and small and isolated geographic ranges. Small range size is characteristic of the threatened (EN, NT, VU) and Data Deficient species and most are under intense anthropic pressure. The cumulative number of new species being described has grown exponentially since 1990, with no signs of reaching an asymptote, and recently described species tend to be smaller and have more restricted ranges than previously described species. The current numbers of Brazilian worm lizards grossly underestimate their real diversity and their predicted richness cannot even be estimated. The vast majority of Brazilian municipalities have no records of worm lizards and sampling is concentrated in regions with greater accessibility and population size, reflecting the historical patterns of colonization of the territory. The combination of very small geographic ranges, a large number of species yet to be discovered, and high rates of habitat loss, especially in the Cerrado of central Brazil, where the diversity of worm lizards is highest, suggests that extinction of undescribed species could be commonplace. The discovery of new species can be accelerated by conducting inventories in unexplored regions, as well as by integrative taxonomic approaches, especially assessing genetic variability in the more broadly distributed taxa. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.biocon.2016.07.033. Acknowledgements This work was supported by CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Grants 109/2007 and 071/2013), Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq (Grants 563320/2010-9 and 457587/2012-1) and Fundação de Apoio à Pesquisa do Distrito Federal – FAPDF (Grants 193.000.292/2007, 193.000.576/2009, 193.000.476/2011 and 193.000.456/2011). References Allsopp, P.G., 1997. Probability of describing an Australian scarab beetle: influence of body size and distribution. J. Biogeogr. 24, 717–724. Barbosa, A.M., Fontaneto, D., Marini, L., Pautasso, M., 2010. Is the human population a large-scale indicator of thespecies richness of ground beetles? Anim. Conserv. 13, 432–441.

Blackburn, T.M., Gaston, K.J., 1995. What determines the probability of discovering a species?: a study of South American oscine passerine birds. J. Biogeogr. 22, 7–14. Blangiardo, M., Cameletti, M., 2015. Spatial and Spatio-temporal Bayesian Models with RINLA. John Wiley & Sons, Ltd, Chichester, UK. Blangiardo, M., Cameletti, M., Baio, G., Rue, H., 2013. Spatial and spatio-temporal models with R-INLA. Spat. Spatio-Tempor. Epidemiol 7, 39–55. Böhm, M., Collen, B., Baillie, J.E.M., Bowles, P., Chanson, J., Cox, N., Hammerson, G., Hoffmann, M., Livingstone, S.R., Ram, M., Rhodin, A.G.J., Stuart, S.N., van Dijk, P.P., Young, B.E., Afuang, L.E., Aghasyan, A., Garcia, A., Aguilar, C., Ajtic, R., Akarsu, F., Alencar, L.R.V., Allison, A., Ananjeva, N., Anderson, S., Andren, C., Ariano-Sanchez, D., Arredondo, J.C., Auliya, M., Austin, C.C., Avci, A., Baker, P.J., Barreto-Lima, A.F., Barrio-Amoros, C.L., Basu, D., Bates, M.F., Batistella, A., Bauer, A., Bennett, D., Bohme, W., Broadley, D., Brown, R., Burgess, J., Captain, A., Carreira, S., Castaneda, M.D., Castro, F., Catenazzi, A., Cedeno-Vazquez, J.R., Chapple, D.G., Cheylan, M., CisnerosHeredia, D.F., Cogalniceanu, D., Cogger, H., Corti, C., Costa, G.C., Couper, P.J., Courtney, T., Crnobrnja-Isailovic, J., Crochet, P.A., Crother, B., Cruz, F., Daltry, J.C., Daniels, R.I.R., Das, I., de Silva, A., Diesmos, A.C., Dirksen, L., Doan, T.M., Dodd, C.K., Doody, J.S., Dorcas, M.E., de Barros, J.D., Egan, V.T., El Mouden, E., Embert, D., Espinoza, R.E., Fallabrino, A., Feng, X., Feng, Z.J., Fitzgerald, L., Flores-Villela, O., Franca, F.G.R., Frost, D., Gadsden, H., Gamble, T., Ganesh, S.R., Garcia, M.A., GarciaPerez, J.E., Gatus, J., Gaulke, M., Geniez, P., Georges, A., Gerlach, J., Goldberg, S., Gonzalez, J.C.T., Gower, D.J., Grant, T., Greenbaum, E., Grieco, C., Guo, P., Hamilton, A.M., Hare, K., Hedges, S.B., Heideman, N., Hilton-Taylor, C., Hitchmough, R., Hollingsworth, B., Hutchinson, M., Ineich, I., Iverson, J., Jaksic, F.M., Jenkins, R., Joger, U., Jose, R., Kaska, Y., Kaya, U., Keogh, J.S., Kohler, G., Kuchling, G., Kumlutas, Y., Kwet, A., La Marca, E., Lamar, W., Lane, A., Lardner, B., Latta, C., Latta, G., Lau, M., Lavin, P., Lawson, D., LeBreton, M., Lehr, E., Limpus, D., Lipczynski, N., Lobo, A.S., Lopez-Luna, M.A., Luiselli, L., Lukoschek, V., Lundberg, M., Lymberakis, P., Macey, R., Magnusson, W.E., Mahler, D.L., Malhotra, A., Mariaux, J., Maritz, B., Marques, O.A.V., Marquez, R., Martins, M., Masterson, G., Mateo, J.A., Mathew, R., Mathews, N., Mayer, G., McCranie, J.R., Measey, G.J., Mendoza-Quijano, F., Menegon, M., Metrailler, S., Milton, D.A., Montgomery, C., Morato, S.A.A., Mott, T., Munoz-Alonso, A., Murphy, J., Nguyen, T.Q., Nilson, G., Nogueira, C., Nunez, H., Orlov, N., Ota, H., Ottenwalder, J., Papenfuss, T., Pasachnik, S., Passos, P., Pauwels, O.S.G., PerezBuitrago, N., Perez-Mellado, V., Pianka, E.R., Pleguezuelos, J., Pollock, C., PonceCampos, P., Powell, R., Pupin, F., Diaz, G.E.Q., Radder, R., Ramer, J., Rasmussen, A.R., Raxworthy, C., Reynolds, R., Richman, N., Rico, E.L., Riservato, E., Rivas, G., da Rocha, P.L.B., Rodel, M.O., Schettino, L.R., Roosenburg, W.M., Ross, J.P., Sadek, R., Sanders, K., Santos-Barrera, G., Schleich, H.H., Schmidt, B.R., Schmitz, A., Sharifi, M., Shea, G., Shi, H.T., Shine, R., Sindaco, R., Slimani, T., Somaweera, R., Spawls, S., Stafford, P., Stuebing, R., Sweet, S., Sy, E., Temple, H.J., Tognelli, M.F., Tolley, K., Tolson, P.J., Tuniyev, B., Tuniyev, S., Uzum, N., van Buurt, G., Van Sluys, M., Velasco, A., Vences, M., Vesely, M., Vinke, S., Vinke, T., Vogel, G., Vogrin, M., Vogt, R.C., Wearn, O.R., Werner, Y.L., Whiting, M.J., Wiewandt, T., Wilkinson, J., Wilson, B., Wren, S., Zamin, T., Zhou, K., Zug, G., 2013. The conservation status of the world's reptiles. Biol. Conserv. 157, 372–385. Borcard, D., Legendre, P., Drapeau, P., 1992. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055. Brandley, M.C., Huelsenbeck, J.P., Wiens, J.J., 2008. Rates and patterns in the evolution of snake-like body form in squamate reptiles: evidence for repeated re-evolution of lost digits and long-term persistence of intermediate body forms. Evolution 62, 2042–2064. Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32. Brown, J.H., 1995. Macroecology. University of Chicago Press, Chicago. Brown, J.H., Maurer, B.A., 1989. Macroecology: the division of food and space among species on continents. Science 243, 1145–1150. Burt, W.H., 1943. Territoriality and home range concepts as applied to mammals. J. Mammal. 24, 346–352. Cabrero-Sañudo, F.J., Lobo, J.M., 2003. Estimating the number of species not yet described and their characteristics: the case of western Palaearctic dung beetle species (Coleoptera, Scarabaeoidea). Biodivers. Conserv. 12, 147–166. Castro-Mello, C., 2000. A new species of Amphisbaena from Central Brazil (Squamata: Amphisbaenidae). Pap. Avulsos. Zool. (São Paulo) 41, 243–246. Ceriani, L., Verme, P., 2012. The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini. J. Econ. Inequal. 10, 421–443. Collen, B., Purvis, A., Gittleman, J.L., 2004. Biological correlates of description date in carnivores and primates. J. Biogeogr. 13, 459–467. Colli, G.R., Zamboni, D.S., 1999. Ecology of the worm-lizard Amphisbaena alba in the Cerrado of central Brazil. Copeia 1999, 733–742. Colli, G.R., Bastos, R.P., Araújo, A.F.B., 2002. The character and dynamics of the Cerrado herpetofauna. In: Oliveira, P.S., Marquis, R.J. (Eds.), The Cerrados of Brazil: Ecology and Natural History of a Neotropical Savanna. Columbia University Press, New York, pp. 223–241. Core Team, R., 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Costa, H.C., Bérnils, R.S., 2014. Répteis brasileiros: Lista de espécies. Herpetol. Bras. 3, 74–84. Costa, H.C., Resende, F.C., Teixeira Jr., M., Dal Vechio, F., Clemente, C.A., 2015. A new Amphisbaena (Squamata: Amphisbaenidae) from southern Espinhaço Range, southeastern Brazil. An. Acad. Bras. Cienc. 87, 891–901. Deng, H., 2013. Guided Random Forest in the RRF Package. arXiv 1306.0237v1. pp. 1–2. Devictor, V., Whittaker, R.J., Beltrame, C., 2010. Beyond scarcity: citizen science programmes as useful tools for conservation biogeography. Divers. Distrib. 16, 354–362. Diniz-Filho, J.A.F., Bastos, R.P., Rangel, T.F.L.V.B., Bini, L.M., Carvalho, P., Silva, R.J., 2005. Macroecological correlates and spatial patterns of anuran description dates in the Brazilian Cerrado. Glob. Ecol. Biogeogr. 14, 469–477.

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx Domingos, F.M.C.B., Bosque, R.J., Cassimiro, J., Colli, G.R., Rodrigues, M.T., Santos, M.G., Beheregaray, L.B., 2014. Out of the deep: cryptic speciation in a Neotropical gecko (Squamata, Phyllodactylidae) revealed by species delimitation methods. Mol. Phylogenet. Evol. 80, 113–124. Durigan, G., Melo, A.C.G., Brewer, J.S., 2012. The root to shoot ratio of trees from open- and closed-canopy cerrado in south-eastern Brazil. Plant Ecol. Divers 5, 333–343. Elith, J., Leathwick, J.R., 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. ESRI, 2011. ArcGis Desktop: Release 10. Environmental Systems Research Institute, Redlands, CA. Françoso, R.D., Brandão, R., Nogueira, C.C., Salmona, Y.B., Machado, R.B., Colli, G.R., 2015. Habitat loss and the effectiveness of protected areas in the Cerrado Biodiversity Hotspot. Nat. Conserv. 13, 35–40. Funk, V.A., Richardson, K.S., Ferrier, S., 2005. Survey-gap analysis in expeditionary research: where do we go from here? Biol. J. Linn. Soc. 85, 549–567. Gans, C., 1967. A check list of recent amphisbaenians (Amphisbaenia, Reptilia). Bull. Am. Mus. Nat. Hist. 135, 61–105. Gans, C., 1975. Tetrapod limblessness: evolution and functional corollaries. Am. Zool. 15, 455–467. Gans, C., 1978. The characteristics and affinities of the Amphisbaenia. Trans. Zool. Soc. London 34, 347–416. Gans, C., 2005. Checklist and bibliography of the Amphisbaenia of the world. Bull. Am. Mus. Nat. Hist. 7-130. Gaston, K.J., 1991. Body size and probability of description: the beetle fauna of Britain. Ecol. Entomol. 16, 505–508. Gaston, K.J., Blackburn, T.M., 2000. Pattern and Process in Macroecology. Blackwell Science Ltd., Oxford. Gaston, K.J., Blackburn, T.M., Loder, N., 1995. Which species are described first? The case of North American butterflies. Biodivers. Conserv. 4, 119–127. Griffith, D.A., 2003. Spatial Autocorrelation and Spatial Filtering: Gaining Understanding Through Theory and Scientific Visualization. Springer-Verlag, Berlin Heidelberg. Guarnizo, C.E., Werneck, F.P., Giugliano, L.G., Santos, M.G., Fenker, J., Sousa, L., D'Angiolella, A.B., dos Santos, A.R., Strussmann, C., Rodrigues, M.T., Dorado-Rodrigues, T.F., Gamble, T., Colli, G.R., 2016. Cryptic lineages and diversification of an endemic anole lizard (Squamata, Dactyloidae) of the Cerrado hotspot. Mol. Phylogenet. Evol. 94, 279–289. Harcourt, A.H., 2006. Rarity in the tropics: biogeography and macroecology of the primates. J. Biogeogr. 33, 2077–2087. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. Hoffmann, M., Hilton-Taylor, C., Angulo, A., Bohm, M., Brooks, T.M., Butchart, S.H.M., Carpenter, K.E., Chanson, J., Collen, B., Cox, N.A., Darwall, W.R.T., Dulvy, N.K., Harrison, L.R., Katariya, V., Pollock, C.M., Quader, S., Richman, N.I., Rodrigues, A.S.L., Tognelli, M.F., Vie, J.C., Aguiar, J.M., Allen, D.J., Allen, G.R., Amori, G., Ananjeva, N.B., Andreone, F., Andrew, P., Ortiz, A.L.A., Baillie, J.E.M., Baldi, R., Bell, B.D., Biju, S.D., Bird, J.P., Black-Decima, P., Blanc, J.J., Bolanos, F., Bolivar, W., Burfield, I.J., Burton, J.A., Capper, D.R., Castro, F., Catullo, G., Cavanagh, R.D., Channing, A., Chao, N.L., Chenery, A.M., Chiozza, F., Clausnitzer, V., Collar, N.J., Collett, L.C., Collette, B.B., Fernandez, C.F.C., Craig, M.T., Crosby, M.J., Cumberlidge, N., Cuttelod, A., Derocher, A.E., Diesmos, A.C., Donaldson, J.S., Duckworth, J.W., Dutson, G., Dutta, S.K., Emslie, R.H., Farjon, A., Fowler, S., Freyhof, J., Garshelis, D.L., Gerlach, J., Gower, D.J., Grant, T.D., Hammerson, G.A., Harris, R.B., Heaney, L.R., Hedges, S.B., Hero, J.M., Hughes, B., Hussain, S.A., Icochea, J., Inger, R.F., Ishii, N., Iskandar, D.T., Jenkins, R.K.B., Kaneko, Y., Kottelat, M., Kovacs, K.M., Kuzmin, S.L., La Marca, E., Lamoreux, J.F., Lau, M.W.N., Lavilla, E.O., Leus, K., Lewison, R.L., Lichtenstein, G., Livingstone, S.R., Lukoschek, V., Mallon, D.P., McGowan, P.J.K., McIvor, A., Moehlman, P.D., Molur, S., Alonso, A.M., Musick, J.A., Nowell, K., Nussbaum, R.A., Olech, W., Orlov, N.L., Papenfuss, T.J., ParraOlea, G., Perrin, W.F., Polidoro, B.A., Pourkazemi, M., Racey, P.A., Ragle, J.S., Ram, M., Rathbun, G., Reynolds, R.P., Rhodin, A.G.J., Richards, S.J., Rodriguez, L.O., Ron, S.R., Rondinini, C., Rylands, A.B., de Mitcheson, Y.S., Sanciangco, J.C., Sanders, K.L., Santos-Barrera, G., Schipper, J., Self-Sullivan, C., Shi, Y.C., Shoemaker, A., Short, F.T., Sillero-Zubiri, C., Silvano, D.L., Smith, K.G., Smith, A.T., Snoeks, J., Stattersfield, A.J., Symes, A.J., Taber, A.B., Talukdar, B.K., Temple, H.J., Timmins, R., Tobias, J.A., Tsytsulina, K., Tweddle, D., Ubeda, C., Valenti, S.V., van Dijk, P.P., Veiga, L.M., Veloso, A., Wege, D.C., Wilkinson, M., Williamson, E.A., Xie, F., Young, B.E., Akcakaya, H.R., Bennun, L., Blackburn, T.M., Boitani, L., Dublin, H.T., da Fonseca, G.A.B., Gascon, C., Lacher, T.E., Mace, G.M., Mainka, S.A., McNeely, J.A., Mittermeier, R.A., Reid, G.M., Rodriguez, J.P., Rosenberg, A.A., Samways, M.J., Smart, J., Stein, B.A., Stuart, S.N., 2010. The impact of conservation on the status of the world's vertebrates. Science 330, 1503–1509. Hoogmoed, M.S., Pinto, R.R., da Rocha, W.A., Pereira, E.G., 2009. A new species of Mesobaena Mertens, 1925 (Squamata: Amphisbaenidae) from Brazilian Guiana, with a key to the Amphisbaenidae of the Guianan region. Herpetologica 65, 436–448. Howard, S.D., Bickford, D.P., 2014. Amphibians over the edge: silent extinction risk of Data Deficient species. Divers. Distrib. 20, 837–846. IUCN, 2016. IUCN red list of threatened species. Version 2016-1. http://www.iucnredlist. org (accessed 09 July 2016). IUCN Standards and Petitions Subcommittee, 2016. Guidelines for Using the IUCN Red List Categories and Criteria. Version 12. Prepared by the Standards and Petitions Subcommittee. Downloadable from http://jr.iucnredlist.org/documents/RedListGuidelines. pdf. Jepson, W., Brannstrom, C., Filippi, A., 2010. Access regimes and regional land change in the Brazilian Cerrado, 1972–2002. Ann. Assoc. Am. Geogr. 100, 87–111. Kearney, M., Stuart, B.L., 2004. Repeated evolution of limblessness and digging heads in worm lizards revealed by DNA from old bones. Proc. R. Soc. Lond. Ser. B-Biol. Sci. 271, 1677–1683.

11

Klink, C.A., Moreira, A.G., 2002. Past and current human occupation, and land use. In: Oliveira, P.S., Marquis, R.J. (Eds.), The Cerrados of Brazil. Ecology and Natural History of a Neotropical Savanna. Columbia University Press, New York, pp. 69–88. Legendre, P., Legendre, L., 1998. Numerical Ecology. second ed. Elsevier, Amsterdan. Liaw, A., Wiener, M., 2002. Classification and regression by randomForest. R News 2, 18–22. Lomolino, M.V., 2004. Conservation biogeography. In: Lomolino, M.V., Heaney, L.R. (Eds.), Frontiers of Biogeography: New Directions in the Geography of Nature. Sinauer Associates, Sunderland, Massachusetts, pp. 293–294. Longrich, N.R., Vinther, J., Pyron, R.A., Pisani, D., Gauthier, J.A., 2015. Biogeography of worm lizards (Amphisbaenia) driven by end-Cretaceous mass extinction. Proc. R. Soc. Lond. Ser. B-Biol. Sci. 282, 20143034. Martin, T.G., Wintle, B.A., Rhodes, J.R., Kuhnert, P.M., Field, S.A., Low-Choy, S.J., Tyre, A.J., Possingham, H.P., 2005. Zero tolerance ecology: improving ecological inference by modelling the source of zero observations. Ecol. Lett. 8, 1235–1246. Meiri, S., 2008. Evolution and ecology of lizard body sizes. Glob. Ecol. Biogeogr. 17, 724–734. de Miranda, S.D., Bustamante, M., Palace, M., Hagen, S., Keller, M., Ferreira, L.G., 2014. Regional variations in biomass distribution in Brazilian savanna woodland. Biotropica 46, 125–138. Morais, A.R., Siqueira, M.N., Lemes, P., Maciel, N.M., De Marco, P., Brito, D., 2013. Unraveling the conservation status of Data Deficient species. Biol. Conserv. 166, 98–102. Motta, P.E.F., Curi, N., Franzmeier, D.P., 2002. Relation of soils and geomorphic surfaces in the Brazilian Cerrado. In: Oliveira, P.S., Marquis, R.J. (Eds.), The Cerrados of Brazil. Ecology and Natural History of a Neotropical Savanna. Columbia University Press, New York, pp. 13–32. Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B., Kent, J., 2000. Biodiversity hotspots for conservation priorities. Nature 403, 853–858. Navas, C.A., Antoniazzi, M.M., Carvalho, J.E., Chaui-Berlink, J.G., James, R.S., Jared, C., Kohlsdorf, T., Dal Pai-Silva, M., Wilson, R.S., 2004. Morphological and physiological specialization for digging in amphisbaenians, an ancient lineage of fossorial vertebrates. J. Exp. Biol. 207, 2433–2441. Nelson, B.W., Ferreira, C.A.C., Dasilva, M.F., Kawasaki, M.L., 1990. Endemism centres, refugia and botanical collection density in Brazilian Amazonia. Nature 345, 714–716. Papenfuss, T.J., 1982. The ecology and systematics of the amphisbaenian genus Bipes. Occas. Pap. Calif. Acad. Sci. 136, 1–42. Pettit, L., 1990. The conditional predictive ordinate for the Normal distribution. J. R. Stat. Soc. Ser. B-Stat. Methodol. 56, 3–48. Quinn, G.P., Keough, M.J., 2002. Experimental Design and Data Analysis for Biologists. Cambridge University Press, Cambridge. Reed, R.N., Boback, S.M., 2002. Does body size predict dates of species description among North American and Australian reptiles and amphibians? Glob. Ecol. Biogeogr. 11, 41–47. Ribeiro, S., Castro-Mello, C., Nogueira, C., 2009. New species of Anops Bell, 1833 (Squamata, Amphisbaenia) from Jalapão region in the Brazilian Cerrado. J. Herpetol. 43, 21–28. Ribeiro, S., Nogueira, C., Cintra, C.E.D., Silva, N.J., Zaher, H., 2011. Description of a new pored Leposternon (Squamata, Amphisbaenidae) from the Brazilian Cerrado. S. Am. J. Herpetol. 6, 177–188. Rodrigues, M.T., 1996. Lizards, snakes, and amphisbaenians from the Quaternary sand dunes of the middle Rio São Francisco, Bahia, Brazil. J. Herpetol. 30, 513–523. Rodrigues, M.T., 2003. Herpetofauna da Caatinga. In: Leal, I.R., Tabarelli, M., Da Silva, J.M.C. (Eds.), Ecologia e Conservação da Caatinga. Ed. Universitária da UFPE, Recife, Pernambuco, pp. 181–236. Rodrigues, M.T., 2005. The conservation of Brazilian reptiles: challenges for a megadiverse country. Conserv. Biol. 19, 659–664. Sánchez-Fernández, D., Lobo, J.M., Abellán, P., Ribera, I., Millán, A., 2008. Bias in freshwater biodiversity sampling: the case of Iberian water beetles. Divers. Distrib. 14, 754–762. Sousa-Baena, M.S., Garcia, L.C., Peterson, A.T., 2014. Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory. Divers. Distrib. 20, 369–381. Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A., 2002. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B-Stat. Methodol. 64, 583–639. Strüssmann, C., Mott, T., 2009. Sympatric amphisbaenids from Manso Dam region, Mato Grosso state, western Brazil, with the description of a new two-pored species of Amphisbaena (Squamata, Amphisbaenidae). Stud. Neotropical Fauna Environ. 44, 37–46. Trindade, J., de Carvalho, R.A., Brito, D., Loyola, R.D., 2012. How does the inclusion of Data Deficient species change conservation priorities for amphibians in the Atlantic Forest? Biodivers. Conserv. 21, 2709–2718. Uetz, P., Goll, J., Hallermann, J., 2007. Die TIGR-Reptiliendatenbank. Elaphe 15, 22–25. Vanzolini, P.E., 1991a. Two further new species of Amphisbaena from the semi-arid northeast of Brasil (Reptilia, Amphisbaenia). Pap. Avulsos. Zool. (São Paulo) 37, 347–361. Vanzolini, P.E., 1991b. Two new small species of Amphisbaena from the fossil dune field of the middle Rio São Francisco, state of Bahia, Brasil (Reptilia, Amphisbaenia). Pap. Avulsos. Zool. (São Paulo) 37, 259–276. Vanzolini, P.E., 1996. On slender species of Amphisbaena, with the description of a new one from northeastern Brasil (Reptilia, Amphisbaenia). Pap. Avulsos. Zool. (São Paulo) 39, 293–305. Vanzolini, P.E., 1997. The silvestrii species group of Amphisbaena, with the description of two new Brasilian species (Reptilia: Amphisbaenia). Pap. Avulsos. Zool. (São Paulo) 40, 65–85. Vanzolini, P.E., 2002. An aid to the identification of the South American species of Amphisbaena (Squamata, Amphisbaenidae). Pap. Avulsos. Zool. (São Paulo) 42, 351–362. Vilela, B., Villalobos, F., Rodriguez, M.A., Terribile, L.C., 2014. Body size, extinction risk and knowledge bias in New World snakes. PLoS One 9, e113429.

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033

12

G.R. Colli et al. / Biological Conservation xxx (2016) xxx–xxx

Webb, J.K., Shine, R., Branch, W.R., Harlow, P.S., 2000. Life underground: food habits and reproductive biology of two amphisbaenian species from southern Africa. J. Herpetol. 34, 510–516. Werneck, F.P., Gamble, T., Colli, G.R., Rodrigues, M.T., Sites, J.W., 2012. Deep diversification and long-term persistence in the South American ‘dry diagonal’: integrating

continent-wide phylogeography and distribution modeling of geckos. Evolution 66, 3014–3034. Willmott, C.J., Matsuura, K., 2001. Terrestrial water budget data archive: monthly time series (1950–1999). http://www.sage.wisc.edu (accessed 06 April 2014).

Please cite this article as: Colli, G.R., et al., In the depths of obscurity: Knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae), Biological Conservation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.033