Abstract
Using the distributional information from approximately 22,000 georeferenced records of the 53 currently recognized species of the genus
Introduction
Catalogues of species names and atlases of distributions are essential to describe and understand the patterns of biological diversity [1]. Unfortunately, almost-universal biases and incompleteness in the available distributional information on invertebrate species [2–4] hinder the reliability and usefulness of this information. In the case of distributional information, several methodological procedures seek to derive geographical representations of “real” or “potential” distributions from partial chorological data and different environmental predictors [5]. These so-called species distribution models (SDMs) or ecological niche models (ENM) have used the exponentially growing number of massive biological and environmental databases that are freely available on the World Wide Web [6, 7], aided by increasing computational and statistical capacity [8]. The modeling procedures use different conceptual and methodological approaches. We highlight those approaches that use the available information on species occurrence to determine the environmental conditions prevailing where the species are present (the species' Grinnellian niches [9]) in order to predict the suitable region (“potential distribution” [10]). Because these potential distributions are partial geographical representations of localities with climatic conditions similar to those where the species of interest are known to occur, these simulations are useful for designing future surveys to improve biogeographical and taxonomic knowledge [11–17].
The genus
Methods
Information from the Musée Canadien de la Nature (CMNC) database was the primary source of distributional and taxonomic information [22]. This information was complemented by data from other museum specimen labels (see Appendix 1) and bibliographic sources (see Appendix 2). All of this information was included in the MANTIS v. 2.0 [23] database system. The resulting database currently includes approximately 22,000 records belonging to the 53 currently recognized
The potential distributions were estimated with a multidimensional envelope procedure according to the protocol recently proposed by Jiménez-Valverde
The 19 freely available bioclimatic variables provided by WorldClim (see www.worldclim.org [27].) obtained by the interpolation of climate-station records from 1950 to 2000 (for details, see [7]) were used as predictors. As the number of used predictors may condition the obtained potential representation, most relevant predictors were selected by using Ecological Niche Factor Analysis (ENFA; see [28]), which is a method firmly rooted in the niche concept [29]. ENFA computes new uncorrelated factors by comparing climatic data in presence locations with conditions in the entire study area. This procedure serves to maximize both the marginality (the distance between the optimum recorded for the species and the average climatic conditions in the area) and the specialization values of the species (the ratio between the climatic variability in the study area and that existing at the points where the selected species occurs). The factors were retained based on their eigenvalues relative to a broken-stick distribution [28]. The original bioclimatic variables selected for each species are those that show the highest correlation values with these retained ENFA factors (absolute factor scores > 0.30). After the most relevant bioclimatic variables were selected individually for each species, we calculated the maximum and minimum scores (the extreme bioclimatic values) in all observed presence cells. All of the cells with climatic values falling within that range were selected as suitable to derive a binary potential distributional hypothesis. This procedure assumes that the recorded occurrences provide a reliable representation of the full spectrum of climatic conditions in which the species can survive and reproduce [10, 30].
Based on the ENFA analyses, the two most relevant bioclimatic variables were used to obtain further estimates. Specifically, the maximum and minimum scores for these variables in the observed presence cells were used to estimate the bioclimatic tolerance of species (maximum-minimum), as well as their averaged conditions (maximum-minimum/2 + minimum). The correlation between the tolerance and the average bioclimatic value was determined with a Spearman rank correlation coefficient (
Multidimensional envelopes were obtained for each species. The potential distributions obtained from these analyses were overlaid to obtain a map of the potential species richness. This map showed a geographical picture that represented the capacity of a locality to harbor suitable climatic conditions without considering the limits imposed by contingent forces such as biotic factors or dispersal limitations. For the four species occurring in three or fewer 0.08° cells, multidimensional envelopes were constructed by increasing by 10% the minimum and maximum climatic values found at the occurrence points for the five most relevant variables (see Appendix 3). The location of the available database records was superimposed on this potential richness map to identify those potentially diverse regions that were not sufficiently surveyed. As we develop geographical representations of potential distributions, comparing the derived potential species richness map to current knowledge of realized distributions suggest that some climatically suitable areas may be uninhabited due to dispersal limitations (as in the case of Caribbean islands or northern zones above the Isthmus of Tehuantepec). However, when the relevance of these dispersal limitations decreases, as in the case of tropical South American areas, we assume that the comparison of the obtained potential species map with the distribution survey may provide useful information on the location of unrecorded places.
Results
The bioclimatic variables that most frequently showed statistically significant correlations with the retained ENFA factors are the annual mean temperature (in 96% of the species), the annual precipitation (in 43% of the species) and the mean diurnal range of temperature (in 51% of the species). Only two other bioclimatic variables appear in at least 20% of the species: the maximum temperature of the warmest month and the annual range of temperature (Appendix 3). ENFA analyses also indicate that the different factors selected generally explain more than 97% of the total species information. The marginality values range between 0.37 and 3.30 (mean ± standard deviation; 1.17 ± 0.70) and the vast majority of species show high values of specialization (Appendix 3).
The tolerances of

Relationship between the averaged annual mean temperature and annual precipitation values of the 0.08º presence cells for each species (open circles) and the tolerance values (maximum-minimum) represented by whisker plots. Only those species with at least five occurrence cells are represented. The species (X-axes) are ordered according to their average temperature and precipitation values.
From the 1,081 possible pairwise estimates of the shared climatic area for the 47 species considered, in only 61 instances (5.6 %) does a pair of species share more than 50% of the total estimated climatic area. The average percentage of overlap was 15.4 ± 15.9% (mean ± standard deviation).
The sum of the individual potential distributions indicates that the highest values of potential species richness for

(A) Potential species richness map for
Discussion
Temperature-related variables appear to play the most important role in explaining American
While the derived climatic optimum of
The correlation between species tolerance values for the two most important climatic variables suggests that the occurrence of these species in a broad range of temperature conditions is associated with their occurrence in a broad range of precipitation values. Therefore, we infer that a broader climatic niche in
As in other groups [31], dispersal constraints and historical factors seem to have played a determining role in the conformation of current American

(A) Relationship between the averaged annual mean temperature and annual precipitation values in the 0.08º presence cells for each species. Histograms on top and right-hand side show the frequency of these variables. (B) The same relationship plotted with circles whose sizes are linearly related to the climatic area covered by each species according to the range of temperature and precipitation values associated with that species. The map represents those areas suitable for any
Implications Implications for conservation
Distribution models can be applied in conservation when we are able to generate reliable predictions of realized distributions that can be coupled with reserve selection algorithms. Unfortunately, the lack of reliable absence information seriously hinders this approach [25]. When only partial representations of the potential distribution can be estimated (as in this case and in most exercises that use invertebrate data), the obtained simulations may help to delimit those areas that are potentially rich in species but not yet adequately surveyed [32]. In the future, new species may even be found in these areas [11, 12], allowing to generate more accurate distributional maps and a better taxonomical knowledge that can be subsequently used for conservation purposes.
According to our results, many of the areas with greater potential richness values (upper quartile > 19 species) are located in Central America, extending from the Yucatán Peninsula to the Panama Canal. As a substantial number of database records exist for this area, we suspect that most species that potentially inhabit this region are in fact absent due to dispersal limitation. In South America, the areas with the greatest potential richness generally correspond to lowland zones: inter-Andean valleys, Andean lowlands, the western, central and southern regions of the Amazon and lowlands of the Guyana Shield. However, only a few of these areas have a substantial number of records: the upper Amazon at the frontier between Perú and Bolivia, the Amazon region of Leticia in the north of Perú and the south of Colombia, as well as the Andean region in northern Ecuador. The remaining climatically favorable areas include few records.
These findings suggest that future collection surveys aimed at improving the present taxonomic and biogeographical knowledge of Neotropical
Footnotes
Acknowledgments
The authors thank the Departamento de Biología and Facultad de Ciencias of Universidad Nacional de Colombia, as well as Museo Nacional de Ciencias Naturales de Madrid for facilitating this work. We particularly thank François Génier at Musée Canadien de la Nature, Gatineau - Canada (CMNC) for allowing us to use all of the information regarding Neotropical
Appendix 1
ABTS: Personal Collection. Andrew B. T. Smith, Kanata-Canada.
AFIC: Personal Collection. Adrian Forsyth, Washington D.C.-U.S.A.
BCRC: Personal Collection. Brett C. Ratcliffe, Lincoln-U.S.A.
BDGC: Personal Collection. Bruce D. Gill, Ottawa-Canada.
BMNH: Natural History Museum. London-United kingdom.
CECR: Personal Collection. Edgar Camero-R., Bogotá D.C.-Colombia.
CJAN: Personal Collection. Jorge Ari Noriega, Bogotá D.C.-Colombia.
CLCPL: Personal Collection. Luis Carlos Pardo-Locarno, Palmira-Colombia.
CMNC: Musée Canadien de la Nature, Ottawa-Canada
CMNH: Carnegie Museum of Natural History, Pittsburgh-U.S.A.
CNC: Agriculture et Agroalimentaire Canada, Ottawa-Canada.
CPALT: Personal Collection. Alejandro Lopera Toro, Bogotá D.C.-Colombia.
CPBM: Personal Collection. Betselene Murcia, Florencia-Colombia.
CPWY: Personal Collection. William Yara, Bogotá D.C.-Colombia.
DGMF: Personal Collection. David G. Marqua, Fort Davis-U.S.A.
EGRC: Personal Collection. Edward G. Riley, College Station-U.S.A.
FSCA: Museum of Entomology, Florida State Collection of Arthropods, Gainesville-U.S.A.
FVMC: Personal Collection. Fernando Z. Vaz de Mello, Cuibá-Brasil.
IAvH: Instituto de Investigación de Recursos Biológicos “Alexander von Humboldt”, Villa de Leyva-Colombia.
IBSP: Colección Entomológica “Adolph Hempel”, Instituto Biológico Sao Paulo, Sao Paulo-Brasil.
ICN-MHN: Instituto de Ciencias Naturales, Bogotá D.C.-Colombia.
INBio: Instituto Nacional de Biodiversidad, Santo Domingo de Heredia-Costa Rica.
MACN: Museo Argentino de Ciencias Naturales, Buenos Aires-Argentina.
MHIC: Personal Collection. Martin Hardy, Québec-Canada.
MHNG: Muséum d'Histoire Naturelle de la Ville de Genève, Genève-Switzerland.
MIZA: Museo del Instituto de Zoología Agrícola de la Universidad Central de Venezuela, Maracay-Venezuela.
MLJC: Personal Collection. Mary Liz Jameson, Lincoln-U.S.A.
MLP: Museo de Historia Natural de La Plata, La Plata-Argentina.
MNHN: Muséum National d'Histoire Naturelle, Paris-France.
MNRJ: Museu Nacional Rio de Janeiro, Rio de Janeiro-Brasil.
MTD: Staatliches Museum Für Tierkunde, Dresden-Germany.
MZSP: Museu de Zoologia Universidade de São Paulo, Sao Paulo-Brasil.
NHRS: Naturhistoriska Riksmuseet, Stockholm-Sweden.
NMPC: Národni Muzeum, Prague-Czech Republic.
OUMNH: Oxford University Museum of Natural History, Oxford-United Kingdom.
PAIC: Personal Collection. Patrick Arnaud, Saintry sur Seine-Francia.
PMOC: Personal Collection. Philippe Moretto, Toulon-Francia.
PSIC: Personal Collection. Paul Schoolmeesters, Herent-Bélgica.
QCAZ: Museo de Zoología Universidad Católica del Ecuador, Quito-Ecuador.
ROME: Royal Ontario Museum, Toronto-Canada.
SEMC: Snow Entomological Museum, Lawrence-U.S.A.
SMF: Forschungsinstitut Senckenberg, Francfort-Germany.
TAMU: Texas A & M University, College Station-U.S.A.
UASC: Museo de Historia Natural “Noel Kempff Mercado”, Santa Cruz-Bolivia.
UNSM: University of Nebraska State Museum, Lincoln-U.S.A.
USNM: National Museum of Natural History, Washington D.C-U.S.A.
UVGC: Colección de la Universidad del Valle de Guatemala, Guatemala-Guatemala.
VMPC: Personal Collection. Vladislav Malý, Praga-República Checa.
WBWC: Personal Collection. William B. Warner, Phoenix-U.S.A.
WDEC: Personal Collection. W. David Edmonds, Marfa-U.S.A.
ZMHB: Museum für Naturkunde, Berlin-Germany.
Appendix 2
Appendix 3
