Project no. 018340
Project acronym: EDIT
Project title: Toward the European Distributed Institute of
Taxonomy
Instrument: Network of Excellence
Thematic Priority: Sub-Priority 1.1.6.3: “Global Change and Ecosystems”
Deliverable 5.35: Predictive
distribution modelling report
Deliverable 5.38: Gap analysis in local
inventories report
Due date of deliverable: Month 21 & 25
Actual submission date: Month 16
Start date of project: 01/03/2006
years
Duration: 5
Organisation name of lead contractor for this deliverable: CSIC – MINCN 4
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)
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PU
PP
RE
CO
Dissemination Level ( “X” in the relevant box)
Public
Restricted to other programme participants (including the Commission Services)
Restricted to a group specified by the consortium (including the Commission Services)
Confidential, only for members of the consortium (including the Commission Services)
X
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Do we need to estimate inventory completeness?
Utility and drawbacks of modelling techniques for biodiversity
databases mining
(A document for the debate between the community interested in the application of
current taxonomic and distributional data)
Taxonomic and distribution information shall be reliable
The conservation of biodiversity is an international goal. It needs a joint cooperation effort to put together all the information on biodiversity at hand to assess
conservation priorities according to current status of biodiversity and current and potential
threats. To do this, a number of taxonomic and biogeographic database programs are being
developed and merging in Biodiversity Information Networks (herein, BIN) (Soberón et al.
1996; Bisby, 2000; Saarenmaa & Nielsen, 2002; Godfray, 2002; GBIF 2003; Graham et al.,
2004; Hortal et al., 2007; Guralnick et al., 2007). All these initiatives are gathering all the
taxonomic and biogeographic information stored during more than two centuries in the
bibliography and natural history collections.
Conservation and research on biodiversity need to be based in a good knowledge of
the taxonomy and distribution of fauna and flora, which constituted the so called
taxonomic and biogeographic quality standards in biodiversity databases jargon. However,
we are far from knowing the whole catalogue of life, not to talk about the distribution of all
these biological diversity. In the countries with a long naturalist tradition survey effort has
been intense but little coordinated, generating a huge amount of taxonomic and
chorological data dispersed in the bibliography and natural history collections. On the
contrary, there is not enough taxonomic and biogeographical information about most part
of world’s richest regions. Due to this, well-known groups and exhaustively inventoried
areas are the exception rather than the rule, in spite of the mass amount of data being
currently gathered; for most part of the biota and regions, there exist only a few local
inventories and some sporadic uncoordinated captures. This is especially true for
hyperdiverse groups of organisms such as invertebrates (more than 80% of global species
richness), and for the severely threatened and species rich global hotspot areas.
Conservation biologists face a conflict of interests; on the one hand, we need
biological information for basic and applied studies; on the other, we need to be cautious
due to the lack of quality and incompleteness of most of this information. Additional
surveys directed to fill in all gaps in knowledge are not a plausible option, since recovering
a reliable picture of the distribution for highly diverse taxa is a colossal task, which is
3
hampered by limited time and resources (in terms of both manpower and financial
support). Rather, alternatives to assess the reliability and/or enhance the information
currently stored in biodiversity databases are preferred (Hortal et al., 2007; Guralnick et al.,
2007). Within such framework, there are a number of questions to answer before
universalizing the direct use of the information from BIN such as GBIF: Are the obtained
representations of the species geographic ranges reliable and comparable? What is the
effect of the spatial and temporal variation in collection effort on these representations? Is
it possible to identify localities/areas with reliable inventories? Where are these well
surveyed localities located? Are they environmentally or spatially biased? What is the effect
of these biases in the picture of biodiversity offered by current data? Can we use such
partial (and frequently biased) biological information to create reliable predictive maps of
species distributions? Answering these questions and overcoming the problems they
highlight are central for the good use of the biological information currently available.
Bias in taxonomic and distribution information
The process of compiling information on species distributions presents a marked
pattern of spatial bias and survey incompleteness (Dennis & Hardy, 1999; Dennis et al.,
1999; Soberón et al., 2000; Lobo et al., 2007). Species location data suffers from the
temporal bias in the recording process: distribution data collected at different times
generally results in different distribution maps for the same species (Lobo et al., 2007). Such
spatially and environmental structured increase in species data over time is the consequence
of an expansion of the species range resulting from sociological, environmental, or
sampling effort bias (Dennis & Hardy, 1999; Dennis et al., 1999; Dennis & Thomas, 2000;
Dennis et al., 2006; Zaniewski et al., 2002; Anderson, 2003; Reutter et al., 2003; Graham et
al., 2004; Soberón & Peterson, 2004; Martínez-Meyer, 2005), in which species occurring in
those places with a prolonged taxonomical tradition have more possibilities of being
discovered than the remaining ones, generally located in southern more species rich regions
(Cabrero & Lobo, 2003). Due to this, site accessibility, distance from the place of residence
of the collecting taxonomist, or degree of interest to the naturalist, are all key parameters
leading to the observed concentration in species presences (Nelson et al., 1990; Peterson et
al., 1998; Dennis & Thomas, 2000; Parnell et al., 2003; Reddy & Dávalos, 2003). Hence, the
efficiency of biodiversity selection procedures for conservation purposes can be highly
4
conditioned by the quality and spatial bias of the used biological information (Nelson et al.,
1990; Faith, 2002; Williams et al., 2002; Hortal & Lobo, 2006; Hortal et al., 2007).
Having the bias in biodiversity data and the rest of above mentioned questions in
mind, some methods to improve biodiversity information have been developed. For
example, a three step biodiversity assessment framework for conservation has been
suggested, comprising surveys, interpolation of biodiversity distribution through predictive
modelling and reserve selection techniques (Austin 1998; Margules & Pressey 2000; Ferrier
2002; Ferrier et al. 2002a, b). Conservation strategies arisen from such framework can be
successful if they are based in good-quality information. Here, modelling techniques are
believed to overcome bias in biodiversity information; they are supposed to offer the
biodiversity information required by biologists and policy makers easily and quickly
(Guisan & Zimmermann, 2000; Guisan & Thuiller, 2005; Elith et al., 2006; Araújo &
Guisan, 2006). However, if the biological information used presents important
environmental and/or geographic biases, the predictive maps obtained could present
important drawbacks (Hortal & Lobo, 2006; Hortal et al., 2007).
Guralnick et al. (2007) promote the use of GBIF data and some of the
bioinformatic tools available to overcome the gaps in biological data. These methods
represent the first waves of biodiversity informatics (Soberón & Peterson, 2004), and
provide a number of good options to compensate for the inaccuracies of biodiversity data.
However, we believe that current limitations and biases of biodiversity data might
compromise their use. Therefore, we here argue that these problems can be diminished if
(i) more attention is paid to the quality of the information used, assessing its reliability, (ii) a
few additional well-planned surveys are eventually carried out to eliminate the undesired
effects of environmental and geographic bias, and (iii) some enhancements are included in
the data used to produce predictive models. A comprehensive protocol integrating these
three aspects is available elsewhere (Hortal et al., 2007), but here we make an overview of
them.
Identifying well surveyed territories
Adequately sampled areas host reliable inventories of the species assemblage, and
can be used to assess the absence of species to improve predictive models of species
distributions. The methods developed to study inventory processes allow identifying well
surveyed areas, provided that a sampling effort measure is available. Species Accumulation
5
Curves (also known as Collector’s Curves) plot the number of species added to the
inventory as sampling effort increases (Soberón & Llorente, 1993; Colwell & Coddington,
1994; Fagan & Kareiva, 1997; León-Cortés et al., 1998; Moreno & Halffter, 2000; Gotelli &
Colwell, 2001). Since the more the sampling effort is carried out, the more species are
captured, capturing new species gets progressively harder. Due to this, these curves show
an asymptotic behaviour, being the asymptote the actual number of species in the studied
area (i.e., the maximum number of species that can be captured there). This relationship is
fitted to an asymptotic function, which relates the species accumulation with the sampling
effort carried out (Soberón & Llorente, 1993).
Species accumulation curves have been used as a tool to estimate species richness
by extrapolating the function found to its asymptote, although their utility for this task is
still being debated (e.g., Colwell & Coddington, 1994; Gotelli & Colwell, 2001; Willott,
2001; Moreno & Halffter, 2001; Hortal et al. 2004, 2006). However, it is well known that
they are very useful to describe the current rate of finding new species for the inventory
(Soberón & Llorente, 1993; Hortal & Lobo, 2005; Soberón et al., 2007). The slope of the
species accumulation curve describes such rate, and can be used as a measure of survey
completeness at each moment of the survey (Hortal et al., 2004; Hortal & Lobo, 2005): the
higher the slope of the curve, the greater the sampling effort necessary to obtain a good
inventory. Conversely, the smaller the slope, the higher the completeness of the survey,
since an important amount of additional sampling effort, would not add significant
numbers of species to the inventory. If the slope is small, but not zero, it can be assumed
that the species missing from current inventory are locally rare or vagrants (see Moreno &
Halffter, 2000), and the inventory can be considered reliable enough, though incomplete.
As the shape of this curve may vary depending on the order of sample accumulation, an
‘ideal’ smoothed curve can be obtained by using the analytical formulas of Colwell and
collaborators (2004) (see also Mao et al., 2004). An equivalent formula for expected
richness, but not for the variance, was independently developed by Ugland and
collaborators (2003). This method is a precise equivalent of the random resampling
technique used previously (Colwell, 2000) for computing these curves. The slope can be
easily calculated from the fitted function, for it is the first derivative of such function.
Although many functions have been proposed to describe this relationship (Soberón &
Llorente, 1993; Colwell & Coddington, 1994; Flather, 1996; Fagan & Kareiva, 1997;
Moreno & Halffter. 2000), we have favoured the use of the Clench equation, since it avoids
problems of overfitting and critical richness underestimation, providing a good description
6
of the inventory process (Hortal et al., 2004; Hortal & Lobo, 2005). Once the function has
been fitted, a simple but powerful measure of inventory completeness is the slope of the
curve at current sampling-effort level. Though no studies have explored which is the most
adequate slope cut-off to identify a reliable inventory, it is possible to use the intuitive
thresholds or 0.1 or 0.05 (i.e. one species each 10 or 20 units of sampling effort,
respectively) depending on the sampling effort measure used (see below), being more
restrictive if more accurate level of knowledge for the inventory is needed.
However, usually biodiversity databases have been compiled from heterogeneous
sources (ecological studies, sporadic captures, data from natural history collections, etc),
and therefore lack a common measure of sampling effort, such as hours/person, traps/day,
standardized squares, etc. Collector’s curves require a spatially and temporally
homogeneous and comparable sampling effort unit. A first option could be the use of
recorded individuals as a measure of the effort (e.g., Hortal et al., 2004). However,
individuals are an unbiased measure of survey effort only if they were exhaustively
recorded, i.e., their numbers reflect the actual abundances of the species in the field, or, at
least, their detectability. That could be the case for some surveys, especially those based in
traps or active bird or butterfly counts. However, in most cases historical surveys aimed to
raise Natural History Collections, and therefore rare (or less detectable) species are
overrepresented (and common species underrepresented) in the samples gathered by
classical collectors. An alternative measure that accounts for this problem is to use the
number of database records as a measure of sampling effort (Lobo & Martín-Piera, 2002;
Hortal et al., 2001).
A database record refers to each time that a species is recorded in a different place,
day, trap, or by a different collector, regardless of the number of individuals found.
Therefore, this measure is independent of the number of individuals captured, but not of
the number of times a species has been recorded or the number of times each collector
went to the field. This measure has successfully been used a number of times to measure
sampling effort and to describe the process of species accumulation from heterogeneous
data (Hortal et al., 2001; Lobo & Martín-Piera, 2002; Martín-Piera & Lobo, 2003; Romo &
García-Barros, 2005; Romo et al., 2006). Also, its behaviour was similar to some
standardized effort measures such as traps, transects or individuals in a recent comparison
of richness estimators (Hortal et al., 2006). However, this measure requires that database
information has been gathered exhaustively; incompletely digitized information
compromises the correct characterization of survey effort, compromising the utility of data
7
from biodiversity databases (Hortal et al., 2007). Therefore, we encourage that all
information is exhaustively digitized following the standards of GBIF without wasting
those seemingly redundant data belonging to the same species and locality.
Allocating additional sampling sites
If important gaps of information appear once the well surveyed territories are
identified, it might be necessary to allocate additional surveys (Neldner et al., 1995; Barnett
& Stohlgren, 2003). Their success in covering biodiversity patterns will be much improved
if they are allocated with the aim of representing as much as possible of the spatial and
environmental variability of the studied territory, while minimizing resources devoted to
surveys (Ferrier, 2002; Hortal & Lobo, 2005; Funk et al., 2005; Rocchini et al., 2005).
Traditionally, protocols for the allocation of sampling points within a territory rely in (i)
selecting points randomly or regularly in space (Southwood & Henderson, 2000; Hirzel &
Guisan, 2002), (ii) obtaining truly statistically-independent samples (Davis & Goetz, 1990;
Pereira & Itami, 1991), or (iii) placing survey points to cover all the environmental
spectrum (stratified, gradsect and ED methods; Gillison & Brewer, 1985; Austin &
Heyligers, 1989, 1991; Belbin, 1993; Faith and Walker, 1994, 1996; Bunce et al., 1996;
Wessels et al., 1998; Hirzel and Guisan, 2002). If the distribution of biodiversity were a
direct effect of environmental variations, the latter might be the most cost-effective option.
However, dispersal limitations and historical factors, as well as the different responses to
environment of different species and/or groups diminish the representation of biodiversity
provided by environmental variation (Araújo et al., 2001). Due to this, it seems more costeffective to allocate sampling sites to cover environmental variation, including spatial
variables as well, to account for distant places with similar environmental conditions but
hosting different species assemblages (Ferrier, 2002; Hortal & Lobo, 2005).
Predicting species distributions
Predictive modelling tools allow filling in the gaps without resorting to the
impractical task of surveying exhaustively a whole region. Modelling techniques are
generally based in establishing the relationship between a set of predictors (frequently
climatic variables) and the presence-absence of the target species. Predictions are based in
such relationship. Currently, there are a number of techniques that allow predicting the
distribution of species (Guisan & Zimmermann, 2000; Guisan & Thuiller, 2005; Elith et al.,
8
2006; Araújo & Guisan, 2006), which essentially differ in the complexity of the
relationships established between dependent and explanatory variables. If the well surveyed
areas represent the full range of environmental and spatial variation (as discussed above),
predictive models will interpolate biodiversity distribution, rather than extrapolating it to
unknown conditions or areas (Austin & Heyligers, 1989; Ferrier, 2002; Ferrier et al.,
2002a,b), increasing their accuracy and reliability (Kadmon et al., 2004). Therefore, they
constitute the logical step once a good coverage of the variations in biodiversity
composition is available (after assessing the reliability of inventories and, eventually,
carrying out some additional surveys; Hortal et al., 2007).
Although predictive modelling is frequently considered a “hard” and useful strategy
to account for the lack of distributional information, model outcomes often overpredict
the distribution of species (Fielding & Haworth, 1995; Araújo & Williams, 2000; Stockwell
& Peterson, 2002; Brotons et al., 2004; Segurado & Araújo, 2004; Stockman et al., 2006;
Hortal & Lobo, 2006; see also Soberón & Peterson, 2005; Araújo & Guisan, 2006). Due to
this, predictive models have few opportunities of being functional in conservation, and
only a few studies use the predictive maps for conservation purposes. To be useful,
predictive models shall provide geographical representations of the actual area where the
species occurs. The reliability of predictive maps depends on many factors (or potential
sources of error), but three stand out as the most important: (i) quality of biological data;
(ii) predictive power of the predictors; and (iii) modelling technique. While most recent
work on the improvement of predictive modelling results has been devoted to the latter
factor (e.g., Brotons et al., 2004; Segurado & Araújo, 2004; Pearson et al., 2006; Elith et al.,
2006), the effects of the other two sources of error have been less studied, in spite of being
highlighted (see Soberón & Peterson, 2005; Barry & Elith, 2006; Araújo & Guisan, 2006).
We believe that accounting for the first source of error (quality of biological data) lies
within the objectives of projects that aim to compile taxonomical and distributional data.
Therefore, we discuss how to improve biological data coming from biodiversity databases.
Good models need good biological data, with the best possible information about
the presence and absence of the species. Species presence data constitutes the bulk of
biodiversity databases; absences (i.e., localities ore areas where the species is not present),
however, are usually not recorded. Although biologists may know the places where a
species is unlikely present, such data is usually not published. Thus, reliable absence data is
often not available, and therefore true absences can not be distinguished from sites where
the species is in fact present, but has not been recorded due to insufficient sampling effort.
9
However, species are often absent from sites with environmentally favourable conditions
due to biological interactions, dispersal limitations and/or historical factors (Ricklefts &
Schluter, 1993; Hanski, 1998; Pulliam, 1998; Pulliam, 2000), so their actual and potential
geographic distributions differ in space (Soberón & Peterson, 2005). If the aim of
predictive maps is to depict the current distribution of species, absences from suitable areas
should be taken into account (see Lobo et al., 2006), as well as predictors that account for
the exclusion of species from some parts of their potential distribution.
Some predictive modelling techniques do not use absences at all (profile techniques,
e.g., Bioclim, MaxEnt, GARP or Biomapper), which hampers including the exclusion of
the species from some areas. In these techniques absences are implicitly understood as the
places that differ (to some extent) from the presence information available. Therefore,
errors in predictive maps will appear if the presence localities where not obtained from an
exhaustive and spatially homogeneous sampling effort (see, e.g., Hirzel et al., 2001;
Stockwell & Peterson, 2002; Zaniewski et al., 2002; Engler et al., 2004; Kadmon et al., 2004;
Hortal & Lobo, 2005; Reese et al., 2005; Soberón & Peterson, 2005; Vaughan & Ormerod,
2005).
Other techniques require presence and absence data (discrimination techniques,
e.g., GLMs, GAMs or Neural Networks), a requirement that could allow including data on
the exclusion of the species from some potentially suitable areas. The abovementioned lack
of records for the absence of species could be compensated by selecting pseudo-absences
(i.e. places where the absence of the species is assumed). However, pseudo-absences data
are often selected at random without examining their reliability (e.g., Elith et al., 2006; Elith
& Leathwick, 2007). If pseudo-absences are selected at will, (the common) biases in
biological information (see above) will result in an uncertain number of false absences (sites
where the species is present but has not been detected; see Anderson, 2003 or Loiselle et
al., 2003). More sensible options are to select pseudo-absences from areas with
environmental conditions out of the environmental range where species presences are
found (Engler et al. 2004, Lobo et al. 2006), or among the areas with environmental
characteristics similar to those contain well sampling data for whole considered group
(Ferrier and Watson 1997, Zaniewski et al. 2002). Of course, the former strategy might be
useful to produce maps of the potential distribution of species, but it might fail to do so
with their actual distribution. Though being better than selecting pseudo-absences at will,
both options might still select some false absences in environmental conditions or
geographic areas where the species presence of the species is yet, neglecting also the
10
potentially important true absences that might be placed within the environmental or
geographical range covered by known presences.
Distinguishing true from false absences during the selection of pseudo-absences
might be a key step to obtain reliable predictive maps of species distribution (see Ferrier
and Watson, 1997; Hirzel et al., 2001; Zaniewski et al., 2002; Tyre et al., 2003; Engler et al.,
2004; Gu and Swihart, 2004; Soberón & Peterson, 2005; Martin et al., 2005; Lobo et al.,
2006 or Jiménez-Valverde & Lobo, 2006). Using the information from sites with reliable
inventories might guarantee obtaining reliable predictions, if these sites are geographic and
environmentally well distributed (Margules et al., 1987; Bojóquez-Tapia et al., 1996; Iverson
and Prasad, 1998; Zimmermann and Kienast, 1999; Hortal et al., 2001; Lobo & MartínPiera 2002; Lobo et al., 2002). The abovementioned joint use of the survey analysis and
sampling allocation (Hortal & Lobo, 2005; Hortal et al., 2007) might account for the good
quality data needed to improve the reliability of predictive maps of species distributions.
Here, we suggest using the well-surveyed sites to obtain pseudo-absences; that is using as
pseudo-absences those sites where the species has not been collected after an important
survey effort that resulted in a highly reliable inventory, assuming that if the species was
present, the effort carried out would have been enough to record it. Presence data does not
need to come from well-surveyed sites, so all reliable records could be used. This approach
might be difficult to apply for some rare or difficult to detect species, or for barely known
groups, but we are aware it is be an affordable task for a number of groups and species
whose distribution is being predicted nowadays.
(Current) Concluding Remarks
To summarize, although the tools currently available to use the data from
biodiversity databases protocols are ready, we believe that in most cases the data currently
available is not exhaustive enough to be used without a previous check-up. Thus, before
any analysis, it is recommendable to: i) assess the reliability of the biological data for the
studied group, ii) compile and analyze the existing information to identify areas with
reliable inventories, and, eventually, iii) design and run a survey to optimize the coverage of
data on biodiversity patterns. These steps allow use reliable data on the presence and
absence of species to create predictive maps of their distribution. Therefore, we suggest
that, apart from currently available tools (see below and Guralnick et al., 2007) some
software able to analyze survey effectiveness and to design surveys must be developed for
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the taxonomist community interested the application of current taxonomic and
distributional data. Once available, such software should be the first step to analyze raw
data extracted from biodiversity databases. A good use of biodiversity data will come only
by assessing the reliability of data and accounting for its actual quality and accuracy. If the
weaknesses of data are previously known and their analysis takes these drawbacks honestly
into account, the conclusions gathered will be robust and trustable. And nevertheless,
uninformed or malicious sceptics would find harder to cast shadows about the biodiversity
crisis or the effects of climate change.
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Available software
 Discerning well surveyed territories
 EstimateS. Statistical estimation of species richness and shared species
from samples
(http://viceroy.eeb.uconn.edu/estimates)
EstimateS is a free software application for Windows and Macintosh
operating systems that computes a variety of biodiversity functions, estimators, and
indices based on biotic sampling data. Some features require species relative
abundance data, others only species presence/absence data
 Ws2m.
Software
for
the
measurement
and
analysis
of
species
diversity
(http://eebweb.arizona.edu/diversity/)
Ws2m estimate the number of species in a collection of identified
individuals generating a series of statistics for a randomly ordered data set. Ws2m
uses a large (and user-controllable) variety of estimators to produce the estimates. It
also reports the number of individuals used to that point and the actual number of
species so far obtained in the collection. It can report species-abundance
distributions and Jaccard indices.
 SPADE. Species Prediction And Diversity Estimation (http://chao.stat.nthu.edu.tw/)
SPADE estimates species richness, shared species richness and various
diversity and similarity indices, based on different types of sample data from one or
two communities.
 EcoSim. Null model software for ecologists
(http://www.garyentsminger.com/ecosim/index.htm)
EcoSim allows you to test for community patterns with non-experimental
data performing Monte Carlo randomizations to create “pseudo-communities”,
theen statistically compares the patterns in these randomized communities with
those in the real data matrix.
 SPECRICH (http://www.mbr-pwrc.usgs.gov/software.html#a)
SPECRICH Computes species richness or total number of species from
empirical species abundance distribution data
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 Spatial analysis and sampling site location
 GBIF MAPA (http://gbifmapa.austmus.gov.au/mapa/)
A GBIF demonstration project which allows users to query the GBIF cache
using names obtained through the Catalogue of life and to map and analyse the
resultant record set. The Survey Gap Analysis (SGA) tool helps to design a
biodiversity survey that will best complement the existing survey effort by
identifying those areas least well surveyed in terms of environmental conditions.
The Species Richness Assessment (SRA) tool provide an estimate of the number of
species in an area; and to gain insight into the adequacy of sampling based on
abundance distributions for those species.
 LoLA. Library of Location Algorithms (http://www.mathematik.uni-kl.de/~lola/)
LoLA is designed as a software system comprising the algorithmic methods
known in location planning. LoLA consists of a GUI (graphical user interface), a
text based interface, and a programming interface which is designed to enable the
users of LoLA to write their own C++ programs using algorithms from the LoLA libraries.
 ZONATION (http://www.helsinki.fi/bioscience/consplan/)
Zonation is a reserve selection framework for spatial conservation planning. It
identifies areas important for retaining habitat quality and connectivity for multiple
species, indirectly aiming at species’ long-term persistence. Zonation can be used for
various purposes such as spatial conservation prioritization, conservation assessment,
reserve selection and reserve network design.
 GeoDa. An Introduction to Spatial Data Analysis (https://www.geoda.uiuc.edu)
GeoDa is the latest incarnation in a long line of software tools designed to
implement techniques for exploratory spatial data analysis on lattice data (points and
polygons). The free program provides a user friendly and graphical interface to
methods of descriptive spatial data analysis, such as spatial autocorrelation statistics, as
well as basic spatial regression functionality.
 SAM. Spatial Analysis in Macroecology (http://www.ecoevol.ufg.br/sam/)
SAM is a compact but robust computer program designed as a package of statistical
tools for spatial analysis, mainly for applications in Macroecology and Biogeography.
SAM runs under Microsoft Windows as a user-friendly, menu-driven, graphical
interface computational program. SAM offers a wide spectrum of statistical methods
currently used in Surface Pattern Spatial Analysis.
 S-Distance. (http://www.prd.uth.gr/res_labs/spatial_analysis/software/SdHome_en.asp)
S-Distance is a standalone Spatial Decision Support System, mainly focused on
location-allocation analysis. While still being in an early stage, the software is functional
and has been tested on many classical Operation Research instances, as well as on
several real-world problems. S-Distance is currently being created using Microsoft
Visual Basic 6.0 and is intended to be used for educational purposes only.
14
 SITATION. Facility Location Software.
(http://users.iems.northwestern.edu/~msdaskin/Mark%20S.%20Daskin%20Software.html#SITA
TION_Software)
The SITATION software solves several classes of location problems running under
Windows 95. This program as others are fully described in the book Network and
Discrete Location: Models, Algorithms, and Applications.
 ResNet and Surrogacy (http://uts.cc.utexas.edu/~consbio/Cons/Labody.html)
ResNet is place prioritization software package designed to select places according
to their biodiversity content. Surrogacy allow to test if environmental surrogates can
represent biodiversity components.
 spsurvey (http://www.epa.gov/nheerl/arm/analysispages/software.htm)
A R library useful for site selection and spatial survey designs.
 SPATSTAT (http://www.spatstat.org/)
A R library for the statistical analysis of spatial point patterns
 Distribution Models
 The R Project for Statistical Computing (http://www.r-project.org/)
R is the most complete and freely available software for statistical
computing and graphics allowing the accomplishment of Generalized Linear
Models, Generalized Additive Models, Classification and Regression Trees or
Neural Networks models
 DIVA-GIS (http://www.diva-gis.org/)
DIVA-GIS is a free mapping program that can be used for many different
purposes. It is particularly useful for mapping and analyzing biodiversity data, such as
the distribution of species, or other 'point-distributions'.
 DIVA-GIS Annapurna (http://cropforge.org/frs/?group_id=34&release_id=185)
Another version of the same programme.
 Biomapper.
A
GIS-toolkit
to
model
ecological
niche
and
habitat
suitability
(http://www2.unil.ch/biomapper/)
Biomapper is a kit of GIS and statistical tools designed to build habitat suitability
models and maps for any kind of animal or plant. It is centred on the Ecological
Niche Factor Analysis that allows to compute models without the need of absence
data.
15
 DesktopGarp (http://nhm.ku.edu/desktopgarp/index.html)
DesktopGarp is a software package for biodiversity and ecologic research that
allows the user to predict and analyze wild species distributions.
 MaxEnt. (http://www.cs.princeton.edu/~schapire/maxent/)
This software takes as input a set of layers or environmental variables (such
as elevation, precipitation, etc.), as well as a set of georeferenced occurrence
locations, and produces a model of the range of the given species based on the
maximum-entropy approach for species habitat modeling.
 OpenModeller.
(http://openmodeller.sourceforge.net/index.php?option=com_frontpage&Itemid=1 )
The OpenModeller project aims to provide a flexible, user friendly, crossplatform environment where the entire process of conducting a fundamental niche
modeling experiment can be carried out. The software includes facilities for reading
species occurrence and environmental data, selection of environmental layers on
which the model should be based, creating a fundamental niche model and
projecting the model into an environmental scenario. A number of fundamental
niche modeling algorithms are provided as plug-ins, including GARP, Climate
Space Model, Bioclimatic Envelopes, and others.
 FloraMap (http://www.floramap-ciat.org/inicio.htm)
FloraMap is a software linked to agroclimatic and other databases able to
showing the most likely distribution of wild species in nature.
 CLIMEX (http://www.hearne.com.au/products/climex/edition/climex3/)
A commercial package that enables to asses the risk of pest establishing in a
new location and the potential success or failure of a biological control agent from
the current locations.
 JUICE (http://sci.muni.cz/botany/zeleny/hof.php)
JUICE Determine species response curves on environmental gradients,
allowing the determination of species optimum and also niche width (tolerance),
and identifying species as generalist or specialist. JUICE work in the R package
software.
16
 WhyWhere (http://landshape.org/enm/whywhere-20-server/)
WhyWhere is another modelling software similar to GARP that can detect
strong associations with virtually any distribution type becasue it does not require
assumptions about the form of the distributions such as gaussian, sigmoidal,
exponential, etc, as an input.
 HyperNiche (http://home.centurytel.net/~mjm/hyperniche.htm)
HyperNiche is a software for nonparametric regression, providing a flexible
tool for multiplicative habitat modelling, habitat models where the predictors are
combined multiplicatively rather than additively.
17
References
Anderson, R.P. (2003). Real vs. artefactual absences in species distributions: tests for
Oryzomys albigularis (Rodentia: Muridae) in Venezuela. Journal of Biogeography 30: 591605.
Araújo M.B., Humphries C.J., Densham P.J., Lampinen R., Hagemeijer W.J.M., MitchellJones A.J. and Gascet J.P. 2001. Would environmental diversity be a good
surrogate for species diversity? Ecography 24: 103–110.
Araújo, M. B., Williams, P.H., 2000. Selecting areas for species persistence using
occurrence data. Biological Conservation 96: 331–345.
Araújo, M.B. 1999. Distribution patterns of biodiversity and the design of a representative
reserve network in Portugal. Diversity and Distributions 5: 151–163.
Araújo, M.B., and A. Guisan. 2006. Five (or so) challenges for species distribution
modelling. Journal of Biogeography 33: 1677-1688.
Araújo, M.B., Densham P.J. and Humphries C.J. 2003. Predicting species diversity with
ED: the quest for evidence. Ecography 26: 380–383.
Austin M.P. and Heyligers P.C. 1989. Vegetation survey design for conservation: gradsect
sampling of forests in north-eastern New South Wales. Biological Conservation 50:
13–32.
Austin M.P. and Heyligers P.C. 1991. New approach to vegetation survey design: gradsect
sampling. In: Margules C.R. and Austin M.P. (eds), Nature Conservation: Cost Effective
Biological Surveys and Data Analysis. CSIRO, Australia pp. 31–36.
Austin, M.P. 1980. Searching for a model for use in vegetation analysis. Vegetatio 42: 11–
21
Austin, M.P., Pausas, J.G. and Nicholls, A.O. 1996. Patterns of tree species richness in
relation to environment in southern–eastern New South Wales, Australia.
Australian Journal of Ecology 21: 154–164.
Barnett D.T. and Stohlgren T.J. 2003. A nested-intensity design for surveying plant
diversity. Biodiversity and Conservation 12: 255–278.
Barry, S., and J. Elith. 2006. Error and uncertainty in habitat models. Journal of Applied
Ecology 43: 413-423.
Belbin L. 1993. Environmental representativeness: regional partitioning and reserve
selection. Biological Conservation 66: 223–230.
Bisby, F. A. (2000). The Quiet Revolution: Biodiversity Informatics and the Internet.
Science 289: 2309-2312.
Bojórquez-Tapia, L. A., I. Azuara, E. Escurra, and O. Flores-Villela. 1996. Identifying
conservation priorities in México through geographic information systems and
modelling. Ecological Applications 5:215-231.
Brotons, L., Thuiller, W., Araújo, M. B., Hirzel, A. H., 2004. Presence-absence versus
presence-only modelling methods for predicting bird habitat suitability. Ecography
27: 437-448.
Bunce R.G.H., Barr C.J., Clarke R.T., Howard D.C. and Lane A.M.J. 1996. Land
classification for strategic ecological survey. Journal of Environmental Management 47:
37–60.
Cabrero, 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). Biodiversity and Conservation 12: 147-166.
Christen, J.A. & Nakamura, M. 2003. Sequential stopping rules for species accumulation.
Journal of Agricultural, Biological and Environmental Statistics 8: 184–195.
Colwell, R.K., 2006. EstimateS: Statistical estimation of species richness and shared species
from samples. Version 8. User's Guide and application published at:
18
http://purl.oclc.org/estimates. Department of Ecology & Evolutionary Biology,
University of Connecticut, Storrs.
Colwell, R. K., C. X. Mao, & J. Chang. 2004. Interpolating, extrapolating, and comparing
incidence-based species accumulation curves. Ecology 85, 2717-2727.
Colwell, R.K. & Coddington, J.A. 1995. Estimating terrestrial biodiversity through
extrapolation. In: Hawksworth DL (ed) Biodiversity, Measurement and Estimation, pp
101–118. Chapman and Hall, London.
Colwell, R.K., & Coddington, J.A., 1994. Estimating terrestrial biodiversity through
extrapolation. Philosophical Transactions of the Royal Society of London B 345,
101-118.
Davis F.W. and Goetz S. 1990. Modeling vegetation–environment association using
digital satellite and terrain data. Landscape Ecology 4: 69–80.
Dennis R.L.H. & Hardy, P.B. 1999. Targeting squares for survey: predicting species
richness and incidence for a butterfly atlas. Global Ecology and Biogeography 8: 443–
454.
Dennis, R.L.H. & Thomas, C.D. 2000. Bias in butterfly distribution maps: the influence of
hot spots and recorder's home range. Journal of Insect Conservation 4: 73-77.
Dennis, R.L.H., Shreeve, T.G., Isaac, N.J.B., Roy, D.B., Hardy, P.B., Fox, R. & Asher, J.
2006. The effects of visual apparency on bias in butterfly recording and
monitoring. Biological Conservation 128: 486-492.
Dennis, R.L.H., Sparks, T.H. & Hardy, P.B. 1999. Bias in butterfly distribution maps: the
effects of sampling effort. Journal of Insect Conservation 3: 33-42.
Elith, J. & Leathwick, J. (2007) Predicting species distributions from museum and
herbarium records using multiresponse models fitted with multivariate adaptive
regression splines. Diversity and Distributions 13: 265-275.
Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J.,
Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle,
B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M.,
Peterson, A.T., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E.,
Soberón, J., Williams, S., Wisz, M.S., Zimmermann, N.E., 2006. Novel methods
improve prediction of species’ distributions from occurrence data. Ecography 29:
129-151.
Engler, R., Guisan, A. & Rechsteiner, L., 2004. An improved approach for predicting the
distribution of rare and endangered species from occurrence and pseudo-absence
data. Journal of Applied Ecology 41: 263–274.
Fagan, W.F. & Kareiva, P.M. 1997. Using compiled species list to make biodiversity
comparisons among regions: a test case using Oregon butterflies. Biological
Conservation 80: 249–259.
Faith D.P. 2003. Environmental diversity (ED) as a surrogate information for specieslevel biodiversity. Ecography 26: 374–379.
Faith D.P. & Walker P.A. 1994. DIVERSITY: A Software Package for Sampling Phylogenetical
and Environmental Diversity. Reference and User’s Guide. v. 2.1.. CSIRO Division of
Wildlife
and
Ecology,
Canberra,
Available
at
http://www.amonline.net.au/systematics/staff_faith.htm.
Faith D.P. & Walker P.A. 1996. Environmental diversity: on the best-possible use of
surrogate data for assessing the relative biodiversity set of areas. Biodiversity and
Conservation 5: 399–415.
Faith, D. 2002. Those complementarity analysis do not reveal extent of conservation conflict
in
Africa.
Science
dEbate.
Available
at
http://www.sciencemag.org/cgi/eletters/293/5535/1591/#381.
19
Ferrier S. 2002. Mapping spatial pattern in biodiversity for regional conservation planning:
Where to from here? Systematic Biology 51: 331–363.
Ferrier S., Watson G., Pearce J. and Drielsma M. 2002a. Extended statistical approaches
to modelling spatial pattern in biodiversity in northeast New South Wales. I.
Species-level modelling. Biodiversity and Conservation 11: 2275–2307.
Ferrier S., Drielsma M., Manion G. & Watson G. 2002b. Extended statistical approaches
to modelling spatial pattern in biodiversity in northeast New South Wales. II.
Community-level modelling. Biodiversity and Conservation 11: 2309–2338.
Ferrier, S. & Watson, G. 1997. An Evaluation of the Effectiveness of Environmental Surrogates and
Modelling Techniques in Predicting the Distribution of Biological Diversity. NSW National
Parks and Wildlife Service Department of Environment, Sport and Territories.
Environment Australia.
Fielding, A. H. & Haworth, P.F., 1995. Testing the Generality of Bird-Habitat Models.
Conservation Biology 9: 1466-1481
Flather, C.H. 1996. Fitting species-accumulation functions and assessing regional land use
impacts on avian diversity. Journal of Biogeography 23: 155–168.
Gaston, K.J. 1994. Rarity. Chapman and Hall, London
Gaston, K.J. 1996. Species richness: measure and measurement. In: Gaston KJ (ed)
Biodiversity. A Biology of Numbers and Difference, pp 77–113. Blackwell Science,
Oxford.
GBIF (2003) Global Biodiversity Information Facility Strategic Plan. Available at
www.gbif.org/GBIF_org/documents.
Gillison, A.N. & Brewer, K.R.W. (1985) The use of gradient directed transects or
gradsects in natural resource surveys. Journal of Environmental Management, 20: 103127.
Godfray, C. (2002) Challenges for taxonomy. Nature 417: 17–19.
Gotelli N.J. & Colwell R.K. 2001. Quantifying biodiversity: procedures and pitfalls in
measurement and comparison of species richness. Ecology Letters 4: 379–391.
Graham, C.H., Ferrier, S., Huettman, F., Moritz, C. & Peterson, A.T. 2004. New
developments in museum-based informatics and applications in biodiversity
analysis. Trends in Ecology and Evolution 19: 497-503.
Gu, W. & Swihart, R.K. 2004. Absent or undetected? Effects of non-detection of species
occurrence on wildlife–habitat models. Biological Conservation 116: 195–203.
Guisan, A. & Thuiller, W., 2005. Predicting species distribution: offering more than simple
habitat models. Ecology Letters 8: 993-1009.
Guisan, A. & Zimmermann, N. E., 2000. Predictive habitat distribution models in
ecology. Ecological Modelling 135: 147-186.
Guralnick, R.P., Hill, A.W. & Lane, M., 2007. Towards a collaborative, global infrastructure
for biodiversity assessment. Ecology Letters 10: 663-672.
Hanski, I. 1998. Metapopulation dynamics. Nature 396: 41-49.
Hirzel A. & Guisan A. 2002. Which is the optimal sampling strategy for habitat suitability
modelling. Ecological Modelling 157: 331–341.
Hirzel, A. H., Helfer, V. & Metral, F., 2001. Assessing habitat-suitability models with a
virtual species. Ecological Modelling 145: 111-121.
Hortal J., Garcia-Pereira P. & García-Barros E. 2004. Butterfly species richness in
mainland Portugal: Predictive models of geographic distribution patterns.
Ecography 27: 68–82.
Hortal, J., & J. M. Lobo. 2005. An ED-based protocol for the optimal sampling of
biodiversity. Biodiversity and Conservation 14: 2913-2947.
Hortal, J. & Lobo, J.M. 2006. Towards a synecological framework for systematic
conservation planning. Biodiversity Informatics 3: 16-45.
20
Hortal, J., Lobo, J.M. & Martín-Piera, F. 2001. Forecasting insect species richness scores
in poorly surveyed territories: the case of the Portuguese dung beetles (Col.
Scarabaeinae). Biodiversity and Conservation 10: 1343–1367.
Hortal, J., Lobo, J.M. & Jiménez-Valverde, A., 2007. Limitations of biodiversity databases:
case study on seed-plant diversity in Tenerife (Canary Islands). Conservation
Biology 21, 853-863.
Hortal, J.,. Borges P. A. V & C. Gaspar. 2006. Evaluating the performance of species
richness estimators: sensitivity to sample grain size. Journal of Animal Ecology 75:
274–287.
Iverson, L. R. & A. M. Prasad. 1998. Predicting potential future abundance of 80 tree
species following climate change in the eastern United States. Ecological Monographs
68:465-485.
Jiménez-Valverde, A. & Lobo, J. M. 2006. The ghost of unbalanced species distribution
data in geographic model predictions. Diversity and Distributions 12: 521-524.
Kadmon, R., Farber, O. & Danin, A. 2003. A systematic analysis of factors affecting the
performance of climatic envelope models. Ecological Applications 13: 853–867
Kadmon, R., Oren, F. & Avinoam, D., 2004. Effect of roadside bias on the accuracy of
predictive maps produced by bioclimatic models. Ecological Applications 14: 401–
413.
Lawton, J.H., Prendergast, J.R. & Eversham, B.C. 1994. The numbers and spatial
distributions of species: analyses of British data. In: Forey PL, Humphries CJ and
Vane-Wright RI (eds) Systematics and Conservation Evaluation, Systematics
Association Special Volume No. 50, pp 177–195. Clarendon Press, Oxford.
León Cortés, J.L., Soberón-Mainero, J. & Llorente-Bousquets, J. 1998. Assessing
completeness of Mexican sphinx moth inventories through species accumulation
functions. Diversity and Distributions 4: 37–44.
Lobo J.M., Sanmartín, I. and Martín-Piera, F. 1997. Diversity and Spatial Turnover of
dung beetle (Coleoptera: Scarabaeoidea) communities in a protected area of South
Europe (Doñana National Park, Huelva, Spain). Elytron 11: 71–88.
Lobo, J. M., J. P. Lumaret & P. Jay-Robert. 2002. Modelling the species richness of
French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predicitive
capacity of different groups of explanatory variables. Global Ecology and Biogeography
11: 265-277.
Lobo, J. M., Verdu, J. R. & Numa, C. 2006. Environmental and geographical factors
affecting the Iberian distribution of flightless Jekelius species (Coleoptera:
Geotrupidae). Diversity and Distributions 12:179-188.
Lobo, J.M. & Martín-Piera, F. 2002. Searching for a predictive model for Iberian dung
beetle species richness based on spatial and environmental variables. Conservation
Biology 16: 158–173.
Lobo, J.M.; Baselga, A.; Hortal, J.; Jiménez-Valverde, A. & Gómez, J.F. 2007. How does
the knoeledge about the sapatila distribution of Iberian dung beetle species
accumulate over time? Diversity and Distributions (in press).
Loiselle, B.A., Howell, C.A., Graham, C.H., Goerck, J.M., Brooks, T., Smith, K.G. &
Williams, P.H. (2003) Avoiding Pitfalls of Using Species Distribution Models in
Conservation Planning. Conservation Biology 17: 1591-1600.
Mac Nally, R. 2000. Regression and model building in conservation biology, biogeography
and ecology: the distinction between – and reconciliation of – ‘predictive’ and
‘explanatory’ models. Biodiversity and Conservation 9: 655–671
Magurran, A.E. 1988. Ecological Diversity and Its Measurement. Princeton University Press,
Princeton, New Jersey
21
Mao, C. X., R. K. Colwell, and J. Chang. 2005. Estimating species accumulation curves
using mixtures. Biometrics 61:433–441.
Margules, C.R., Nicholls, A.O. & Austin, M.P. 1987. Diversity of Eucaliptus species
predicted by a multivariable environment gradient. Oecologia 71: 229–232.
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. Ecology Letters 8: 1235–
1246.
Martínez-Meyer, E. 2005. Climate change and biodiversity: some considerations in
forecasting shifts in species potential distributions. Biodiversity Informatics 2: 42-55
Martín-Piera, F. & Lobo, J.M. 2003. Database records as a sampling effort surrogate to
predict spatial distribution of insects in either poorly or unevenly surveyed areas.
Acta Entomológica Ibérica e Macaronésica 1: 23–35.
Moreno C.E. & Halffter G. 2000. Assessing the completeness of bat biodiversity
inventories using species accumulation curves. Journal of Applied Ecology 37: 149–
158.
Moreno C.E. & Halffter G. 2001. On the measure of sampling effort used in species
accumulation curves. Journal of Applied Ecology 38: 487–490.
Neldner V.J., Crossley D.C. & Cofinas M. 1995. Using Geographic Information Systems
(GIS) to determine the adequacy of sampling in vegetation surveys. Biological
Conservation 73: 1–17.
Nelson BW, Ferreira CAC, da Silva MF & Kawasaki M.L. 1990. Endemism centers,
refugia and botanical collection density in Brazilian Amazonia. Nature 345:714716.
Parnell, J.A.N.; Simpson DA, Moat J, Kirkup DW, Chantaranothai P, Boyce PC, Bygrave
P, Dransfield S, Jebb MHP, Macklin J, Meade C, Middleton DJ, Muasya AM,
Prajaksood A, Pendry CA, Poomar R, Suddee S, & Wilkin P. 2003. Plant
collecting spread and densities: their potential impact on biogeographical studies
in Thailand. Journal of Biogeography 30: 193-209.
Pearson, R.G., W. Thuiller, M.B. Araújo, E. Martinez-Meyer, L. Brotons, C. McClean, L.
Miles, P. Segurado, T.P. Dawson & D.C. Lees. 2006. Model-based uncertainty in
species range prediction. Journal of Biogeography 33: 1704-1711.
Pereira J.M.C. & Itami R.M. 1991. GIS-based habitat modelling using logistic multiple
regression: a study of the Mt. Graham Red Squirrel. Photogramm. Eng. Rem. Sens. 57:
1475–1486.
Peterson AT, Navarro-Sigüenza AG & Benítez-Díaz, H. 1998. The need for continued
scientific collecting: a geographic analysis of Mexican bird specimens. Ibis 140:
288-294.
Prendergast, J.R.; Wood, S.N.; Lawton, J.H. & Eversham, B.C. 1993. Correcting for
variation in recording effort in analyses of diversity hotspots. Biodiversity Letters 1:
39–53.
Pulliam, H.R. 1988. Sources, sinks and population regulation. American Naturalist 132: 652661.
Pulliam, H.R. 2000. On the relationship between niche and distribution. Ecology Letters 3:
349-361.
Reddy, S. & Dávalos, L.M. 2003. Geographical sampling bias and its implications for
conservation priorities in Africa. Journal of Biogeography 30:1719-1727.
Reese,G.C., Wilson, K.R., Hoeting, J.A. & Flather, C.H. 2005. Factors affecting species
distribution predictions: a simulation model experiment. Ecological Applications 15:
554–564.
22
Reutter, B.A., Helfer, V., Hirzel, A.H. & Vogel, P. 2003. Modelling habitat-suitability
using museum collections: an example with three sympatric Apodemus species from
the Alps. Journal of Biogeography 30: 581-590.
Ricklefs R E. & Schluter, D. 1993. Species Diversity in Ecological Communities. Historical and
Geographical Perspectives. University Chicago Press, Chicago
Rocchini, D., Butini, S.A. & Chiarucci, A. 2005. Maximizing plant species inventory
efficiency by means of remotely sensed spectral distances. Global Ecology and
Biogeography 14, 431-437.
Romo, H. & García-Barros, E. 2005. Distribución e intensidad de los estudios faunísticos
sobre mariposas diurnas en la Península Ibérica e Islas Baleares (Lepidoptera,
Papilionoidea y Hesperoidea). Graellsia 61: 37–50.
Romo, H., García-Barros, E. & Lobo, J.M. 2006. Identifying recorder-induced geographic
bias in an Iberian butterfly database. Ecography 6: 883-875.
Saarenmaa, H., Nielsen, E.S. eds., 2002. Towards a global biological information
infrastructure. Challenges, opportunities, synergies, and the role of entomology.
European Environment Agency, Copenhagen.
Segurado, P. & M.B.Araújo. 2004. An evaluation of methods for modelling species
distributions. Journal of Biogeography 31: 1555-1568.
Segurado, P., Araújo, M.B. & Kunin, W.E., 2006. Consequences of spatial autocorrelation
for niche-based models. Journal of Applied Ecology 43: 433–444.
Soberón J, Llorente J. & Benítez H. 1996. An International view of National Biological
Surveys. Annals Miss Bot Gardens 83:562-573.
Soberón, J. & Llorente, B.J. 1993. The use of species accumulation functions for the
prediction of species richness. Conservation Biology 7: 480–488.
Soberón, J., Jiménez, R., Golubov, J. & Koleff, P., 2007. Assessing completeness of
biodiversity databases at different spatial scales. Ecography 30, 152-160.
Soberón, J. & Peterson, A.T. 2005. Interpretation of models of fundamental ecological
niches and species’ distributional areas. Biodiversity Informatics 2: 1-10
Soberón, J. & Peterson, A.T., 2004. Biodiversity informatics: managing and applying
primary biodiversity data. Phil. Trans. R. Soc. Lond. B, 359: 689–698
Soberón, J.M., Llorente, J. & Oñate, L. 2000. The use of specimen-label databases for
conservation purposes: an example using Mexican Papilionid and Pierid
butterflies. Biodiversity and Conservation 9:1441-1466
Southwood T.R.E. & Henderson P.A. 2000. Ecological Methods, 3rd edn. Blackwell Science,
Oxford.
Stockman, A.K., Beamer, D.A. & Bond, J.E., 2006. An evaluation of a GARP model as an
approach to predicting the spatial distribution of non-vagile invertebrate species.
Diversity and Distributions 12: 81–89
Stockwell, D. R. B. & Peterson, A.T., 2002. Effects of sample size on accuracy of species
distribution models. Ecological Modelling 148: 1–13.
Tyre, A.J., Tenhumberg, B., Field, S.A., Niejalke, D., Paris, K. & Possingham, H.P. 2003.
Improving precision and reducing bias in biological surveys by estimating false
negative error rates in presence-absence data. Ecological Applications 13: 1790–1801.
Ugland, K. I., J. S. Gray, & K. E. Ellingsen. 2003. The species-accumulation curve and
estimation of species richness. Journal of Animal Ecology 72: 888-897.
Vaughan, I.P. & Ormerod, S.J., 2005. The continuing challenges of testing species
distribution models. Journal of Applied Ecology 42: 720-730.
Wessels K.J., van Jaarsveld A.S., Grimbeek J.D. & van der Linde M.J. 1998. An evaluation
of the gradsect biological survey method. Biodiversity and Conservation 7: 1093–1121.
Williams, P.H.; Margules, C.R. & Hilbert, D.W. 2002. Data requirements and data sources
for biodiversity priority area selection. Journal of Bioscience 27:327-338.
23
Willott S.J. 2001. Species accumulation curves and the measure of sampling effort. Journal
of Applied Ecology 38: 484–486.
Zaniewski, A.E., Lehmann, A. & Overton, J.M., 2002. Predicting species spatial
distributions using presence-only data: a case study of native New Zealand ferns.
Ecological Modelling 157: 261-280.
Zimmermann, N. E. & F. Kienast. 1999. Predictive mapping of alpine grasslands in
Switzerland: species versus community approach. Journal of Vegetation Science 10:469482.
24