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MACROECOLOGICALMETHODS
rangeMapper: a platform for the studyof macroecology of life-history traitsgeb_739 945..951
Mihai Valcu1*, James Dale1,2 and Bart Kempenaers1
1Department Behavioural Ecology and
Evolutionary Genetics, Max Planck Institute
for Ornithology, Seewiesen, Germany,2Institute of Natural Sciences, Massey
University, Albany Campus, Auckland, New
Zealand
ABSTRACT
Aim To introduce rangeMapper, an R package for the study of the macroecologicalpatterns of life-history traits, and demonstrate its capabilities using three casestudies. The first case study addresses an important topic in conservation biology:biodiversity hotspots. Specifically, we investigate the congruence between globalhotspots of three parameters that describe avian diversity: species richness,endemic species richness and relative body mass diversity. The second case studyinvestigates a topic of relevance for macroecology: the inter-specific relationshipbetween range size and body size for avian assemblages, and how it varies geo-graphically. The third case study tackles a methodological problem in macroecol-ogy: the influence of map resolution on statistical inference, i.e. the question ofwhether and how the relationship between species richness and body mass varieswith map resolution.
Innovation rangeMapper offers a tight integration of spatial and statistical toolsfor macroecological projects and it relies on a high-performance database enginewhich makes it suitable for managing projects using a large number of species.rangeMapper’s architecture follows closely the concepts described by Gaston et al.(2008 Journal of Biogeography, 35, 483–500) and its flexibility allows for bothcomplex data manipulation procedures and easy implementation of new functions.By choosing case studies to cover various technical and conceptual issues we dem-onstrate rangeMapper’s capabilities to address a wide array of questions.
Main conclusion rangeMapper (http://cran.r-project.org/package=rangeMapper) is an open source front end software which can be used to addressquestions in both fundamental ecological research and conservation biology.
KeywordsBirds, body mass, breeding range size, functional diversity, hotspots, R package,species distribution, species richness.
*Correspondence: Mihai Valcu, DepartmentBehavioural Ecology and Evolutionary Genetics,Max Planck Institute for Ornithology, EberhardGwinner Strasse, 82319 Seewiesen, Germany.E-mail: valcu@orn.mpg.deRe-use of this article is permitted in accordancewith the Terms and Conditions set out athttp://wileyonlinelibrary.com/onlineopen#OnlineOpen_Terms
INTRODUCTION
Understanding patterns of biological diversity is a major goal of
macroecological research. In particular, over the last decade sub-
stantial effort has been invested in explaining geographical
variation in species richness (e.g. Gaston, 2000; Davies et al.,
2007). In addition to species diversity, functional trait diversity
has now been recognized as an equally important component of
biological diversity (e.g. Petchey & Gaston, 2006). Indeed, with
the increased availability of life-history data compiled for whole
taxonomic groups, the first studies on life-history traits at a
global level have recently been undertaken (e.g. Jetz et al., 2008;
Olson et al., 2009).
Global patterns of species richness and life-history trait dis-
tributions are likely driven by multiple factors, at multiple levels
of organization (from population to assemblage level) and at
multiple spatial scales (from regional to global) (Gaston et al.,
2008). This makes macroecology inter-disciplinary in both its
approach and the tools it uses (Blackburn, 2004; Smith et al.,
2008). For example, a typical macroecology project (e.g. Hurl-
bert & Jetz, 2007; Olson et al., 2009; Fritz & Purvis, 2010;
Powney et al., 2010) requires a tight integration of geospatial
species range vector data with (1) life-history information and
(2) satellite remote-sensing ecological and climatological raster
data. The conceptual and technical complexity of such a project
is increased further depending on whether the level of analysis is
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Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2012) 21, 945–951
© 2012 Blackwell Publishing Ltd DOI: 10.1111/j.1466-8238.2011.00739.xhttp://wileyonlinelibrary.com/journal/geb 945
at the inter-specific or the assemblage level. For an inter-specific
analysis, the parameters required are the life-history traits of
interest for each species and the phylogenetic relationships
between species, linked with spatial characteristics of the species
ranges (e.g. range size or range shape) and the key environmen-
tal variables within the ranges. In contrast, for an assemblage-
level analysis, the geographical ranges of all species are
partitioned into a regular grid, and each ‘assemblage’ is defined
as the community of species that occurs within each cell. Each
assemblage can then be described by its richness (i.e. the count
of all species in a grid cell) and the life-history characteristics of
its species community. Environmental variables at each grid cell
are then typically used as predictor variables for both species
richness and/or life-history traits.
The analyses described above require adequate statistical
models that allow for spatial autocorrelation and/or phyloge-
netic control. Although various statistical tools already exist
along with spatial and database management support, these are
often difficult to integrate under the same computing platform.
Thus researchers typically have to switch between various com-
puter programs that are loosely interconnected at best and often
function as a black box (i.e. when not open source).
Here we introduce rangeMapper (http://cran.r-project.org/
package=rangeMapper), a versatile framework for the study of
macroecological patterns of life-history traits that can be used to
answer a large array of questions in both fundamental ecological
research and conservation biology. rangeMapper is an open
source extension for R (R Development Core Team, 2010), built
using R’s comprehensive database (James, 2010) and spatial
classes support (Pebesma & Bivand, 2005; Bivand et al., 2008;
Hijmans & Etten, 2010). Macroecological projects can be per-
formed at both inter-specific and assemblage levels, and tools for
connecting the two approaches are provided. rangeMapper
further allows a straightforward integration of the many statis-
tical tools existing in R.
In this paper we first describe the concept behind rangeMap-
per and introduce its general capabilities. Second, we apply
rangeMapper to three case studies chosen to cover various tech-
nical and conceptual issues. For each case study, we provide a
brief introduction to the topic, a description of the method, the
rangeMapper results and a brief discussion. These examples are
based on a comprehensive dataset of the geographical breeding
distributions of more than 8000 avian species. The case studies
are accompanied by reproducible examples using the life-history
traits and the breeding range distribution of the New World
wrens (Troglodytidae), a dataset which is bundled with the
package (see Appendices S1–S5 in the Supporting Information).
MATERIALS AND METHODS
The rangeMapper general framework
rangeMapper adopts a modular framework (Fig. 1) where each
project is partitioned into several steps. This versatility ensures
that it is relatively straightforward at each step to plug in various
statistical models of any degree of complexity. This mechanism
allows us to implement both (1) range structure indexes (e.g.
range shape; Pigot et al., 2010) and (2) measures of environmen-
tal parameters (e.g. mean primary production or elevation
range). Environmental parameters can be computed either
within the range of each taxon or at each grid cell (with the
support from the raster and rgdal packages; Hijmans & Etten,
2010; Keitt et al., 2010). Finally, the modular framework allows
the use of a wide array of statistical models computed at each
Figure 1 rangeMapper’s workflow. A regular grid (canvas) of a given resolution and coordinate reference system (CRS) is set (a),geographical ranges are overlaid on the canvas (b) and parameters are optionally computed for chosen range traits (c). Once thegeographical ranges are interpolated on the canvas, life-history traits (d) and environmental data (e) can be imported. Maps are saved tothe project (f) and can be retrieved in a format derived from class Spatial (Pebesma & Bivand, 2005). Maps can be customized forvisualization in R (Neuwirth, 2007; Bivand, 2009) (g) and exported for visualization (e.g. in Quantum GIS Development Team, 2011) or forspatial analysis (e.g. Rangel et al., 2010) (f). All modules are tightly interconnected and checks are performed at every step to ensure dataintegrity. The ‘SUBSET’ option of the mapping module (f) allows for the creation of new maps based on data at both species level (rangetraits, life-history traits) and assemblage level (existing maps, environmental data). An across-platform graphical user interface is availablefor modules (a) to (g), yet rangeMapper can also be used for scripting and simulations (h) (see Appendices S2–S4). function(x, y. . .) indicateswhich modules allow integration of existing or user defined functions (e.g. glm-s, range shape indices).
M. Valcu et al.
Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd946
canvas cell, from simple analyses (count, average) to rather
complex models applied at each pixel (e.g. generalized linear
models).
A ‘subsetting’ mechanism used for map building is an addi-
tional strength of rangeMapper. That is, subsets of life-history
traits, range traits, assemblage traits (i.e. pixel traits) or any
combination of those can be easily defined, ensuring a tight
connection between inter-specific and assemblage levels.
rangeMapper projects are hosted on disk in sqlite databases so
most computations are not memory limited. Moreover the
robust sqlite engine allows efficient management of large
projects. For example creating a global map of median body
mass of 8434 species using a canvas with a grain of 50 km2 takes
2.1 min on a four-core 2.8 GHz Intel Xeon running 64-bit
Ubuntu Linux 10 with 11.8 GB of physical memory.
For users without knowledge of R scripting language, a cross-
platform graphical user interface (GUI) is provided for most
tools (Valcu & Dale, 2011). Finally, rangeMapper is built using S4
classes (Chambers, 2008) and can therefore be easily extended.
Case-study datasets
We collected body mass data of 8434 bird species from the CRC
handbook of avian body masses (Dunning, 2008). When multiple
entries per species were available we used median body mass as
the species value. We digitized breeding ranges (i.e. the geo-
graphical extent of occurrence of each species in the reproduc-
tive season) from various sources (Cramp & Simmons, 1977–
1994; Brown et al., 1982–2004; Marchant & Higgins, 1990–2006;
del Hoyo et al., 1992–2010; Ridgely & Tudor, 2009) onto a 720 ¥360 unprojected pixel template of the earth and converted these
raster images into vector files. The hotspot congruence analysis
and the ‘range size–body mass’ analysis were performed using a
rangeMapper project with an equal area projection canvas
approximating to a 1° scale.
Case study 1: congruence of different biodiversity hotspots
Biodiversity ‘hotspots’ are a central concept in conservation
biology (Orme et al., 2005; Ceballos & Ehrlich, 2006) because
they form the foundation for establishing global conservation
priorities. However, because there is an ongoing debate about
which specific biodiversity measure is most relevant, it is impor-
tant to understand the extent to which different types of
hotspots overlap.
We identified the global hotspots of three parameters describ-
ing avian diversity: total species richness, endemic species rich-
ness and relative body mass diversity. This was done by
generating maps for species richness (total number of species in
each canvas cell), endemic species richness (number of species
with the 25% smallest breeding ranges present in each canvas
cell, e.g. Orme et al., 2005) and relative body mass diversity
(coefficient of variation of log10 body mass in each cell) (Fritz &
Purvis, 2010). The maps of endemic species richness and body
mass relative diversity can be found in Appendix S1. We defined
hotspots for each of these measures as the richest 5% of grid
cells (Fig. 2). We then measured the congruence between
hotspots by the extent of spatial overlap, i.e. the percentage of
canvas cells which met the definition of two or three of the
hotspots (Orme et al., 2005; Ceballos & Ehrlich, 2006).
We found a very low spatial overlap (0.9%) between hotspots
of species richness and relative body mass diversity (Fig. 2). The
overlap between hotspots of endemic species and relative body
mass diversity was also low (2.3%, Fig. 2). The overlap between
hotspots of endemic species and species richness was 3% and the
cumulative overlap between the three sets of hotspots was only
0.15%.
Our results suggest that the three avian diversity hotspots
considered here are virtually independent. This replicates previ-
ous results showing little congruence between hotspots of
species richness and endemism (Orme et al., 2005; Ceballos &
Ehrlich, 2006). Adding relative body mass diversity as a comple-
mentary biodiversity measure did not change the overall
picture, suggesting that spatial patterns exhibited by various
aspects of biodiversity are determined by different mechanisms.
This case study illustrates the use of rangeMapper for identi-
fying biodiversity hotspots (see Appendix S2 for a reproducible
R code example using the wrens dataset). Using rangeMapper’s
flexible subsetting mechanism, this example can be further
refined. For example, by changing the subset definitions to
incorporate only certain groups of species, we could investigate
hotspot congruency for particular taxonomic clades, different
functional groups or different habitat classes.
Case study 2: geographical variation in the relationship between
range size and body size
The relationship between species range size and average body
size is a classic topic in macroecology (e.g. Gaston & Blackburn,
1996, 2000). The relationship is positive across many taxa, but a
few studies report a negative relationship or no relationship.
Gaston & Blackburn (1996) suggested that negative relation-
ships are artefacts because the likelihood of finding a negative
(or no) correlation between range size and body size is higher
when the scale (i.e. the extent) of the study is too small to
encompass all the geographical ranges of the studied species.
Alternatively however, global-scale spatial variation in the range
size–body size relationship itself may exist. This is not far-
fetched, because spatial variation in similar relationships has
been documented. For example, the generally positive correla-
tion between range size and local abundance does not apply for
an entire biogeographical area, even though it is one of the most
robust findings in macroecology (Symonds & Johnson, 2006).
Here, we investigated global spatial variation of the slope of
the correlation between range size and body mass for avian
assemblages. For each grid cell, we estimated the slope of the
range size–body mass regression using a robust regression (Ven-
ables & Ripley, 2002) whereby both range size and body mass
were log-transformed and standardized with z-scores (i.e. scaled
and centred). This alternative to ordinary least squares regres-
sion ensures an unbiased estimation even when the model
assumptions are unfulfilled.
Macroecology of life history traits
Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd 947
Our analysis revealed strong geographical variation in the
slope of the range size–body mass regression despite the fact that
98.9% of the slopes were positive (Fig. 3a). Relatively strong
range size–body mass regression slopes were confined to certain
geographical areas. Moreover, after controlling for multiple
testing only 52.2% of the grid cells contained a statistically sig-
nificant range size–body mass regression (Fig. 3b). Interestingly,
among the four avian studies reviewed by Gaston & Blackburn
(1996) the two studies that reported negative or no relationships
(3 and 4 in Fig. 3b) examined areas where the positive relation-
ship was non-significant or negative, while the two studies
reporting positive relationships (1 and 2 in Fig. 3b) were from
areas where the positive relationship was strong and statistically
significant.
This case study illustrates the use of rangeMapper for statisti-
cal modelling at grid cell level (see Appendix S3 for a reproduc-
ible example using the wrens dataset). Although here we used a
robust regression slope, other statistical models can be incorpo-
rated equally easily in rangeMapper. For example, one could use
the slope of a mixed model (Pinheiro & Bates, 2000) (see Appen-
dix S3) that included higher taxonomic groups as random
effects and would thus allow for a certain degree of phylogenetic
correction.
Case study 3: the influence of grid size on the relationship
between species richness and body size
Spatial patterns of species richness can be strongly dependent on
the chosen resolution (i.e. grid cell size). When the spatial reso-
lution is not adequate, species richness obtained from geo-
graphical ranges is a poor estimator of true species richness
(Hurlbert & Jetz, 2007). Moreover, the effect size of predictors of
species richness can change with varying spatial resolution
(Rahbek & Graves, 2001; Davies et al., 2007). Therefore it is
a
b
cFigure 2 Global distribution of threemeasures of biodiversity hotspots definedas the richest 5% of grid cells (orange).(a) Hotspots of body mass diversity(coefficient of variation of log bodymass). (b) Hotspots of species richness.(c) Hotspots of endemic species. Overlapbetween hotspots of body mass diversitywith hotspots of (b) species richness and(c) endemic species, respectively, areindicated in blue.
M. Valcu et al.
Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd948
reasonable to predict that the relationships between species rich-
ness and life-history variables will also depend on the spatial
resolution. For example, Olson et al. (2009) showed that species
richness was a strong predictor of variation in median body size
in an assemblage-level spatial multiple regression using a 1°
resolution (see Fig. 4a,b).
Here we examined the relation between median body mass
and species richness at the assemblage level using 100 different
spatial resolutions. We used rangeMapper projects set on equal
area projection canvases ranging from 50 to 550 km2 (i.e. from c.
0.45 to c. 4.5°) with median body mass (log10-transformed) as
the dependent variable and species richness (square-root trans-
formed) as the only predictor. To account for spatial autocorre-
lation we used simultaneous autoregressive models (SAR)
(Bivand et al., 2008).
The strength of the body size–species richness relationship,
quantified as the slope of a SAR model, varied considerably at
the 100 different spatial resolutions. We confirmed that the SAR
slope is negative for the entire resolution range, but found it to
decrease dramatically with increasing grid cell size (i.e. decreas-
ing map resolution; Fig. 4c). Typically such analysis is done for
three to four (Davies et al., 2007) or sometimes 10 resolutions
(Rahbek & Graves, 2001). However, because rangeMapper easily
allowed us to use a large number of spatial resolutions we could
additionally resolve a nonlinear dependence of the SAR slope on
the spatial resolution (Fig. 4d).
This case study exemplifies a more advanced use of
rangeMapper because it requires the command line interface or
batch processing, while the first two case studies can be per-
formed entirely from the GUI. However, the compact scripting
language makes it possible to implement complex analyses with
relatively few commands. In Appendix S4 we show how to itera-
tively build up projects of increasing resolution and extract the
parameters of interest (i.e. the slope of the body size–species
richness regression) associated with each level of resolution.
The techniques presented in this case study can be combined
with the ones described for case study 1 to investigate another
important methodological problem in macroecology – the
influence of range size on statistical inference (Jetz & Rahbek,
2002; Tello & Stevens, 2010). Appendix S5 shows the influence of
range size on the body mass–species richness regression slope
using the case study on the wrens.
CONCLUSION
rangeMapper is an open source front end R package for macro-
ecological studies designed to serve as an interface between the
spatial and the statistical tools offered through the R environ-
ment. By choosing three case studies covering various technical
and conceptual issues and a dataset of the global geographical
distribution of more than 8000 bird species we demonstrated
rangeMapper’s capabilities to address a wide array of questions.
a
b −1.0
−0.5
0.0
0.5
1.0
1
34
2
Figure 3 (a) Global distribution of the slope of the standardized range size–body size regression. (b) Statistically significant (P < 0.05) cellsafter controlling for multiple testing (Benjamini & Yekutieli, 2001) are shown in red. Numbers on the map refer to avian studies reviewed inGaston & Blackburn (1996, Table 1): (1) North American land birds, (2) Australian birds, (3) British birds, (4) Finnish passerines.
Macroecology of life history traits
Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd 949
FINAL REMARK
When you use rangeMapper, we ask you to cite R together with
this publication followed by the package version you used.
ACKNOWLEDGEMENTS
We thank Allen Hurlbert and three anonymous referees for
helpful comments on an earlier version of this manuscript.
REFERENCES
Benjamini, Y. & Yekutieli, D. (2001) The control of the false
discovery rate in multiple testing under dependency. Annals of
Statistics, 29, 1165–1188.
Bivand, R. (2009) classInt: choose univariate class intervals.
R package version 0.1-14. http://CRAN.R-project.org/package=classInt (accessed 21 December 2009).
Bivand, R., Pebesma, E.J. & Gómez-Rubio, V. (2008) Applied
spatial data analysis with R. Springer, New York.
Blackburn, T.M. (2004) Method in macroecology. Basic and
Applied Ecology, 5, 401–412.
Brown, L.H., Urban, E.K. & Newmann, K. (1982–2004) The
birds of Africa (in 7 volumes). Academic Press, London.
Ceballos,G. & Ehrlich,P.R. (2006) Global mammal distributions,
biodiversity hotspots, and conservation. Proceedings of the
National Academy of Sciences USA, 103, 19374–19379.
Chambers, J.M. (2008) Software for data analysis: programming
with R. Springer, New York.
Cramp, S. & Simmons, K.E.L. (1977–1994) Handbook of the birds
of Europe, the Middle-East and North-Africa: the Birds of the
Western Palearctic (in 9 volumes). Oxford University Press,
Oxford.
Davies, R.G., Orme, C.D.L., Storch, D., Olson, V.A.,
Thomas, G.H., Ross, S.G., Ding, T.S., Rasmussen, P.C.,
Bennett, P.M., Owens, I.P.F., Blackburn, T.M. & Gaston,
K.J. (2007) Topography, energy and the global distri-
bution of bird species richness. Proceedings of the
Royal Society B: Biological Sciences, 274, 1189–
1197.
Dunning, J.B. (2008) CRC handbook of avian body masses, 2nd
edn. CRC Press, Boca Raton, FL.
Fritz, S.A. & Purvis, A. (2010) Phylogenetic diversity does not
capture body size variation at risk in the world’s mammals.
Proceedings of the Royal Society B: Biological Sciences, 277,
2435–2441.
Gaston, K.J. (2000) Global patterns in biodiversity. Nature, 405,
220–227.
Gaston, K.J. & Blackburn, T.M. (1996) Range size–body size
relationships: evidence of scale dependence. Oikos, 75, 479–
485.
Gaston, K.J. & Blackburn, T.M. (2000) Pattern and
process in macroecology. Blackwell Science, Oxford.
Gaston, K.J., Chown, S.L. & Evans, K.L. (2008) Ecogeographical
rules: elements of a synthesis. Journal of Biogeography, 35,
483–500.
Hijmans, R.J. & Etten, J.V. (2010) raster: geographic analysis and
modeling with raster data. R package version 1.5-16. http://
CRAN.R-project.org/package=raster (accessed 11 October
2011).
del Hoyo, J., Elliott, A., Sargatal, J. & Christie, D.A. (1992–2010)
Handbook of the birds of the world (in 15 volumes). Lynx
Edicions, Barcelona.
Hurlbert, A.H. & Jetz, W. (2007) Species richness, hotspots,
and the scale dependence of range maps in ecology and
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5
10
15
20
a
b
c
100 200 300 400 500
−0.
07−
0.06
−0.
05−
0.04
−0.
03−
0.02
Grid cell size (km2)
Slo
pe o
f bod
y m
ass
- s
peci
es r
ichn
ess
regr
essi
on
Figure 4 Global distribution of (a) square-root transformed species richness and (b) log10 median body mass at 1° resolution. (c) Theslope of the spatial regression (SAR) model between log10 median body mass and species richness (square-root transformed) depends onthe spatial resolution of analysis. SAR slopes are presented together with their 95% confidence intervals.
M. Valcu et al.
Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd950
conservation. Proceedings of the National Academy of Sciences
USA, 104, 13384–13389.
James, D.A. (2010) RSQLite: SQLite interface for R. R package
version 0.9-4. http://CRAN.R-project.org/package=RSQLite
(accessed 24 November 2010).
Jetz, W. & Rahbek, C. (2002) Geographic range size and deter-
minants of avian species richness. Science, 297, 1548–1551.
Jetz, W., Sekercioglu, C.H. & Bohning-Gaese, K. (2008) The
worldwide variation in avian clutch size across species and
space. PLoS Biology, 6, 2650–2657.
Keitt, T.H., Bivand, R., Pebesma, E. & Rowlingson, B. (2010)
rgdal: bindings for the geospatial data abstraction library. R
package version 0.6-28. http://CRAN.R-project.org/package=rgdal (accessed 24 July 2010).
Marchant, S. & Higgins, P.J. (1990–2006) Handbook of Austra-
lian, New Zealand and Antarctic birds (in 7 volumes). Oxford
University Press, Melbourne, Vic..
Neuwirth, E. (2007) RColorBrewer: ColorBrewer palettes. R
package version 1.0-2. http://CRAN.R-project.org/package=RColorBrewer (accessed 22 October 2007).
Olson, V.A., Davies, R.G., Orme, C.D., Thomas, G.H., Meiri, S.,
Blackburn, T.M., Gaston, K.J., Owens, I.P. & Bennett, P.M.
(2009) Global biogeography and ecology of body size in birds.
Ecology Letters, 12, 249–259.
Orme, C.D.L., Davies, R.G., Burgess, M., Eigenbrod, F., Pickup,
N., Olson, V.A., Webster, A.J., Ding, T.S., Rasmussen, P.C.,
Ridgely, R.S., Stattersfield, A.J., Bennett, P.M., Blackburn,
T.M., Gaston, K.J. & Owens, I.P.F. (2005) Global hotspots of
species richness are not congruent with endemism or threat.
Nature, 436, 1016–1019.
Pebesma, E.J. & Bivand, R.S. (2005) Classes and methods for
spatial data in R. R News, 5, 9–13.
Petchey, O.L. & Gaston, K.J. (2006) Functional diversity: back to
basics and looking forward. Ecology Letters, 9, 741–758.
Pigot, A.L., Owens, I.P.F. & Orme, C.D.L. (2010) The environ-
mental limits to geographic range expansion in birds. Ecology
Letters, 13, 705–715.
Pinheiro, J.C. & Bates, D.M. (2000) Mixed-effects models in S and
S-PLUS. Springer, New York.
Powney, G.D., Grenyer, R., Orme, C.D.L., Owens, I.P.F. & Meiri,
S. (2010) Hot, dry and different: Australian lizard richness is
unlike that of mammals, amphibians and birds. Global Ecology
and Biogeography, 19, 386–396.
Quantum GIS Development Team (2011) Quantum GIS geo-
graphic information system. Open source geospatial foundation
project. http://qgis.osgeo.org (accessed 1 August 2011).
R Development Core Team (2010) R: a language and environ-
ment for statistical computing. R Foundation for Statistical
Computing, Vienna.
Rahbek, C. & Graves, G.R. (2001) Multiscale assessment of pat-
terns of avian species richness. Proceedings of the National
Academy of Sciences USA, 98, 4534–4539.
Rangel, T.F., Diniz, J.A.F. & Bini, L.M. (2010) SAM: a compre-
hensive application for spatial analysis in macroecology. Ecog-
raphy, 33, 46–50.
Ridgely, R.S. & Tudor, G. (2009) Field guide to the songbirds of
South America: the passerines. University of Texas Press,
Austen, TX.
Smith, F.A., Lyons, S.K., Ernest, S.K.M. & Brown, J.H. (2008)
Macroecology: more than the division of food and space
among species on continents. Progress in Physical Geography,
32, 115–138.
Symonds, M.R.E. & Johnson, C.N. (2006) Range size–
abundance relationships in Australian passerines. Global
Ecology and Biogeography, 15, 143–152.
Tello, J.S. & Stevens, R.D. (2010) Multiple environmental deter-
minants of regional species richness and effects of geographic
range size. Ecography, 33, 796–808.
Valcu, M. & Dale, J. (2011) Rangemapper: a platform for the study
of macroecology of life history traits. R package version 0.0-6.
http://cran.r-project.org/web/packages/range
Mapper/vignettes/rangeMapper.pdf (accessed 8 April 2011).
Venables, W.N. & Ripley, B.D. (2002) Modern applied statistics
with S, 4th edn. Springer, New York.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
Appendix S1 Maps of endemic species richness and body mass
relative diversity.
Appendices S2–S5 R code accompanying case studies.
As a service to our authors and readers, this journal provides
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BIOSKETCHES
Mihai Valcu is a behavioural ecologist with a strong
interest in spatial ecology. His research focuses on
spatial aspects of avian reproductive behaviour and
life-history strategies.
James Dale is a senior lecturer in ecology at Massey
University. His research mostly focuses on social
behaviour in animals, with an emphasis on
communication, sexual selection, individual recognition
and reproductive strategies.
Bart Kempenaers is a behavioural ecologist interested
in the diversity of life-history traits. He is director of
the Department of Behavioural Ecology and
Evolutionary Genetics, Max Planck Institute for
Ornithology.
Editor: José Alexandre F. Diniz-Filho
Macroecology of life history traits
Global Ecology and Biogeography, 21, 945–951, © 2012 Blackwell Publishing Ltd 951
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