Docstoc

Threat of extinction in Neotropical birds

Document Sample
Threat of extinction in Neotropical birds Powered By Docstoc
					Animal Conservation (2004) 7, 161–168   C   2004 The Zoological Society of London. Printed in the United Kingdom DOI:10.1017/S1367943004001246



Ecological correlates of the threat of extinction in Neotropical
bird species


G. S. Gage1 , M. de L. Brooke1 , M. R. E. Symonds1,2 and D. Wege3
1 Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
2 School of Tropical Biology, James Cook University, Townsville, Qld 4811, Australia
3 BirdLife International, Wellbrook Court, Girton Road, Cambridge CB3 0NA, UK

(First received 14 October 2002; revised received 3 October 2003; accepted 11 October 2003)


Abstract
Predicting the threat of extinction aids efficient distribution of conservation resources. This paper utilises a
comparative macroecological approach to investigate the threat of extinction in Neotropical birds. Data on
ecological variables for 1708 species are analysed using stepwise regression to produce minimum adequate
models, first using raw species values and then using independent contrasts (to control for phylogenetic
effects). The models differ, suggesting phylogeny has significant effects. The raw species analysis reveals that
number of zoogeographical regions occupied, elevational range and utilisation of specialised microhabitats
were negatively associated with threat, while minimum elevation and body mass were positively associated,
whereas the independent contrasts analysis only identifies zoogeographical regions as important. Confining the
analysis to the 582 species restricted to a single zoogeographical region reveals elevational range and number
of habitats occupied to be negatively correlated with threat whether the analysis is based on the raw data or on
independent contrasts. Analysis of four contrasting zoogeographical regions highlights regional variation in the
models. In two Andean regions the threat of extinction declines as the elevation range across which the species
occurs increases. In the presence of substantial human populations on high Andean plateaus, a species with a
greater elevational range may be more likely to persist at some (relatively) unsettled altitudes. In Central South
America, the strongest predictor of threat is minimum elevation of occurrence: species with a lower minimum
are less threatened. The minimum elevation result suggests that lowland species experiencing an ecological
limit to their minimum elevation (min. elevation > 0 m) may be more at risk than those not experiencing such
a limit (min. elevation = 0 m). Finally, in southern Amazonia, where there is little altitudinal variation, the
only weak predictors of threat are body size, larger species being more threatened, and number of habitats,
species occupying more habitats being less threatened. These contrasting results emphasise the importance of
undertaking extinction risk analyses at an appropriate geographical scale. Since the models explained only a
low percentage of total variance in the data, the effects of human-mediated habitat disturbance across a wide
range of habitats may be important.



INTRODUCTION                                                               risk in two mammalian clades, carnivores and primates,
                                                                           in terms of such parameters as population density and
Extinction rates are rapidly increasing and recent rates may
                                                                           geographical range size. Predicting the threat of extinction
be 100–1000 times those of pre-human times (Raup, 1978;
                                                                           is of theoretical interest to population biologists and of
Pimm et al., 1995). Future rates may be still higher. Studies
                                                                           practical importance to both conservation biologists and
of non-random patterns of species extinction (Raup, 1994;
                                                                           wildlife managers (Pimm, Jones & Diamond, 1988). For
Bennett & Owens, 1997; McKinney, 1997; Russell et al.,
                                                                           conservation biologists, such ‘knowledge . . . may provide
1998; Purvis et al., 2000a; von Euler, 2001) and of the
                                                                           operational paradigms and parameters within which
variables associated with the threat of extinction (Gaston
                                                                           successful conservation strategies can be developed’
& Blackburn, 1995; Bennett & Owens, 1997; Purvis et al.,
                                                                           (Winker, 1998). For example, it may aid the identification
2000b) suggest it may be possible to predict the threat of
                                                                           of species not yet threatened but likely to become so
extinction. For example, Purvis et al. (2000b) explained
                                                                           in the future if pre-emptive conservation measures are
nearly 50% of the between-species variation in extinction
                                                                           not taken. Successful prediction will also allow current
                                                                           estimates of extinction rates to be revised (Manne,
All correspondence to: M. de L. Brooke. Fax: +44-1223-336676;              Brooks & Pimm, 1999). Thus, understanding extinction
E-mail: mb10005@cus.cam.ac.uk                                              patterns is one step in allowing conservation biologists
162                                                  G. S. GAGE ET AL.

to devise management strategies (Goerck, 1997). This            Table 1. Pearson correlation coefficients between the logarithmic-
paper utilises a comparative approach to investigate            ally transformed values of the five continuous variables used in this
how ecological factors relate to the threat of extinction       study of 1708 species of Neotropical bird
amongst Neotropical bird species. Individual species will
                                                                                                                   No. of
function as replicates in assessing the association between                    Minimum Elevational No. of          Zoogeographical
ecological variables and the threat of extinction.                             Elevation Range     Habitats        Regions
   Parker, Stotz & Fitzpatrick (1996) compiled compre-
hensive information on the ecology and zoogeography             Body Mass − 0.107∗∗∗ 0.021              0.054∗     0.203∗∗∗
                                                                                                             ∗∗∗
of Neotropical bird species. Their unparalleled work in         Minimum              0.035            − 0.206    − 0.312∗∗∗
this field was based on their belief that knowledge of             Elevation
bird communities can be used to prioritise effectively the      Elevational                             0.350∗∗∗     0.329∗∗∗
allocation of conservation resources and aid the selection        Range
                                                                No. of                                               0.362∗∗∗
of sites as conservation targets.
                                                                  Habitats
   To this end, we examine how variation in the threat
of extinction varies with ecological traits on a macro-         ∗
                                                                    P < 0.05; ∗∗∗ P < 0.001.
ecological scale. Significant statistical correlation may
indicate that the ecological trait is associated with
the threat of extinction. More specifically, we analyse
how the estimated risk of extinction varies with ele-           extinction risk uses the IUCN (The World Conservation
vational distribution, geographical distribution by habitat     Union) categories developed by Mace & Stuart (1994).
and zoogeographical region, body mass and dependence            The criteria used are primarily based on a combination of
on microhabitat or undisturbed edge habitats. The               population and range size and their rate of decline. The
relationship between body mass and extinction risk has          threat of extinction was scored on a five point ordinal scale
already been investigated by several authors, whose             where: 1 = not currently threatened; 2 = near threatened;
studies variously report positive, negative or no relation-     3 = vulnerable; 4 = endangered; 5 = critical (after Bennett
ship (see Terborgh & Winter, 1980; Diamond, 1984; Pimm          & Owens, 1997). This scale is viewed as a continuous
et al., 1988; Soul´ et al., 1988; Laurance, 1991; Gaston &
                  e                                             variable (after Purvis et al., 2000b). Species not listed
Blackburn, 1995; Bennett & Owens, 1997; Owens &                 in Hilton-Taylor (2000) were classified as ‘not currently
Bennett, 2000).                                                 threatened’. A total of 1556 species was so classified,
                                                                while 72 species were near-threatened, 49 vulnerable,
                                                                21 endangered and 10 critical.
METHODS
Data on Neotropical bird species were compiled into a           The seven predictor variables
database to allow comparative testing of a single dataset
                                                                Male Body Mass: data were taken from Dunning (1992).
through the tandem methods of interspecific analysis using
                                                                Where more than one mean body mass was reported the
raw data and using independent contrasts (Felsenstein,
                                                                arithmetic mean, weighted according to sample size, was
1985). Ecological data were primarily taken from the
                                                                calculated. Although female body mass may be a more
comprehensive reference database of Parker et al. (1996);
                                                                accurate reflection of a species’ fecundity, the resulting
data on body masses were taken from Dunning (1992).
                                                                reduction in sample size meant that the results were
The full dataset is available on request.
                                                                potentially subject to sampling effects and, therefore, we
   The data were compiled into a single database of
                                                                used male body mass.
1708 Neotropical bird species, approximately half of
                                                                   Minimum Elevation and Elevational Range: elevational
the Neotropical species. For every species, there was
                                                                data in Parker et al. (1996) included the minimum and
a response variable and seven predictor variables, each
                                                                maximum elevational distribution for all 1708 species.
explained below. A total of 125 species listed as having
                                                                The former was Minimum Elevation in our study. Both of
primary habitats of saltwater marshes, coastal sand
                                                                Parker et al.’s elevational fields used the term ‘L’ to refer
beaches/mudflats and coastal or pelagic waters was
                                                                to low-relief lowland areas. This was recoded as ‘0 m’
excluded because their ecology is so different to that of the
                                                                under minimum elevation and ‘500 m’ under maximum
terrestrial majority of species, and the threats they face,
                                                                elevation, 500 m being the upper elevational limit of
such as wetland drainage, are correspondingly different.
                                                                lowland regions in Parker et al. (1996). The difference
These excluded species comprised some or all of the
                                                                between the maximum and minimum elevations of a
members of the orders Procellariiformes, Pelecaniformes,
                                                                species was calculated by straightforward subtraction and
Sphenisciformes, Charadiiformes, Ciconiiformes and
                                                                is referred to as the Elevational Range of a species. These
Gruiformes.
                                                                two elevation measures are not significantly correlated
                                                                (Table 1), an important consideration when undertaking
                                                                stepwise regression (see Analysis, below). Together, they
The response variable
                                                                give a measure of the altitude occupied by a species and
Threat of Extinction: measures of the threat of extinction      whether it occurs over a narrower or wider altitudinal
were taken from Hilton-Taylor (2000). This listing of avian     range.
                                           Extinction risk in Neotropical birds                                      163

   No. of Habitats and No. of Zoogeographical Regions:         1985). The results provide a practical comparison of
habitats refers to 41 habitat categories based on how          the use of interspecific analyses of raw data and
Neotropical birds appear to distinguish vegetation type        independent contrasts (Blackburn & Gaston, 1998) and
(Parker et al., 1996). Zoogeographical regions refers to       allow significant conclusions to be accepted with greater
the 22 regions of the Neotropics identified by Parker et al.    statistical validity.
(1996) on the basis of major vegetation types and                 The Comparative Analysis by Independent Contrasts
physiogeographical features. For each species the number       (CAIC) program (Purvis & Rambaut, 1995) employs
of regions and habitats occupied is tallied on a continuous    Pagel’s version of Felsenstein’s method (Pagel, 1992) to
scale.                                                         produce statistically independent contrasts at each node
   Microhabitat: species with a critical dependence on         of a phylogeny. The threat of extinction was taken as the
a specific feature of a habitat type are listed as being        dependent, or response, variable. For comparative studies
associated with a microhabitat (Parker et al., 1996).          taxa must represent equivalent and comparable units and
The eight individual microhabitats (army ant swarms,           so a single phylogeny was used to avoid taxonomic
bamboo, ground bromeliads, rocky outcrops or caves,            differences. The full Sibley & Ahlquist (1990) phylogeny
streamside, treefalls, vine tangles or bamboo/vine tangles)    based on DNA–DNA hybridisation comparisons was used.
are typically restricted to forest areas. In this analysis     Data on lengths were not available and so branch lengths
Microhabitat is a dichotomous variable; species either         were set under CAIC’s default assumptions to equal
show a dependence upon any microhabitat or they                lengths.
do not.                                                           While CAIC’s BRUNCH programme may analyse
   Edge: edge species listed are those whose primary           dichomotomous variables one at a time, we wished
habitat comprises the edges of undisturbed habitat. Note       to examine the predictive effect of several variables
that they are distinct from the species that have adapted      simultaneously. Moreover, for multiple regression, all
to secondary forests. This variable is dichotomous; species    contrasts must be generated within the same analysis and,
are either dependent upon an edge habitat or they are          therefore, we had to exclude the dichotomous variables
not.                                                           Edge and Microhabitat, from the analysis of independent
                                                               contrasts. The remaining six continuous variables (Threat
                                                               of Extinction, Body Mass, Minimum Elevation, Eleva-
Analysis                                                       tional Range, Habitats and Zoogeographical Regions)
                                                               were logarithmically transformed (as mentioned above)
All data were analysed using the Minitab statistical com-
                                                               and analysed using CAIC’s CRUNCH programme.
puter package. Continuous variables were logarithmically
                                                               Independent contrasts were tested by regression through
transformed before analysis and, once transformed, were
                                                               the origin (Garland, Harvey & Ives, 1992). If the
found to be normally distributed. In the presentation of
                                                               regression coefficients differed significantly from zero,
results, the regression coefficients given are based on the
                                                               then the correlated changes between the two characters can
analysis using transformed variables.
                                                               be said to have occurred independently of phylogenetic
   An aggregated minimum adequate model (MAM) was
                                                               association (Cotgreave & Harvey, 1992).
derived using stepwise regression with forward selection
                                                                  To examine regional variation in the predictors of
and model simplification (Zar, 1999). The chosen cut-
                                                               extinction risk, habitat, elevational and body mass data
off P value was 0.15. The result is a model representing
                                                               were analysed for the species dwelling in four individual
a particular statistical likelihood and should not be
                                                               zoogeographical regions, two of high montane habitat
considered as the true or complete model. For instance,
                                                               and two of lowland (descriptions were taken from Parker
the inclusion of additional variables or changes to the P
                                                               et al., 1996):
value may result in a different model.
   The results of stepwise regression may be unreliable        Northern Andes: all the montane regions, from the coastal
if the independent variables are themselves highly               cordilleras of Venezuela, south to Porculla Pass and the
correlated. For this reason, Table 1 shows the correlation       Rio Maranon in Peru.
matrix for the five continuous variables assessed in our        Central Andes: the montane region and associated valleys
study: no correlation coefficients exceed 0.4.                    from Porculla Pass and the Maranon Valley, Peru, south
   Evolutionary relatedness amongst species means that           to Tucuman and Catamarca, Argentina and northern
each species is not an independent data point and                Atacama, Chile.
that sample sizes and the degrees of freedom available         Central South America: the lowland, open habitats
for testing statistical significance are artificially inflated      stretching from Maranhao east to Rio Grande de
(Felsenstein, 1985; Harvey, 1996). This is due to phy-           Norte and south through interior eastern Brazil,
logenetically related species being more similar than            eastern Bolivia and Paraguay to Rio Negro in central
would be expected by chance alone and results in                 Argentina.
elevated type I error rates (false rejection of a null hypo-   Amazonia South: the area south of the Rio Maranon, Rio
thesis). In order to account for variation in the degree of      Solimoes and Rio Amazonas, west to the base of the
common phylogenetic association the data were assessed,          Andes and south and east to the edge of Amazonian
first, using interspecific analysis of raw data and, second,       forest in Santa Cruz, Bolivia and Mato Grosso, Goias
using phylogenetically independent contrasts (Felsenstein,       and Maranhao, Brazil.
164                                                             G. S. GAGE ET AL.

Table 2. Results of linear regression of extinction risk of Neotropical   y-intercept) of the raw data. The resultant model, which
birds on various parameters                                               explains 9.97% of the total variance in extinction risk, is
                                                                          (for log-transformed variables):
Predictor                   N       t value      P value   R2 adj. (%)
                                                                           Threat of Extinction
No. of Zoogeographical      1708    − 11.13      < 0.001   6.7
  Regions                                                                     = 0.567 − 0.088 No. of Zoogeographical Regions
Minimum Elevation           1708       4.54      < 0.001   1.1
Elevational Range           1708     − 8.71      < 0.001   4.2                   − 0.070 Elevational Range + 0.023 Body Mass
Body Mass                   1708       2.53        0.011   0.3
                                                                                 + 0.005 Minimum Elevation − 0.051 Microhabitat
No. of Habitats             1708     − 6.72      < 0.001   2.5
Edge                        1708     − 2.84        0.005   0.4                   − 0.043 Edge
Microhabitat                1708     − 1.24        0.214   0.0
                                                                             Increasing No. of Zoogeographical Regions and Ele-
All parameter values were logarithmically transformed before              vational Range, or dependence upon a Microhabitat or
analysis.                                                                 possibly an Edge, are all associated with a reduction
                                                                          in the threat of extinction. Increasing Body Mass and
                                                                          Minimum Elevation increases the threat of extinction. Of
   Following the methods of the cross-continent analysis,
                                                                          these predictors, No. of Zoogeographical Regions is the
we focused on the five continuous variables, Minimum
                                                                          most significant. A small geographical range is a major
Elevation, Elevational Range, No. of Zoogeographical
                                                                          correlate of threat: so much so that its inclusion in the
Regions, No. of Habitats and Body Mass for the regional
                                                                          model may obscure other correlates.
analyses. These were logarithmically transformed and
                                                                             Therefore, to explore in more detail predictors of
independent contrasts produced by CAIC were entered
                                                                          threat for species with modest range size, we repeated
into a stepwise regression as above.
                                                                          the analysis for the 582 species that are confined to a
                                                                          single zoogeographical region. In this case, the model,
                                                                          explaining 8.56% of the variation, included four variables
RESULTS                                                                   (all P < 0.05). It was:
Non-phylogenetic analysis of raw species values                              Threat of Extinction
Table 2 shows the results of the first step of stepwise                           = 1.067 − 0.127 Elevational Range
regression, which is equivalent to univariate analysis of
each variable. All variables, except Microhabitat, were                             − 0.134 No. of Habitats − 0.186 Microhabitat
significant at the set P-value of 0.15 and, in fact, at 0.05.                        + 0.032 Body Mass
   The most significant variable, No. of Zoogeographical
Regions, was included in the model and the regression                       Thus, the model showed considerable similarities to
step was repeated with each remaining variable being                      the trans-continental model, but No. of Habitats, which is
included in the model in turn. Table 3 shows the step-                    moderately well correlated with No. of Zoogeographical
by-step development of the stepwise regression (with a                    Regions (see Table 1), now entered the model.


Table 3. The step-by-step development of the regression model using interspecific analysis to identify correlates of extinction risk in 1708
species of Neotropical bird

Step                                    1                   2                3               4                 5                 6

Constant                                   0.1705             0.6392           0.5390          0.5517            0.5660            0.5673
No. Zoogeographical Regions              − 0.1044           − 0.0865         − 0.0964        − 0.0882          − 0.0891          − 0.0883
  t-value                               − 11.13∗∗∗          − 8.79∗∗∗        − 9.64∗∗∗       − 8.34∗∗∗         − 8.42∗∗∗         − 8.35∗∗∗
Elevational Range                                           − 0.068          − 0.065         − 0.070           − 0.071           − 0.070
  t-value                                                   − 5.52∗∗∗        − 5.31∗∗∗       − 5.61∗∗∗         − 5.70∗∗∗         − 5.64∗∗∗
Body Mass                                                                      0.0236          0.0241            0.0236            0.0228
  t-value                                                                      4.79∗∗∗         4.88∗∗∗           4.78∗∗∗           4.61∗∗∗
Minimum Elevation                                                                              0.0054            0.0052            0.0051
  t-value                                                                                      2.36∗             2.29∗             2.22∗
Microhabitat                                                                                                   − 0.051           − 0.051
  t-value                                                                                                      − 1.95(∗)         − 1.98∗
Edge                                                                                                                             − 0.043
  t-value                                                                                                                        − 1.81(∗)
R2 adj. (%)                                   6.72               8.30            9.46            9.70              9.85            9.97

All parameter values were logarithmically transformed before analysis, and the regression coefficients refer to the transformed values.
(∗)
    P < 0.1; ∗ P < 0.05; ∗∗ P < 0.01; ∗∗∗ P < 0.001.
                                                    Extinction risk in Neotropical birds                                             165

Table 4. The step-by-step development of the regression model using independent contrasts to identify correlates of extinction risk in
those Neotropical bird species confined to a single zoogeographical region

                                                                                                             N
Step                             1                         2                      3                          Species           Contrasts

Elevational Range                − 0.154                   − 0.132                − 0.125                    582               182
  t-value                        − 5.74∗∗∗                 − 4.76∗∗∗              − 4.49∗∗∗
No. of Habitats                                            − 0.115                − 0.118                    582               182
  t-value                                                  − 3.00∗∗               − 3.10∗∗
Body Mass                                                                           0.035                    582               182
  t-value                                                                           2.54∗
R2 adj. (%)                          5.21                      6.50                 7.37

All parameter values were logarithmically transformed before analysis, and the regression coefficients refer to the transformed values.
∗
  P < 0.05; ∗∗ P < 0.01; ∗∗∗ P < 0.001.



Phylogenetic analysis of independent contrasts
                                                                            When we undertook the regional analyses within CAIC
When we performed a stepwise regression (through the                     using the five continuous variables, No. of Zoogeo-
origin) of the 381 independent contrasts, the resultant                  graphical Regions appeared as a significant variable
model included only a single variable, No. of Zoogeo-                    (P < 0.05) in every model, along with 0–2 other variables.
graphical Regions, and explained 8.6% of the variation                   Thus, as was true of the continent-wide analysis, the
around the origin. Increasing No. of Zoogeographical                     regional analyses indicated that number of regions, a
Regions was associated with reduced threat of extinction                 surrogate for geographical range size, was an important
(t = − 6.06, P < 0.001). Thus, while No. of Zoogeo-                      variable. We then repeated the analyses excluding No.
graphical Regions was significant in both the raw data                    of Zoogeographical Regions. The results for the four
and independent contrasts models, Elevational Range,                     individual regions are shown in Table 5. Within both
Body Mass, Minimum Elevation and Microhabitat were                       the two primarily montane regions (Northern Andes
significant only in the former (where the sample size was                 and Central Andes), Elevational Range was the most
over four times greater).                                                significant predictor variable. The direction of the
   Confining the independent contrasts analysis to the                    effect was similar in both regions: species with greater
582 species occurring in just one region yielded a model                 elevational ranges were less threatened. Within Central
that explained 7.4% of the variation about the origin                    South America, Minimum Elevation was the most
(Table 4). With the exception of Microhabitat, the                       significant predictor, but No. of Habitats also entered the
variables included (Elevational Range, No. of Habitats                   model. Finally, within Amazonia South, Body Mass and
and Body Mass) were the same as appeared in the raw                      No. of Habitats entered the model, but both were only
data analysis (see above).                                               marginally significant. The percentage of variation about



Table 5. Analysis of extinction risk of birds in four zoogeographical regions

                          Northern Andes†                      Central Andes‡     Central South America§           Amazonia South
Step                      1                  2                 1                  1            2                   1            2

Elevational Range         − 0.212            − 0.247           − 0.128
  t-value                 − 4.10∗∗∗          − 4.39∗∗∗         − 2.60∗
Minimum Elevation                                                                 0.135            0.122
  t-value                                                                         7.82∗∗∗          6.80∗∗∗
Body Mass                                                                                                                         0.027
  t-value                                                                                                                         1.57(∗)
No. of Habitats                                  0.086                                         − 0.090             − 0.033      − 0.034
  t-value                                        1.53(∗)                                       − 2.12∗             − 1.88(∗)    − 1.97(∗)

All parameter values were logarithmically transformed before analysis, and the regression coefficients refer to the transformed values.
(∗)
    P < 0.15; ∗ P < 0.05; ∗∗∗ P < 0.001.
†
  N = 332 species and 120 contrasts.
‡
  N = 345 species and 128 contrasts.
§
  N = 346 species and 135 contrasts.
  N = 512 species and 166 contrasts.
166                                                 G. S. GAGE ET AL.

the origin explained by these models varied considerably       extrinsic factors. However, here we investigated variation
as follows: Northern Andes 12.7%, Central Andes 4.3%,          in ecological factors that might predispose Neotropical
Central South America 32.8% and Amazonia South 2.4%.           bird species to the threat of extinction. Major determinate
                                                               factors, such as human-mediated habitat destruction, lay
                                                               outside the scope of the current investigation and their
DISCUSSION                                                     effect may have limited the explanatory power of the
                                                               analyses, a point emphasised by Owens & Bennett (2000).
Comments on analysis
                                                                  In fact, despite the inclusion of five significant predictor
Analysis of the raw data illustrates both the importance       variables, only 10% of the total variance was explained
and power of stepwise regression and MAM techniques.           by the model using the raw data (Table 2). The results
Probably because of the correlation with No. of Zoogeo-        of other studies (Gaston & Blackburn, 1995; Bennett &
graphical Regions, the stepwise model did not contain          Owens, 1997) indicate that the inclusion of life history and
the variable No. of Habitats, which was significant when        fecundity data would possibly enhance the explanatory
considered as the sole predictor variable in the univariate    power of the model, but no relevant comprehensive data
analysis.                                                      sets are available.
   The tandem methods of analysis, using raw data and             In many cases, the threat of extinction will be sub-
using independent contrasts, produced different models,        stantially due to large-scale habitat destruction operating
as might be expected from the fact that both methods           over large geographical areas and across various habitats.
are imperfect (c.f. Ricklefs & Starck, 1996). Thus, while      The ecological features of a species will then only weakly
No. of Zoogeographical Regions was significant in both          explain a species’ risk of extinction because, put simply,
the raw data and the independent contrasts models for the      a significant fraction of species will be at risk. This effect
entire continent, Elevational Range, Body Mass, Minimum        of large-scale habitat destruction could be a reason for the
Elevation and Microhabitat were significant only in the         low percentage of total variance explained by the analysis.
former.                                                           The decline in threat of extinction with occupation of
   Our independent contrasts analysis is, of course,           more distinct zoogeographical regions, identified by both
dependent on the phylogeny used. Sibley & Ahlquist’s           continent-wide models, is intuitively reasonable. As the
(1990) phylogeny is, like any phylogeny, imperfect. In         number of zoogeographical regions occupied increases,
particular, its lack of resolution (only 381 contrasts were    the geographical area occupied by the species increases,
produced from 1708 species) results in a substantial loss of   reducing the species’ exposure to smaller scale local
information (Purvis, Gittleman & Luh, 1994) and this may       effects. The results, therefore, mirror those of Purvis
explain why fewer variables were identified as signficant        et al. (2000b) who found that a greater geographical
in the independent contrasts analysis. However, there          range among mammals was associated with a reduced
is considerable evidence that using even a moderately          threat. However, and perhaps unexpectedly, the number
accurate, if imperfect, phylogeny produces more accurate       of habitats occupied by a bird species in our study did
results than using no phylogeny at all (Symonds,               not appear as a significant predictor of extinction risk
2002).                                                         in either multivariate analysis. This may have arisen
   The generation of 381 contrasts from 1708 species may       because number of habitats is correlated with number of
be due to one of two factors: a lack of resolution or          zoogeographical regions (Table 1) and it is the latter that
accuracy in the phylogeny, or an accurate representation       is the more important predictor of threat.
of polytomies found in the bird phylogeny. The full               This suggestion is reinforced by the analyses of those
Sibley & Ahlquist (1990) phylogeny is based on DNA–            species confined to a single region (Table 4). Here it
DNA hybridisations and is thought to reflect true bran-         emerged that species that were able to utilise more habitats
ching patterns more accurately than other phylogenies          were less likely to be threatened.
currently available (Cotgreave & Harvey, 1992). However,          The continent-wide raw data model, but not the
the phylogeny (Sibley & Ahlquist, 1990) is relatively          independent contrasts model, identified four further
unresolved below the subfamily level and this may              significant variables that we now consider in turn, since the
contribute to the redundancy of data. Polytomies do            discussion is of direct relevance to the models emerging
exist in the true phylogeny and are limited by issues of       from the regional analysis.
speciation and species definition. Moreover, analysis using        Larger body mass was associated with an increased
independent contrasts is fairly robust to the ‘bushiness’ of   risk of extinction in the raw data model (Owens &
phylogenetic trees caused by polytomies (Purvis et al.,        Bennett, 2000). We cannot determine whether this is a
1994).                                                         direct or indirect effect. Body mass may correlate with
                                                               other variables, which are, in turn, correlated with the
                                                               threat of extinction (Pimm et al., 1988; Lawton, 1994;
Comments on extinction risk
                                                               Gaston & Blackburn, 1995). For instance, body size may
Extrinsic variables, as opposed to ecological variables, are   be negatively correlated with species abundance (Peters,
likely to be significant in explaining variance in the threat   1983), which may itself be negatively correlated with the
of extinction; Diamond’s (1984) ‘evil quartet’ of habitat      threat of extinction (Pimm et al., 1988; Laurance, 1991).
loss, over-exploitation, introduced species and chains         Alternatively body size may be positively correlated with
of extinction provides well-known examples of such             susceptibility to environmental perturbation (Lindstedt &
                                            Extinction risk in Neotropical birds                                                 167

Boyce, 1985) and this may be positively correlated with         (Table 3). This was also true when the analysis was
the threat of extinction (Pimm, 1991; Lawton, 1994).            restricted to the species confined to a single zoogeo-
Other ecological traits that might be related to both           graphical region. For technical reasons this effect could
body size and the threat of extinction are foraging strata,     not be investigated when phylogenetic effects were
longevity, fecundity and dispersal ability (see Pimm            controlled. If there is an effect, it is almost certainly
et al., 1988; Kunin & Gaston, 1993; Lawton, 1994).              small and it perhaps arises because at least some
When phylogenetic effects are controlled, the relationship      microhabitats (e.g. rocky outcrops, caves) may remain
between male body mass and the threat of extinction is no       relatively unaffected by regional habitat damage.
longer significant.                                                 Within the two montane regions (Central Andes and
   The raw data model predicted that increased elevational      Northern Andes) the threat of extinction can be predicted
range was correlated with a reduced risk of extinction. A       from data on Elevational Range (Table 5). Reduced
species with a larger elevational range has a greater chance    Elevational Range increases the threat of extinction,
of avoiding human impact somewhere within that range            as in the continent-wide interspecific analysis. Within
than a species with a lesser range.                             these mountainous regions human populations are not
   In the raw data model, the threat of extinction increased    concentrated at lower elevations but inhabit relatively flat
with increasing minimum elevation. If human populations         land wherever it might be available. Species with small
are concentrated in low-lying coastal regions, species          elevational ranges may be adversely affected by human
with a minimum elevation of 0 m would be expected to            populations living on high altitude mountain plateaus,
be more affected by human-mediated disturbance, and             whereas species with greater ranges may be able to avoid
thus at a greater threat of extinction, than species with       human influence, at least at some altitudes within their
a higher minimum elevation. In fact the opposite result         overall range. Other studies, both in the Andes (Manne
emerged and we offer the following two non-exclusive            et al., 1999) and in south-east Asia (Brooks et al.,
explanations.                                                   1999), have commented on the vulnerability of montane
   First, following Brown (1984), species may be thought        avifaunas in populated regions.
of as being limited by combinations of physical and                The models for the lowland regions are different. In
biotic variables. Spatial variation in population density       Central South America, Minimum Elevation is significant,
reflects the probability density distribution of the required    possibly for the ecological reasons discussed above, while
variables and, if some sets of environmental variables are      an increased No. of Habitats is associated with reduced
independently distributed, then the density of a species        threat. In Amazonia South, where the topography is essen-
should decline towards the limits of its range. Whilst          tially flat and elevational variables are unlikely to be im-
this theory was used to explain an abundance–range              portant, greater Body Mass and fewer Habitats are the two
size relationship it is also applicable to elevational data     variables associated with increased threat, albeit weakly.
if elevation is taken as one of the required variables.            These disparate results serve to remind us that models
This might be a reasonable assumption if elevation              for each individual zoogeographical region may differ
controls a secondary variable, such as limiting the             from the model for the entire Neotropical region and
occurrence of specific habitat features. Species with a          highlight the significance of detecting trends at the
minimum elevation at or above 500 m experience an               appropriate ecological scale. The importance of detecting
ecological limit to their range and are thus limited in their   trends at the regional scale should be borne in mind by
response to habitat disturbance, since they are restricted      those planning regional conservation policy, despite the
by the probability density distributions of their required      temptation to draw on more readily available global or
variables. In addition such species are likely to occur at      continent-wide analyses.
lower relative abundances at the lower elevational limit
of their range, reducing their resilience to population
fluctuations. However, species naturally occurring down          Acknowledgements
to sea level may not be ecologically limited. Rather this
                                                                We would like to thank Andrew Balmford, Chris Elphick,
minimum elevation is set by the obvious topographical
                                                                                        u
                                                                Rhys Green, Oliver Kr¨ ger, Alison Stattersfield and two
constraint: occurrence below sea level is impossible. Thus,
                                                                referees for their thoughtful and patient comments and
populations of such species may be more resilient than
                                                                Bill Entwistle, Liz Playle and Buffy Robinson for all sorts
those of species with a higher minimum elevation.
                                                                of help.
   Second, it is broadly the case that the amount of land at
a given elevation decreases the higher the elevation. Thus
the area occupied by species with a minimum elevation
                                                                REFERENCES
at sea level may be greater than the area occupied by
those with higher minimum elevations. This could lead           Bennett, P. M. & Owens, I. P. F. (1997). Variation in extinction risk
to smaller population sizes and higher susceptibility to           among birds: chance or evolutionary predisposition? Proc. Roy. Soc.
extinction among the latter group.                                 Lond. Ser. B 264: 401–408.
                                                                Blackburn, T. M. & Gaston, K. J. (1998). Some methodological issues
   Although species associated with a distinctive Micro-           in macroecology. Am. Nat. 151: 68–83.
habitat were not more or less at risk of extinction in a                                              .
                                                                Brooks, T. M., Pimm, S. L., Kapos, V & Ravilious, C. (1999). Threat
simple regression (Table 2), they appeared to be slightly          from deforestation to montane and lowland birds and mammals in
less at risk of extinction in the raw species analysis             insular South-east Asia. J. Anim. Ecol. 68: 1061–1078.
168                                                               G. S. GAGE ET AL.

Brown, J. H. (1984). On the relationship between abundance and                    ecology and conservation: Stotz, D. F., Fitzpatrick, J. W., Parker,
   distribution of species. Am. Nat. 124: 255–279.                                T. A. III & Moskovits, D. K. (Eds). Chicago: University of Chicago
Cotgreave, P. & Harvey, P. H. (1992). Relationships between body size,            Press.
   abundance and phylogeny in bird communities. Funct. Ecol. 6: 248–           Peters, R. H. (1983). The ecological implications of body size.
   256.                                                                           Cambridge: Cambridge University Press.
Cracraft, J. (1982). Geographical differentiation, cladistics, and             Pimm, S. L. (1991). The balance of nature? Chicago: University of
   vicariance biogeography: reconstruction of the tempo and mode of               Chicago Press.
   evolution. Am. Zool. 22: 411–424.                                           Pimm, S. L., Jones, H. L. & Diamond, J. (1988). On the risk of extinction.
Diamond, J. M. (1984). “Normal” extinctions of isolated populations.              Am. Nat. 132: 757–785.
   In Extinctions: 191–246. Nitecki, M. H. (Ed.). Chicago: Chicago             Pimm, S. L., Russell, G. S., Gittleman, J. L. & Brooks, T. M. (1995).
   University Press.                                                              The future of biodiversity. Science 269: 347–350.
Dunning, J. B. (1992). CRC handbook of avian body masses. Boca                 Purvis, A. & Rambaut, A. (1995). Comparative analyses by independent
   Raton, FL: CRC Press.                                                          contrasts: an Apple Macintosh application for analysing comparative
Felsenstein, J. (1985). Phylogenies and the comparative method. Am.               data. Comp. Applic. Biosci. 11: 247–250.
   Nat. 125: 1–15.                                                             Purvis, A., Gittleman, J. L. & Luh, H.-K. (1994). Truth or consequences:
Garland, T., Harvey, P. H. & Ives, A. R. (1992). Procedures for the               effects of phylogenetic accuracy on two comparative methods. J.
   analysis of comparative data using phylogenetically independent                Theoret. Biol. 167: 293–300.
   contrasts. Syst. Biol. 41: 18–32.                                           Purvis, A., Agapow, P.-M., Gittleman, J. L. & Mace, G. M. (2000a).
Gaston, K. J. & Blackburn, T. M. (1995). Birds, body size and the                 Nonrandom extinction and the loss of evolutionary history. Science
   threat of extinction. Phil. Trans. Roy. Soc. Lond. Ser. B 347: 205–            288: 328–330.
   212.                                                                        Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. (2000b).
Goerck, J. M. (1997). Patterns of rarity in the birds of the Atlantic forest      Predicting extinction risk in declining species. Proc. Roy. Soc. Lond.
   of Brazil. Conserv. Biol. 11: 112–118.                                         Ser. B 267: 1947–1952.
Harvey, P. H. (1996). Phylogenies for ecologists. J. Anim. Ecol. 65:           Raup, D. M. (1978). Cohort analysis of generic survivorship.
   225–263.                                                                       Paleobiology 4: 1–15.
Hilton-Taylor, C. (Compiler) (2000). 2000 IUCN Red List of threatened          Raup, D. M. (1994). The role of evolution in extinction. Proc. Natl.
   species. Gland and Cambridge: IUCN.                                            Acad. Sci. USA 91: 6758–6763.
Kunin, W. E. & Gaston, K. J. (1993). The biology of rarity: patterns,          Ricklefs, R. E. & Starck, J. M. (1996). Applications of phylogenetically
   causes and consequences. Trends Ecol. Evol. 8: 298–301.                        independent contrasts: a mixed progress report. Oikos 77: 167–172.
Laurance, W. F. (1991). Ecological correlates of extinction proneness          Russell, G. J., Brooks, T. M., McKinney, M. M. & Anderson, C. G.
   in Australian tropical rain forest mammals. Conserv. Biol. 5: 79–89.           (1998). Present and future taxonomic selectivity in bird and mammal
Lawton, J. H. (1994). Population dynamics principles. Phil. Trans. Roy.           extinctions. Conserv. Biol. 12: 1365–1376.
   Soc. Lond. Ser. B 344: 61–68.                                               Sibley, C. G. & Ahlquist, J. E. (1990). Phylogeny and classification of
Lindstedt, S. L. & Boyce, M. S. (1985). Seasonality, fasting, endurance,          birds: a study in molecular evolution. Yale: Yale University Press.
   and body size in mammals. Am. Nat. 125: 873–878.                                 e
                                                                               Soul´ , M. E., Bolger, D. T., Alberts, A. C., Sauvajot, R., Wright, J.,
Mace, G. M. & Stuart, S. (1994). IUCN Red List categories. Species                Sorice, M. & Hill, S. (1988). Reconstructed dynamics of rapid
   21–22: 13–24.                                                                  extinctions of chaparral-requiring birds in urban habitat islands.
Manne, L. L., Brooks, T. M. & Pimm, S. L. (1999). Relative risk of                Conserv. Biol. 2: 75–92.
   extinction of passerine birds on continents and islands. Nature 399:        Symonds, M. R. E. (2002). The effects of topological inaccuracy
   258–260.                                                                       in evolutionary trees on the phylogenetic comparative method of
McKinney, M. L. (1997). Extinction vulnerability and selectivity:                 independent contrasts. Syst. Biol. 51: 541–553.
   combining ecological and paleontological views. Annu. Rev. Ecol.            Terborgh, J. & Winter, B. (1980). Some causes of extinction.
   Syst. 28: 495–516.                                                             In Conservation biology: an evolutionary–ecological perspective:
Owens, I. P. F. & Bennett, P. M. (2000). Ecological basis of extinction                           e
                                                                                  119–133. Soul´ , M. E. & Wilcox, B. A. (Eds). New York: Sinauer
   risk in birds: habitat loss versus human persecution and introduced            Associates.
   predators. Proc. Natl. Acad. Sci. USA 97: 12144–12148.                      Von Euler, F. (2001). Selective extinction and rapid loss of evolutionary
Pagel, M. D. (1992). A method for the analysis of comparative data.               history in the bird fauna. Proc. Roy. Soc. Lond. Ser. B 268: 127–130.
   J. Theoret. Biol. 156: 431–442.                                             Winker, K. (1998). Recent geographical trends in neotropical avian
Parker, T. A. III, Stotz, D. F. & Fitzpatrick, J. W. (1996). Ecological           research. Condor 100: 764–768.
   and distributional databases. Supplement to Neotropical birds:              Zar, J. H. (1999). Biostatistical Analysis. New Jersey: Prentice Hall.

				
DOCUMENT INFO
Shared By:
Stats:
views:17
posted:5/21/2012
language:English
pages:8
Description: Predicting the threat of extinction aids efficient distribution of conservation resources. This paper utilises a comparative macroecological approach to investigate the threat of extinction in Neotropical birds. Data on ecological variables for 1708 species are analysed using stepwise regression to produce minimum adequate models, first using raw species values and then using independent contrasts