A Journal of Conservation Biogeography
Diversity and Distributions, (Diversity Distrib.) (2012) 18, 92–96
BIODIVERSITY
LETTER
Island biogeography is not a singlevariable discipline: the small island
effect debate
Kostas A. Triantis1,2,3* and Spyros Sfenthourakis4
1
Azorean Biodiversity Group, Departamento
de Ciências Agrárias–CITAA, Universidade
dos Açores, Pico da Urze, 9700-042, Angra do
Heroı́smo, Terceira, Açores, Portugal,
2
Biodiversity Research Group, Oxford
University Centre for the Environment, South
Parks Road, Oxford OX1 3QY, UK,
3
Department of Ecology and Taxonomy,
Faculty of Biology, National and Kapodistrian
University, Athens GR-15784, Greece, 4Section
of Animal Biology, Department of Biology,
University of Patras, GR-26504 Patra, Greece
ABSTRACT
In some island systems, an ‘anomalous’ feature of species richness on smaller
islands, in comparison with larger ones, has been observed. This has been
described as the small island effect (SIE). The precise meaning of the term remains
unresolved, as does the explanation for the phenomenon and even whether it
exists. Dengler (2010; Diversity Distrib, 16, 256–266.) addresses a number of
conceptual and methodological issues concerning the nature and the detection of
the SIE but fails to settle conclusively most of the issues he raises. We contend that
his approach is theoretically flawed, especially in its treatment of habitat diversity.
We offer a few suggestions of what is needed to advance understanding of the SIE.
Keywords
Habitat diversity, islands, methods, species richness, species–area relationship,
theory, thresholds.
Diversity and Distributions
*Correspondence: Kostas Triantis, Azorean
Biodiversity Group, Departamento de Ciências
Agrárias– CITAA, Universidade dos Açores,
Pico da Urze, 9700-042 Angra do Heroı́smo,
Terceira, Açores, Portugal.
E-mails: konstantinos.triantis@ouce.ox.ac.uk;
island.biogeography@gmail.com
‘Our ultimate theory of species diversity may not mention area,
because area seldom exerts a direct effect on a species’ presence.
More often area allows a large enough sample of habitats, which in
turn control species occurrence’. MacArthur & Wilson (1967; p. 8)
The term small island effect has been applied to several subtly
different phenomena and contexts. Perhaps, the first
use of the term was by Rand & Rabor (1960) who studied
differences in body size and other attributes between bird
species on small islands and the mainland. They defined small
island effects as ‘the apparent ecological differences between
faunas and populations of small islands and those of near-by
larger land masses’. Similarly, Mees (1969) applied the term to
size differences among birds in the family Zosteropidae living
on large and small islands. A little later, Soulé (1980) used the
term to refer to the effects of a chronically small population on
genetic variation. Shortly, after the paper by Rand & Rabor
(1960), the observation was made that in some systems the
general positive relationship of species richness with area is
absent below a certain threshold of island size. This was also
described as the small island effect [e.g. Preston, 1962; Wiens,
1962; Niering, 1963; MacArthur & Wilson, 1967; Whitehead &
Jones, 1969; Heatwole & Levins, 1973; Heatwole, 1975; see also
Lack (1971) and Carlquist (1974) for similar, but not identical,
considerations of the phenomenon]. This usage has recently
become dominant within the literature, mainly as a result of an
influential paper by Lomolino & Weiser (2001).
Still, even with the incorporation of the small island effect
(hereafter SIE) within the current paradigm of biogeography, as
an ‘anomalous’ feature of species richness on smaller islands
compared with larger ones, it nonetheless remains unclear what
DOI:10.1111/j.1472-4642.2011.00812.x
http://wileyonlinelibrary.com/journal/ddi
ª 2011 Blackwell Publishing Ltd
In some island systems, an ‘anomalous’ feature of species
richness on smaller islands, in comparison with larger ones, has
been identified; a phenomenon that has been described as the
small island effect. Dengler (2010) addresses a number of
conceptual and methodological issues concerning the nature
and especially the methods used for the detection of the small
island effect, introducing a number of criteria for the study of
the phenomenon. He does so with direct reference to the data
set presented in a recent paper of ours (Sfenthourakis &
Triantis, 2009), criticizing both our approach and results. He
extends his criticism to the method introduced by Triantis
et al. (2006) for the study of the SIE. We contend that he fails
to settle conclusively most of the issues he raises and that his
overview of the theoretical background and methods for the
detection of small island effect is itself flawed.
THE SMALL ISLAND EFFECT
92
The small island effect
exactly the term stands for, what mechanisms underlie the SIE
and whether it even exists (see Lomolino, 2000; Lomolino &
Weiser, 2001; Gentile & Argano, 2005; Triantis et al., 2006;
Burns et al., 2009; Dengler, 2010). Here lies the first major
conceptual flaw of Dengler’s approach; as he himself recognizes,
there are various concepts attributed to the term SIE, leading to
different methodological approaches. For example, the method
applied by Lomolino & Weiser (2001) assumed that species
richness will vary independently of area below a threshold of
island size when a SIE is present, while the method applied by
Gentile & Argano (2005) is checking for the existence of a
threshold, below which the relationship between species
richness and area is different than the same relationship above
the threshold. On the other hand, Triantis et al. (2006)
suggested that we need to consider additional variables for
the study of the SIE, in addition to area, and introduced a new
method for the detection of the upper limit of the SIE.
However, Dengler decides to assign the approach of Lomolino
& Weiser (2001), i.e. species richness varying independently of
area below a certain threshold, as the ‘classic’ one (‘SIE sensu
stricto’) and the rest as ‘deviating SIE concepts’ (‘SIE sensu
lato’). This is simply wrong; there is currently no strong
theoretical or empirical evidence for accepting one particular
approach as ‘correct’ (not to mention ‘classic’). Although a
traditional link is certainly identified between Niering’s (1963;
fig. 7) first graphical representation of the SIE and Lomolino &
Weiser’s method for the detection of the phenomenon, this
does not definitively identify their method as being the most
appropriate one. Moreover, the use of terms such as ‘SIE sensu
stricto’ and ‘SIE sensu lato’ could be seen as further complicating
the overall discussion; we can understand the use of these terms
in taxonomy and phylogeny, but we see no necessity for using
them for theoretical constructs such as the SIE.
The discussion over the nature of the SIE, even its very
existence, is still inconclusive, and it is obvious that further
theoretical and empirical studies are needed. Elevating the first
to come and/or most popular approach to the level of the
norm, without a robust theoretical documentation and strong
factual evidence, is certainly premature sensu stricto.
METHODOLOGICAL ISSUES
Dengler presents nine different methods applied so far for the
detection of the SIE. This comparative presentation is a
valuable addition to the discussion. Furthermore, we agree
with his approach to apply nonlinear regressions for the
various regressions involved in the methods, an innovation
that may overcome some of the major drawbacks of traditional
data transformation. Additionally, we agree that the inclusion
of islands with no species in the study of SIE could provide
new and interesting insights (e.g. Morrison, 2011).
Nonetheless, several statistical and conceptual issues remain
to be clarified and resolved:
1. In the title of his paper, Dengler claims robustness for the
methodological steps he introduces for the study of the SIE,
but as his method has only been tested on a single data set it
Diversity and Distributions, 18, 92–96, ª 2011 Blackwell Publishing Ltd
appears too early to judge how robust and appropriate the
method may be. It would be helpful if Dengler’s method had
been applied to more data sets and/or simulated data sets, for
which he can control for a SIE or not. Note also that the
regressions he has used should be further evaluated by
statistical examination for normality and homoscedasticity.
Dengler, in his review of our manuscript, elucidated that
‘robustness’ is referring to the internal logic of the method.
Then, it is this ‘internal logic’ that we consider to be flawed.
2. It is not clear that all the studies have been correctly assigned
in terms of the methods used for the detection of the SIE. For
example, the study by Panitsa et al. (2006) was assigned to the
‘Visual inspection’ category, when in fact these authors applied
the Lomolino & Weiser (2001) breakpoint regression method
(Panitsa et al., 2006, p. 1225, last paragraph). Similarly, for the
study of Morrison (1997), Dengler reports that the ‘Criterion
for SIE’ was the ‘higher R2 of the SIE model’; subsequently, the
‘Methodological shortcoming was: No penalization for additional parameter(s’)’. Although Morrison did report R2 values,
he used a partial F-test to determine whether the additional
parameter in the curvilinear model was statistically significant,
as a test for an SIE (p. 453).
3. Triantis et al. (2006; hereafter KAT) suggested that we need
to consider additional variables for the study of the SIE, apart
from area, and introduced a new method for the detection of
the upper limit of the SIE. The method is based on an a priori
theoretical model according to which island area affects habitat
diversity directly and area and habitat diversity directly affect
species number per island. Following this, each data set
(among the 16 used) was tested for the existence of a certain
island size under which the direct effects of area were
eliminated. This detection was carried out through the
sequential exclusion of islands, from largest to smallest, and
the simultaneous estimation of the standardized partial
regression coefficient of area; when this coefficient was found
to be equal or smaller than zero, the respective area was
assigned as the upper limit of SIE. Note that an SIE was
detected in six of the 16 cases considered (37.5%).
Dengler claims that the KAT method suffers from five major
shortcomings: (1) It incorporates a de facto comparison of log
S = z1 log A + z2 log H vs. log S = (log A < T) z3 log H + (log
A > T) (z1 log A + z2 log H), and thus, we should penalize the
two additional parameters. This is a misunderstanding; no
comparison is made with any simpler model incorporating
habitat diversity or with any other method, owing to the
distinctive nature of the method, i.e. inclusion of habitat
diversity measures. The upper limit of the SIE is estimated based
on the value of the standardized partial regression coefficient of
area and not on any measure of fit. Thus, no penalization of
parameters is needed in the particular context of the method.
(2) High correlation between H and A causes imprecise
parameter estimates. We concur with the suggestion that high
collinearity between area and habitat diversity can have possible
confounding effects and that this is an issue to be addressed
when the method is applied, but the need for this is discussed in
KAT. The use of path analysis can partially amend this problem
93
K. A. Triantis and S. Sfenthourakis
(e.g. Grace et al., 2010 for a discussion on the structural
equation modelling). (3) ‘Forcing’ the regression through the
origin. The equation used for the estimation of path coefficients
includes a constant, so the regression is not forced through the
origin of the axes. Unfortunately, this point was omitted in error
by Triantis et al. (2006), and we therefore take the opportunity
of correcting that omission here. (4) Deviating concept of SIE
(‘cryptic SIE’). As noted earlier, we reject the notion that there is
a single correct formulation of the SIE; labelling approaches
different from Lomolino & Weiser (2001) as ‘deviating’ is
unhelpful. The SIE agenda should not be constrained by
imposing a particular concept and dismissing all others as
deviations. (5) Problematic habitat definition (see below).
Dengler compared the upper limit estimations for the SIE
we presented in Sfenthourakis & Triantis (2009) following
KAT with what he considers as implementations of the SIE
sensu lato, i.e. all methods apart from the one suggested by
Lomolino & Weiser (2001). He argues that the lack of similar
values between the two sets of values indicates a lack of support
for the method we have applied. Nevertheless, as he also
recognizes, the estimated values from the KAT method derive
from a totally independent approach. We have assessed the
possible mechanisms establishing the SIE through studying the
composition of island faunas in the Aegean Sea in terms of
specialist and generalist species representation, below and
above the SIE threshold. In this case, the SIE threshold was
estimated as the island size where the cumulative ratio of
specialists to generalists diverged from that of the overall data
set, and it was almost identical with the threshold estimated by
the KAT method. We concluded that the relative representation of specialist and generalist species on islands of different
sizes plays an important role in shaping SIE-related patterns.
According to Dengler, ‘this coincidence does not hold for the
thresholds of the SIEs sensu lato determined in this article,
which removes any support for the priority setting proposed
by Sfenthourakis & Triantis (2009)’. Again, we suggest that it is
unhelpful to attempt to elevate one usage to a position of
primacy as though we were dealing with taxonomic naming
conventions for species and genera.
HABITAT DIVERSITY IS IMPORTANT
As indicated in the quotation at the beginning of this paper,
MacArthur & Wilson (1967), among others, highlighted the
necessity for incorporating environmental heterogeneity into
theories and models for explaining species diversity patterns
(e.g. Williams, 1964; Kohn & Walsh, 1994; Ricklefs & Lovette,
1999; Triantis et al., 2003; Hortal et al., 2009). This is
reinforced at small spatial scales, where environmental heterogeneity and area can become increasingly decoupled, and area
alone cannot efficiently express the total effects of habitat
diversity and island size on species richness (e.g. Triantis et al.,
2005). One of the first to recognize this was Fosberg (1948),
who studied the flora of atolls in the Pacific Ocean and
concluded that ‘The flora is larger (species more numerous) in
more or less direct proportion to the amount of rainfall and the
94
area of the islet’. In this context, Triantis et al. (2006)
highlighted the need to consider habitat diversity explicitly in
the study of the SIE. This is a crucial conceptual difference with
the other methods that consider only area. Moreover, even if
the method proves to be wrong, the challenge for incorporating measures of environmental heterogeneity remains.
Dengler follows a rather ambiguous line of reasoning on this
issue. Towards the end of his paper, he admits that habitat
diversity should not be ignored, suggesting that it could be
incorporated in the study of the SIE as an explanatory variable
for the residuals of the best species–area model. He suggests
that plotting the residuals of the habitat–area relationship
against the residuals of the species–area relationship could
provide insights for the relative importance of habitat diversity.
This could certainly act as supplemental to other available
approaches. However, there are two points to consider. First, it
has to be determined which species–area model to apply,
because there are at least 20 different functions proposed so far,
without strong evidence supporting the a priori selection of one
or even a few of them; hence, a multimodel approach could be
helpful (e.g. Guilhaumon et al., 2008; Tjørve, 2009). Second,
Dengler’s approach considers area to be more meaningful and/
or effective in describing species richness than habitat diversity.
Although area has proved to be the best single explanatory
variable for species richness, when habitat diversity is also
considered it is often found to be a better predictor of species
richness than area (e.g. Lack, 1973; Kohn & Walsh, 1994;
Ricklefs & Lovette, 1999; Sfenthourakis & Triantis, 2009). Thus,
an equally plausible approach in such cases might be to use area
to explain the residuals of the species–habitat relationship.
The core of our criticism of Dengler’s approach relates to the
way he treats habitat diversity as used in the KAT method and
in ecological studies overall. He comments ‘These habitat
definitions [the ones used in Sfenthourakis & Triantis, 2009]
are idiosyncratic, generally not transferable among island
datasets and constructed from an anthropogenic view [our
emphasis]. Moreover, this approach does not account for the
proportional areas of the habitats on the islands. Nor does it
reflect the varying degrees of similarity between the habitat types
[…]. Thus, it is questionable as to the meaning of an SIE and of
its upper limit L if habitats are defined and used in such a way.’
In another part of his paper, he adds: ‘apart from solving the
other methodological shortcomings of the method applied by
Triantis et al. (2006) (see Table 1), one should apply a uniform
habitat definition for all island data sets to be compared. To find
such a general, globally applicable and, at the same time,
ecologically meaningful measure of habitat diversity will certainly
not be an easy task. And finally ‘To disentangle these two
processes by including a measure of habitat diversity into the SAR
models […] seems hardly plausible since there are infinite
possibilities of how to quantify habitat diversity – and each will
result in different outcomes’.
Hence, although Dengler suggested possible ways of incorporating habitat diversity in the SIE search programme, he
discourages any effort towards quantifying it. It might be ideal,
but probably impossible, or even misguided according to some
Diversity and Distributions, 18, 92–96, ª 2011 Blackwell Publishing Ltd
The small island effect
conceptual approaches to the term ‘habitat’, if in the future we
could define an International Prototype of Habitat Unit similar to
the International Prototype of Kilogram (see http://www.bipm.
org/en/scientific/mass/prototype). Meanwhile, we cannot just
ignore environmental heterogeneity and its possible effects on
species diversity. Although well-defined obvious and discrete
variables or quantities, such as area, elevation, etc., will remain
easy to understand and explain, as in other fields of science, we
are usually left with rather abstract, open and generalized
concepts; we just hope to increase the degree of abstraction
producing concepts with higher degrees of generality (see Pickett
et al., 2007). Hence, although we are aware of the many pitfalls in
habitat diversity/heterogeneity conceptualization and quantification, this is not a reason to abandon it. Similar to SIE, niche
theory has many pitfalls and problems of definition (e.g. Colwell
& Rangel, 2009); should we abandon niche theory too?
A FEW POINTERS FOR FUTURE RESEARCH
The current uncertainty over the nature, the driving mechanisms and the methods for the study of the SIE are
understandable, given the relatively short time since the
patterns’ recognition and the limited number of studies
addressing it. Even for better established and extensively
explored patterns of biogeography, such as nestedness, no
consensus has yet been reached regarding the mechanisms
generating the pattern and the appropriate metric to quantify it
(e.g. Ulrich et al., 2009). Hence, we stress the lack of strong
consensus along with the growing number of applied and
theoretical studies of SIE and the need for a critical review of
the state of art and for perspectives guiding future research.
Such a review should incorporate at least three major tasks
as follows: (1) An evaluation of the various views on what
constitutes a SIE in respect to their ability to effectively
describe actual patterns, hence increasing the utility of the
term; (2) A classification of the available explanations/mechanisms and the hypotheses generated by these explanations.
According to Sfenthourakis & Triantis (2009), there are two
main categories of explanation as follows: (i) demographic
stochasticity (i.e. extinction rates independent of island area)
and (ii) idiosyncratic habitat diversity at small spatial scales
[see discussion about the Small island habitat effect in
Whittaker & Fernández-Palacios (2007); see also Heatwole
et al. (1981)]. To date, explanations have mainly been
promulgated as hypotheses related to species richness, but a
broader theoretical framework should also introduce hypotheses on community composition, species abundances, species
co-occurrence and generalist-specialist species representation
(e.g. Burns et al., 2009; Sfenthourakis & Triantis, 2009;
Morrison, 2011); and (3) An evaluation of the available
methods applied to date for the detection of the SIE, possibly
leading to the introduction of new methods. Dengler undertook the latter task but failed to place his evaluation within the
general framework set by the two previous tasks.
No single cause is sufficient to explain the vast majority of
ecological phenomena, and in many cases, a single variable is
Diversity and Distributions, 18, 92–96, ª 2011 Blackwell Publishing Ltd
not even enough to effectively describe them. Habitat
diversity has a well-established effect on species diversity,
at least at certain spatial scales. Therefore, before we restrict
our approaches to a single variable (area), we should at least
try to incorporate other variables known to have a significant influence. The ‘debate’ over the existence and the
nature of the small island effect is certainly not resolved yet.
Although the pattern is gaining space in the ecological
literature, further theoretical and empirical studies are
needed to fully resolve these issues. For example, it is
possible that the SIE is manifested through some critical
value range, rather than a strict threshold point (e.g. Ficetola
& Denoël, 2009).
ACKNOWLEDGEMENTS
We are grateful to Richard Ladle, Mike Weiser, Kevin Burns,
Joaquin Hortal, Sam Scheiner, François Guilhaumon, Aris
Parmakelis, Even Tjørve and especially Rob Whittaker for
comments and suggestions on previous versions of the
manuscript. We thank L. Morrison and an anonymous
reviewer for comments on a draft of this manuscript. We
have to note that we did not have the opportunity to express
our opinion on Dengler’s paper before or during the evaluation process; however, we thank J. Dengler for comments on a
previous version of the manuscript. KAT is supported by a
FCT Fellowship (SFRH/BPD/44306/2008).
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BIOSKETCHES
Kostas Triantis was recently appointed Assistant Professor in
the Biology Department of the University of Athens. He is
mainly interested in island biogeography of oceanic and
mainland islands and macroecology. His other research
interests include the following: scale in ecological and biogeographical analysis, species–area relationship, environmental
heterogeneity and conservation biogeography.
Spyros Sfenthourakis is an Assistant Professor of Ecology
and Biogeography in the University of Patras. He is interested
in processes shaping species diversity, mainly in insular
communities. He has published in the fields of island
biogeography, community assembly, phylogeography and
systematics of terrestrial isopods, as well as biodiversity
patterns in Greece. He is also interested in the reproductive
and migratory behaviour of bird species.
Editor: Marcel Rejmanek
Diversity and Distributions, 18, 92–96, ª 2011 Blackwell Publishing Ltd