Final Thesis
Great crested newt (Triturus cristatus) as a diversity
indicator species and evaluation of sampling
methods
Martin Planthaber
LiTH-IFM-Ex--/--SE
Table of contents
Abstract ......................................................................................................... 1
2 Introduction ................................................................................................ 1
3 Material and methods ................................................................................. 3
3.1 Indicator species study ......................................................................... 3
3.1.1 Field work...................................................................................... 3
3.1.2 Analyses ........................................................................................ 4
3.2 Sampling variation study ..................................................................... 4
3.2.1 Temporal variation ........................................................................ 4
3.2.2 Interobserver variation .................................................................. 5
4 Results ........................................................................................................ 5
4.1 Indicator species study ......................................................................... 5
4.2 Sampling variation study ..................................................................... 7
4.2.1 Temporal variation ........................................................................ 7
4.2.2 Interobserver variation .................................................................. 9
5 Discussion ................................................................................................ 11
5.1 Indicator species study ....................................................................... 11
5.2 Sampling variation study ................................................................... 12
5.2.1 Temporal variation ...................................................................... 12
5.2.2 Interobserver variation ................................................................ 14
5.3 Conclusions ........................................................................................ 15
Acknowledgements ..................................................................................... 15
References ................................................................................................... 15
Abstract
Indicator species are sometimes used to facilitate biodiversity monitoring,
but the selection of indicator species requires careful evaluation of
comprehensive data in order for conservation efforts to be successful. The
Great crested newt (Triturus cristatus) has previously been proposed as a
possible indicator species for aquatic macrophyte diversity. Six pairs of
ponds in southeastern Sweden were examined for macroinvertebrate
diversity. Each pair consisted of one pond with and one without T.
cristatus, but otherwise as similar as possible. There were no significant
differences between ponds with and without T. cristatus. However,
differences within the pairs differed significantly between those pairs
located in open areas, such as pastures or arable land, compared with pairs
in forest environments. When comparing the two habitat types the open
areas showed a pattern of high diversity in ponds with T. cristatus and low
in ponds without whereas the forest habitat showed no clear pattern.
Sampling error in suggested methods for T. cristatus monitoring was also
evaluated, as were temporal and interobserver variation. Significant
differences among observers were observed using visual sampling but not
when dipnetting for T. cristatus larvae. The visual sampling method
appeared to be biased in favor of males compared with other methods.
Besides implications for methods, the information gathered could be used
in poweranalyses to estimate sample size needed for effective monitoring
of populations.
Keywords: amphibians, Caudata, interobserver variation,
macroinvertebrate, temporal variation, visual observation
2 Introduction
Amphibians are currently suffering declines at unprecedented rates
worldwide and of the species described, 31% are listed as threatened
(IUCN criteria CR, EN or VU). The number of threatened amphibian
species has, during the past ten years (1996-2006), increased from 124 to
1811 and the number of critically endangered species has increased from
18 to 442. In comparison, the number of threatened species in any of the
classes mammals, birds and reptiles have not increased even one tenth of
this percentage. Among the amphibian orders, Caudata (newts and
salamanders) has the highest percentage with 46.5% of all known species
being threatened. Furthermore amphibian values are likely to be
underestimated since approximately 24% of amphibian species described
are classified as Data Deficient. The percentage of Data Deficient
1
mammals, birds and reptiles are markedly lower. (IUCN Red List 2006).
Analyses of earlier records show that the declining trends in amphibian
populations started about five decades ago (Houlahan et al. 2000) and they
have worsened during the last 25 years (Beebee & Griffiths 2005). The
conclusion from these numbers of decreases and lack of data are that
amphibians are in urgent need of monitoring and conservation efforts. This
requires continued development of reliable methods for monitoring
amphibian populations in order to determine conservation priorities as well
as evaluating effects of conservation efforts (Buckley & Beebee 2004).
To facilitate monitoring indicator species/taxon are sometimes used
e.g. as a measure of diversity among other taxa. But the selection of one or
a few taxa as indicators for several others is far from unproblematic and
requires a careful evaluation of the indicators based on comprehensive data.
If not, there is a risk that conservation efforts are futile and resources
wasted (Lawler et al. 2003, Lindenmayer&Fischer 2003, Grenyer et al.
2006). The steep decline in amphibian populations could indicate that they
suffer a large impact from the environmental threats that has developed
during the recent decades and could be among the most sensitive taxa to the
effects of human activities. If this is the case they could possibly be used as
indicators of unaltered ecosystems.
The great crested newt (Triturus cristatus Laurenti 1768), a caudate
amphibian, has previously been proposed as an indicator species of aquatic
macrophyte diversity (Gustafsson et al. 2006). Triturus cristatus adults
migrate from their terrestrial habitat to breed in ponds during spring and
early summer and the larvae develop in the aquatic habitat during the
summer and thereafter become terrestrial at late summer or fall (Malmgren
2002). Since T. cristatus seems to display metapopulation dynamics, is
sensitive to habitat fragmentation and is depending on connectivity
between aquatic and terrestrial habitats (Joly et al. 2001) it could perhaps
be a useful indicator for undisturbed pond landscapes and pond
biodiversity.
In order to get reliable data for analysis when performing monitoring,
it is crucial that the methods used are accurate (i.e. give a reasonable
representation of the underlying “true values”, despite variation caused by
varying conditions during sampling) and reliable (repeatable). Hence, it is
important to know the reasons and magnitude of any distortions.
Monitoring amphibian populations is complicated by low and variable
probabilities of detection (Schmidt 2005). Except for small, well defined
populations, the most practically feasible way of performing large scale
monitoring of amphibians has been suggested to be by recording
presence/absence instead of trying to measure changes in abundance in
2
individual populations (Beebee & Griffiths 2005, Buckley & Beebee 2004).
This is mainly due to the, compared with presence/absence sampling, more
labour intensive methods of newt population size estimation for example
by mark-recapture (Bailey et al. 2004). However, the reliability of such
presence/absence data remains unknown.
One aim of this project was to investigate whether T. cristatus could
be used as an indicator species for macroinvertebrate diversity, and another
was to evaluate sampling variation when using the standardized methods
proposed by the Swedish Environmental Protection Agency (Malmgren et
al. 2005). Temporal and interobserver variations in T. cristatus abundance
sampling will be described aiming for ways to improve the data quality of
monitoring schemes and perform poweranalyses.
3 Material and methods
3.1 Indicator species study
3.1.1 Field work
In the indicator species study, six pairs of ponds that were pairwise similar
with respect to size, depth, surrounding area and over-grown to the same
extent were compared. Each pair consisted of one occupied pond where T.
cristatus larvae were found and one empty pond with earlier record
(Karlsson 2006) of adult absence and where no newt larvae were found.
They were all situated in a landscape with regular occurences of newts and
newt reproduction (Karlsson 2006, personal observations) so that absence
in a sampled pond should not be primarily caused by isolation effects.
Three of the pairs were located in open areas such as pastures or arable
land (hereafter referred to as open areas) and three were located in forest
dominated areas. Selection of which ponds to examine was based on
records and maps of earlier observations of absence/presence and
reproduction provided by the County administrative board in Östergötland
(Karlsson 2006). In those records, some basic properties of the ponds were
described and those combined with field observations were used to find
ponds similar enough to form pairs. The ponds should not contain fish or
crayfish. With the above mentioned criteria applied to records of 349
ponds in Östergötland surveyed for T. cristatus, six acceptable pairs were
found. The chosen ponds were all within a range of approximately 45 km.
The size range of the ponds was approximately from a couple of meters to
slightly over twenty meters across.
Dipnetting of newt larvae was performed during August according to
the Z-sweep method described by Malmgren et al. (2005). The dipnetting
was performed by sweeping the net through the littoral vegetation at 2-5
3
dm above the bottom in a Z-like movement during approximately 3
seconds. This was repeated every 5 meters around the entire littoral zone or
until larvae were found, and thereby verifying reproduction.
Invertebrate sampling was performed with a cylinder sampling of five
randomly selected places in each pond at 2-4 dm depth. The samples were
collected with a plastic cylinder of 33 cm in diameter that was pushed into
the bottom sediment and thereby isolating a small body of water. This
water and its contents was scooped out and filtered through a 0.5 mm mesh
and the filtrate was used as one sample. Areas for sampling that were
inaccessible or where the method did not work properly, e.g. due to roots or
other hard structures on the bottom, were excluded during the selection.
3.1.2 Analyses
Log-transformed abundance values were used in order to reduce the
influence of the most abundant species. The macroinvertebrate assemblages
in the ponds were described by a Principal Component Analysis (PCA). A
comparison of the macroinvertebrates in ponds with and without T.
cristatus larvae was done using partial Redundancy Analysis (pRDA),
using the pairs as covariables (entered as a number of categorical dummy
variables). In both cases, the software CANOCO 4.5 software (ter Braak
and Smilauer 2002) was used. Differences within pairs were also tested in a
paired t-test, with Simpsons diversity index and taxon count as response
variables.
3.2 Sampling variation study
3.2.1 Temporal variation
In the study where the visual method described by Malmgren et al. (2005)
was evaluated, two ponds were selected for an intensive study and, as
described in the next section, for an interobserver variation study. One was
a large pond with high abundance of T. cristatus and one was a smaller
pond with lower abundance.
In the intensive study, ponds were sampled using visual observation
approximately two times per week and on each occasion usually twice.
Sampling was performed during approximately two hours after dark.
Visual observation was performed during May and the first half of June
according to the standardized methods described by Malmgren et al.
(2005). The observation was performed at night using a relatively powerful
(10-20 W) headlight. The observer walked slowly around the water´s edge
while looking for newts and stopped every 5 m to more carefully look
during approximately 30 seconds. The whole pond was searched and four
4
classes of individuals were noted: males, females, juveniles and adults that
could not be sexually determined.
Pondwise standardized values of abundance were plotted as function
of date and minutes after sunset when sampling was performed and lowesslines (stiffness 0.6) were used to illustrate trends in data. The sex ratio
during the season was plotted with 95% binomial confidence intervals
(Sauro & Lewis 2005). Analysis of sample size needed at different standard
deviations to detect differences among T. cristatus populations was
calculated using Studysize 1.0.
3.2.2 Interobserver variation
In the interobserver variation study, five observers sampled the ponds
during two nights each in May using the visual observation method.
Furthermore, the same observers also dipnetted for larvae twice during
August. Visual observation and dipnetting of newt larvae was performed
according to the standardized methods described by Malmgren et al. (2005)
and briefly in the previous sections.
Interobserver variation was analysed using variance component
analysis in the STATISTICA software version 7.0 (StatSoft, Inc. 2004).
4 Results
4.1 Indicator species study
The pRDA showed no significant differences (P=0.3203) in
macroinvertebrate assemblages between ponds containing T. cristatus
larvae and those that did not. PCA was used to further illustrate differences
in species composition between ponds in the selected pairs, but no clear
patterns within the pairs was found (Figure 1). A paired t-test performed on
taxon count showed no significant differences within the pairs (P= 0.298,
t(5)=1.1622), and the same test performed using Simpsons diversity index
(1-D) values did not show differences either (P=0.881, t(5)=0.1569).
However, a comparison of pairs located in different areas did show
some differences. When comparing Simpsons diversity index (1-D) the
pairs in forest environments differed from the ponds located in open
environments, such as pastures or arable land, which all had at least 59%
higher diversity index in ponds where newt larvae were present than in the
ones where they were absent (Figure 2). No significant difference in
diversity index within pond pairs in forest versus open area was shown
(P=0.0824, t(2.1)=3.1493) using a t-test, while differences in taxon count
within pairs did show significant differences (P=0.035, t(4)=-3.1264). The
ponds with presence in open landscapes had at least 78% higher taxon
5
0.8
count and 59% higher diversity index than the absence ponds. There was
no apparent pattern in variation in the pairs in forest environment.
Hesperoc
Cloeon i
Somatoch
Coenagri
Haliplus
Rhantus
Herpobde
Gerridae
Helobdel
Leucorrh
Suphrody
Hygrotus
Anacaena
Ilybius
-0.8
Asellus
GlossiphPhysa fo
0.8
1.2
-0.8
5
1
6
1
4
Anisoptera
Somatochlora
metallica
Leucorrhinia dubia
Zygoptera
Coenagrion sp
Heteroptera
Hesperocorixa
sahlbergi
Gerridae (nymphs)
Coleoptera
Anacaena sp
Haliplus Haliplus sp
Hygrotus sp
Ilybius sp
Rhantus sp
Suphrodytes sp
Ephemeroptera
Cloeon inscriptum
Isopoda
Asellus aquaticus
Gastropoda
Physa fontinalis
Hirudinea
Herpobdella sp
Helobdella stagnalis
Glossiphonia
complanata
4
6
2
2
3
5
-1.0
3
-1.0
1.0
Figure 1. PCA of species composition in pairs of ponds with (filled circles) and
without (empty circles) T. cristatus larvae. Ponds 1, 2 and 3 were located in
open areas while 4, 5, and 6 were located in forest. There were 55 taxa in the
analysis but only the 17 contributing most to variation are displayed (complete
names in table). Eigenvalues of PC1 and PC2 were 0.275 and 0.213
respectively.
6
30
Presence
Taxon count
a)
Absence
20
10
0
1
2
3
4
5
6
1
2
3
4
5
6
Simpson index (1-D)
1
b)
0.8
0.6
0.4
0.2
0
Pond pair
Figure 2. Taxon count (2a) and Simpsons diversity index (1-D) (2b) for the six
pairs of ponds. Pairs 1, 2 and 3 were located in open areas such as pastures or
arable land while 4, 5 and 6 were located in forest environments
4.2 Sampling variation study
4.2.1 Temporal variation
Trends in seasonal variation appeared to consist of a small increase during
the first part of the sampling period and a more pronounced, but still a
slow, decrease towards the end (Figure 3a). Time of sampling during the
night also showed a trend where peak values appeared to be reached around
90 min after sunset (Figure 3b).
The individuals of T. cristatus recorded changed from being clearly
dominated by males during the first half to more equal, but still maledominated counts in the second half of the sampling period (Figure 4). The
equalization is caused by a steady decrease in male counts and a smaller
increase in female counts in the second half.
7
a)
Date
b)
Minutes after sunset
Figure 3: a) Seasonal variation in T. cristatus abundance. Standardized values
from both ponds. b) Temporal variation of T. cristatus abundance. Standardized
values from both ponds.
8
Proportion of males
1
0.5
Sampling 1
Sampling 2
0
01 May 06 May 11 May 16 May 21 May 26 May 31 May 05 June 10 June 15 June
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
Figure 4. Seasonal variation in sex ratio (proportion of males) with 95% binomial
confidence intervals.
4.2.2 Interobserver variation
The variance component analysis using standardized values from each
pond showed a significant (P=0.0443)(F=6.88) difference between
observers with 51% of the total variance being explained by variation
between observers. The same analysis performed on values from larval
sampling by dipnetting (only the large pond) showed no significant
observer effect (Figure 5) (P=0.385)(F=1.29).
In the smaller pond with lower abundance, no T. cristatus were found
in 60% of the samplings (Figure 5). This would result in a 36% probability
of an inventory resulting in a “false zero”, when using the presently
suggested methodology that consists of two samplings. If the criteria to
consider T. cristatus as absent would be based on three instead of two
negative samplings, the probability of an inventory resulting in a “false
zero” would be 22%. In comparison, an observer with sampling experience
found no T. cristatus in 28% of 18 samplings. This would result in an 8%
probability of an inventory resulting in a “false zero” (2% if based on three
negative samplings).
In the large pond, T. cristatus was found in every sampling. The
number of individuals found varied between 10 and 39 (average 23.8, SD
10.75).
9
a)
b)
c)
Observer
Figure 5. Interobserver variation in T. cristatus adults and juveniles (a and b)
and in larvae (c). Observer 5 had previous sampling experience while observers
1-4 only had basic species information and method instructions. First sampling
empty bars, second sampling filled bars.
10
5 Discussion
5.1 Indicator species study
The results suggests that presence of T. cristatus larvae can not be used as
an indicator of macroinvertebrate diversity under the circumstances in this
study. However, the average taxon count for presence ponds was 46%
higher than in the absence ponds. The surrounding area of the selected pairs
could probably have contributed to a large part of variation in the data,
since the results showed signifiant differences in taxon count within pairs
from either open or forest environment. The eventual indicator species
properties of T. cristatus could be restricted to only some of its habitats.
Studies on the use of indicator taxa have shown that they can be a
useful tool in diversity assessments, but that indicator properties can be
quite specific for certain taxa, spatial scales and geographic regions.
Correlations between taxa may vary considerably between different
geographic regions so that patterns of correlations between taxa in one area
do not necessarily show the same pattern even in another similar area (Hess
et al. 2006). Highest probability for a taxon to function as an indicator
appears to be among taxonomically similar species (e.g. rare plants
indicating plant richness) or taxa with functional dependence (butterflies as
indicator for flowering plant richness: Flather et al. 1997). However,
Lawler et al. (2003) found that threatened species from six different taxa
performed better than the individual taxonomic groups (freshwater fish,
birds, mammals, freshwater mussels, reptiles, amphibians) as indicators of
overall taxonomic diversity. Regarding the spatial scale Grenyer et al.
(2006) concludes that “even among terrestrial vertebrates, the extent to
which rare and threatened species from one group can act as a surrogate for
corresponding species in other groups is severely limited, especially at the
finer scales most relevant to conservation”.
At a larger spatial scale, T. cristatus could possibly have indicator
species properties other than eventual cross taxon biodiversity, for example
as an indicator of an unfragmented landscape due to the species low
dispersal abilities and dependence on metapopulation dynamics (Malmgren
2002, Joly et al. 2001). Hager (1998) proposes that fragmentation-sensitive
species of amphibians and reptiles could be used as this type of indicator
due to nested patterns of amphibian occurences found on islands. Hence,
stable and functioning metapopulations of T. cristatus could possibly have
value as an indicator of an unfragmented landscape and connectivity
between ponds suitable for amphibians. Lambeck (1997) suggests, in
accordance with this, that when selecting areas for reserves a suite of
species should be selected which represents the most sensitive to one or
11
more of the different threats that the reserve aims to protect from. The most
sensitive species will thereby set the acceptable level of a certain stressor.
The chosen species could e.g. be area-, connectivity- or resource
demanding. Pearman (1997) and Krishnamurthy (2003) found that some
amphibian taxa were clearly more affected by habitat fragmentation and
degradation caused by human activities than other. Triturus cristatus
eventual usefulness as a connectivity indicator requires further
investigation.
Criticism against the use of indicator species rightfully claims that not
all threats and their effects will be detected when using one or few species
as indicators, but an advantage in focusing on the presumably most
sensitive species to a known threat is that the eventual negative impacts
will likely be detected earlier than when focusing on entire communities
(Faria et al. 2006). Furthermore, the single/few species monitoring is likely
less work-demanding than ecosystem/community monitoring and therefore
requires less resources enabling monitoring that is spatially or temporally
more comprehensive.
Further testing with a large number of pairs from increased sample
areas including various kinds of habitats could perhaps shed more light on
the possible indicator species properties for T. cristatus in different
habitats, spatial scales and taxa.
5.2 Sampling variation study
5.2.1 Temporal variation
Triturus cristatus peaked at the early parts of May coinciding with the
reproductive activity (Malmgren 2002). After the winter T. cristatus is
depending on a temperature rise to 0-5°C and rainfall for initiation of
migration to reproduction ponds (Malmgren et al. 2005).
The period of highest T. cristatus activity (above average) appears to
be reached around 90 minutes after sunset (Figure 4) and thereafter stay at
rather high levels during the sampled period, indicating optimal time of
sampling to be from around 90 minutes to at least 210 minutes after sunset
The male-dominated sex ratio of observed T. cristatus could be caused
by different probabilities of detection of males and females since previous
studies, using other than visual methods, have shown that the only skew in
sex ratio for T. cristatus generally is towards a higher proportion of females
in the later parts of the breeding season (Arntzen 2002). A large part of the
observed difference could possibly be caused by the easily spotted male
bright white stripe along the tail. This stripe allows easy spotting as well as
identification of males but is not seen from all angles causing the ratio of
sexually unidentified newts to likely be skewed toward higher proportion
12
of females. This could in turn cause a bias in numbers of observed newts of
each sex since the sexually unidentified individuals were excluded from the
sex-ratio calculations. A personal observation was that T. cristatus
(primarily males) often appeared to aggregate in open areas while
observations from more densely vegetated areas gave more equal counts of
males and females. These aggregations are likely a display of lekking
behaviour (Hedlund 1989) and if males gather in more open areas it could
increase their probability of detection and cause a further skew in the sex
ratio.
The visual observation method (Malmgren et al. 2005) as a measure of
population size is likely to be a relatively blunt instrument in estimating
population size compared to more resource demanding techniques such as
mark/recapture (Bailey et al. 2004, Schmidt 2004, Flint & Harris 2005).
However, visual encounter methods have, for other caudates, shown to give
a valid index of relative population sizes (Flint & Harris 2005). It could
probably have a value as a complement to presence/absence recordings
considering the relatively low increase in workload to count individuals in
ponds sampled. Figure 6 illustrates how the minimal detectable difference
varies with SD and sample size using a paired t-test. Using the estimate of
SD of the abundance of T. cristatus from the large pond, (SD=10.8 when
using data from five observers) we can estimate the number of ponds
needed to detect an average change in abundance estimated at two points in
time. Although number of ponds is likely to be seen as a minimum value,
since the values are based on a pond with relatively high abundance and
large in size (smaller ponds will likely have a higher degree of sampling
error causing variation; Marsh 2001). Caution is generally needed when
drawing conclusions from amphibian abundance data, due to often high
seasonal fluctuations in population Coefficient of Variation
(CV=SD/(Average number of individuals); Marsh 2001). The CV for the
ponds in the intensive study were 0.407 and 0.947 for the large and small
pond, respectively.
The variation in abundance data can also be used for poweranalyses
for longer series. For example four sampling series conducted every other
year in the large pond would detect, at the 5% level, a 10% decrease in
abundance nine times out of ten (estimated using MONITOR 7.0; Gibbs
1995)
Longer time series from several ponds, with as little inter-pond
correlation as possible, might be necessary to differentiate variation caused
by natural fluctuations from that of antropogenic disturbances (Pechmann
et al.1991).
13
Mean of difference
50
40
30
SD 14
SD 12
SD 10
SD 8
SD 6
20
10
0
2
4
6
8
10
12
14
Sample size
Figure 6. Estimation of number of ponds needed to detect population changes
(Significance level=0.05, Power=0.8, H0: Mean of difference =0)
5.2.2 Interobserver variation
The significant differences between observers could probably in part be
explained by one observer having field experience of the method used
when this sampling was performed compared with observers with no
previous experience. The difference between observers appeared most
pronounced with regard to be able to distinguish juvenile T. cristatus from
adult T. vulgaris at a distance, and this will likely demand more practice
and experience than sampling adults. The most pronounced difference at a
distance is the heavier built T. cristatus juveniles with a relatively larger
head, but some practice is needed to easily see the difference. There are
also differences in markings but they are less obvious when seen from
above in suboptimal light and visibility conditions as often is the case.
The probability of two consecutive samplings not detecting T.
cristatus presence in the low abundance (small) pond by inexperienced
observers compared with a somewhat experienced observer was in this case
four times higher (0.36 vs 0.08). This is presumably to a large part caused
by the above-mentioned difficulty in distinguishing T. cristatus juveniles
from T. vulgaris adults since the probability of the somewhat experienced
observer getting a false zero increases to 0.20 if excluding juveniles from
the data. This could indicate that inexperienced observers need to sample
the same pond more than twice in order to be able to determine absence
with the same level of confidence as an experienced observer. Increasing
the number of samplings to three or four would decrease the probability to
0.22 and 0.13 respectively. Alternatively the samplings could be performed
14
with a couple of weeks separation, thereby allowing the observer to gain
experience between samplings.
The reason that sampling of larvae showed no significant interobserver differences is likely to be a combination of the fact that the
dipnetting method does not require ability to quickly determine species at a
distance, in contrast to the visual observation, and a relatively small
number of samplings. However, the larval sampling gave much lower
abundance values and is therefore not preferable compared with the visual
method in determining presence/absence due to the higher risk of “false
zeros” at low abundances.
5.3 Conclusions
Triturus cristatus does not appear to be useful as an indicator of
macroinvertebrate diversity under the circumstances in this study, but
further research in different spatial scales, habitat types and properties
being indicated should be undertaken before drawing final conclusions.
The monitoring methods of T. cristatus used gave a relatively low amount
of incorrect absence/presence assessments and appear to be accurate
enough to delineate expected trends in temporal variation. Interobserver
variation was evident, likely in large part due to different amounts of
sampling experience.
Acknowledgements
I thank my supervisors Per Milberg and Karl-Olof Bergman for their
support. I would also like to thank Tommy Karlsson at the County
Administrative Board in Östergötland and Jan Malmgren for their support
with species and methods information, and Anders Göthberg at Linköping
University for his help with invertebrate sampling and taxon determination.
References
Arntzen JW (2002) Seasonal variation in sex ratio and asyncronous
presence at ponds of male and female Triturus newts. Journal of
Herpetology 36, 30-35
Bailey LL, Simons TR, Pollock KH (2004) Estimating site occupancy and
species detection probability parameters for terrestrial salamanders.
Ecological Applications 14, 692-702
Beebee TJC, Griffiths RA (2005) The amphibian decline crisis: a
watershed for conservation biology? Biological Conservation 125, 271-285
15
Buckley J, Beebee TJC (2004) Monitoring the conservation status of an
endangered amphibian: the natterjack toad Bufo calamita in Britain.
Animal Conservation 7, 221-228
Faria MS, Ré A, Malcato J, Silva PCLD, Pestana J, Agra AR, Nogueira
AJA, Soares AMVM (2006) Biological and functional responses of in situ
bioassays with Chironomus riparius larvae to assess river water quality and
contamination. Science of the Total Environment 371, 125-137
Flather CH, Wilson KR, Dean DJ, McComb WC (1997) Identifying gaps in
conservation networks: of indicators and uncertainty in geographic-based
analyses. Ecological Applications 7, 531-542
Flint WD, Harris RN (2005) The efficiacy of visual encounter surveys for
population monitoring of Plethodon punctatus (Caudata: Plethodontidae)
Journal of Herpetology 39, 578-584
Gibbs JP (1995) MONITOR 7.0
http://www.mbr-pwrc.usgs.gov/software/monitor.html (9 mars 2007)
Grenyer R, Orme CDL, Jackson SF, Thomas GH, Davies RG, Davies TJ,
Jones KE, Olson VA, Ridgely RS, Rasmussen PC, Ding TS, Bennet PM,
Blackburn TM, Gaston KJ, Gittleman JL, Owens IPF (2006) Global
distribution and conservation of rare and threatened vertebrates. Nature
444, 93-96
Gustafsson DH, Petterson CJ, Malmgren JC (2006) Great crested newts
(Triturus cristatus) as indicators of aquatic plant diversity. Herpetological
Journal 16, 347-352
Hager HA (1998) Area-sensitivity of reptiles and amphibians: are there
indicator species for habitat fragmentation? Ecoscience 5, 139-147
Hedlund L, Robertson JGM (1989) Lekking behaviour in crested newts,
Triturus cristatus. Ethology 80, 111-119
Hess GA, Bartel RA, Leidner AK, Rosenfeld KM, Rubino MJ, Snider SB,
Ricketts TH (2006) Effectiveness of biodiversity indicators varies with
extent, grain, and region. Biological Conservation 132, 448-457
16
Houlahan JE, Findlay CS, Schmidt BR, Meyer AH, Kuzmin SL (2000)
Quantitative evidence for global amphibian population declines. Nature
404, 752-755
International Union for Conservation of Nature and Natural Resources
IUCN Red List of endangered species. http://www.iucnredlist.org/info/stats
(14 mars 2007)
Joly P, Miaud C, Lehmann A, Grolet O (2001) Habitat matrix effects on
pond occupancy in newts. Conservation Biology 15, 239-248
Karlsson T (2006) Större vattensalamander (Triturus cristatus) i
Östergötland: Sammanställning av inventeringar 1994-2005 och övriga
fynd i Östergötlands län. Länsstyrelsen Östergötland, rapport 2006:4.
Lawler JJ, White D, Sifneos JC, Master LL (2003) Rare species and the use
of indicator groups for conservation planning. Conservation Biology 17,
875-882
Lindenmayer DB, Fischer J (2003) Sound science or social hook-a
response to Brooker’s application of the focal species approach. Landscape
and Urban Planning 62, 149-158
Malmgren J, Gustafsson D, Pettersson CJ, Grandin U, Rygne H (2005)
Inventering och övervakning av större vattensalamander. Naturvårdsverket
Malmgren J (2002) Faktablad: Triturus cristatus – större vattensalamander.
ArtDatabanken.
Marsh DM (2001) Fluctuations in amphibian populations: a meta-analysis.
Biological Conservation 101, 327-335
Pechmann JHK, Scott DE, Semlitsch RD, Caldwell JP, Vitt LJ, Gibbons
JW (1991) Declining amphibian populations: the problem of separating
human impacts from natural fluctuations. Science 253, 892-895
Schmidt BR (2005) Monitoring the distribution of pond breeding
amphibians when species are detected imperfecly. Aquatic Conservation:
Marine and Freshwater Ecosystems 15, 681-692
17
Schmidt BR (2004) Declining amphibian populations: the pitfalls in count
data in the study of diversity, distributions, dynamics, and demography.
Herpetological Journal 14, 167-174
StatSoft, Inc. (2004) STATISTICA (data analysis software system), version
7, www.statsoft.com
Sauro J, Lewis JR (2005) Estimating completion rates from small samples
using binomial confidence intervals: Comparisons and recommendations.
In Proceedings of the Human Factors and Ergonomics Society 49th Annual
Meeting (pp. 2100-2104). Santa Monica, CA: Human Factors and
Ergonomics Society. http://www.measuringusability.com/wald.htm
(14 mars 2007)
Studysize 1.0 CreoStat HB. Enbarsv.11. 42655 V.Frolunda. Sweden.
ter Braak CJF Smilauer P (2002) Canoco reference manual and user’s
guide to Canoco for windows: software for Canonical Community
Ordination (version 4). Microcomputer Power, Ithaca, NY.
18