Electronic Supplementary Materials
The climate sensitive zone along an altitudinal gradient in central Himalayan rivers: a useful concept to
monitor climate change impacts in mountain regions
Ram Devi Tachamo Shah1,2*, Subodh Sharma3, Peter Haase1,2, Sonja C. Jähnig4+, Steffen U. Pauls1+*
1
Senckenberg - Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, Frankfurt am Main,
D-60325, Germany
2
Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum,
Clamecystrasse 12, D-63571 Gelnhausen, Germany
3
Department of Environmental Science & Engineering, Kathmandu University, P.O. Box 6250 Kathmandu,
Nepal
4
Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Department of Ecosystem Research,
Müggelseedamm 301, 12587 Berlin, Germany
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
*Corresponding authors:
E-mail: [email protected]
E-mail: [email protected]. Tel.: + 49 (0) 69 7542-1884. Fax: + 49 (0) 69 7542-1800
+
shared senior authorship
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Online Resource 1. Distribution of Sampling Sites
Figure OR1: Sampling locations in headwater streams in (a) central and (b) eastern regions of Nepal, central
Himalaya; Streams are tributaries of Langtang River in Langtang valley, streams in Gosaikunda valley, Nagmati
River in Kathmandu valley, tributaries of Indrawati River system in Shindupalchwok area and tributaries of
Arun River system in Makalu Barun valley. Relationships between temperature and altitude for sampling
locations: (c) Annual mean air temperature (extracted from worldclim (Hijmans et al. 2005)) and (d) mean water
temperature for 9 – 12 months of the study period between the years 2012 and 2013 (extracted from temperature
loggers between altitudes 3900 m and 1500 m asl).
Online Resource 2. Detailed methodology of the multivariate generalized linear model (GLM)
We used GLM to identify which environmental predictors significantly influence the invertebrate community
composition, as well as individual taxa. First, we specifically tested how communities are influenced by twelve
environmental factors. Implying subsets of environmental variables as a single variable in the model could
interfere with the final output of the model. We thus ran a principal component analysis (PCA) to obtain the
combined single variable that best captures the essence of the subset variables. PCA was run separately for flow
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parameters (velocity, width, and depth); substrate parameters (% of boulders, cobbles, gravels and sands); flow
patterns (% riffles, runs, and pools). Prior to PCA, the subsets of environmental variables that were estimated in
percentage cover e.g., substrate composition and flow pattern were transformed to Arcsine Square root. The first
axis of the PCA was extracted for GLM. Using correlated variables in GLM may lead to inaccurate model
parameterization (Graham 2003). Thus, we calculated pairwise Pearson’s correlation between predictor
variables to avoid multicollinearity among the 12 explanatory variables (altitude, mean annual temperature,
mean annual precipitation, distance from source, aspect, canopy coverage (%), pH, conductivity, oxygen
saturation (%), pc1flow, flowpattern_pc1, and pc1substrate). Variables with a correlation coefficient >0.6 with
altitude were removed (i.e., temperature and precipitation were removed) (Feld and Hering 2007). We
ultimately ran the GLM using the R package mvabund (Wang et al. 2012) based on seven environmental
parameters and three PCA axes (Table 1). Second, we extracted taxon-specific responses to each of the
environmental parameters from the GLM. A separate model was fit to each taxon through the manyglm
function, using an uncorrelated set of explanatory variables simultaneously with all explanatory variables being
centered and scaled. The analysis was run on the raw abundance data using a negative binomial distribution.
ANOVA was used to identify significant effects of variables with the anova.manyglm function (likelihood ratio
test, 500 bootstrap replicates).
Online Resource 3. Details on the Threshold Indicator Taxa Analysis (TITAN) method
We used TITAN (Baker and King 2010) to identify potential change points in both the relative frequency and
abundance of individual taxa along an environmental gradient, and assess possible synchrony among taxa
change points as evidence for changes in the community. The analysis can thus serve to identify the CSZ, as
well as diagnose taxa that are particularly responsive to the altitudinal gradient and may be used to monitor the
CSZ. TITAN takes into account each taxon’s occurrence, its abundance and directionality of response along the
environmental gradient to provide an environmental threshold for individual taxa. When analyzing multiple
taxa, a threshold is also generated for the entire community. TITAN splits samples into two groups and seeks
the value along the environmental gradient that maximizes the association between each taxon and the
environmental variable of one side of the partition (Baker and King 2010). The association is measured by
indicator value scores (IndVals, Dufrene and Legendre 1997) and standardized as z scores to facilitate crosstaxon comparison via permutation of samples along the environmental gradient. Indicator value score range
from 0-100 %, whereby a score of 100% indicates a perfect association i.e., a taxon was collected in every
sample within the respective group and not in any other group (King et al. 2011). In general the method
distinguishes two groups of indicator taxa: 1) taxa with a negative (z-) response, and 2) taxa with a positive (z+)
response. Taxa with a negative response (z-) show a decline in frequency and abundance with increasing values
of the prevailing environmental conditions along the environmental gradient; whereas z+ taxa show an increase
in frequency and abundance with increasing values of the prevailing environmental conditions along the
environmental gradient. Bootstrap resampling estimates the quality of the indicator taxa by assessing purity (i.e.,
taxa respond consistently in the same direction) and reliability (i.e., taxa show consistently strong changes) as
well as uncertainty of the change point locations of individual taxa and the entire community. Only taxa with
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purity > 0.95 and reliability > 0.95 are considered as robust indicators taxa and achieve p < 0.05 (Baker and
King 2013), indicating consistent changes in same direction, during resampling. Uncertainty in change points
along the environmental gradient is indicated by the width of the confidence interval and is shown as a
horizontal line for each taxon (Baker and King 2010). We computed uncertainty of change points with 500
bootstrap replicates. The 95% confidence interval of the change points for increasing taxa (z+) begin with lower
end of the distribution (i,e, the left end of the horizontal line); for the taxa with negative response (z-) the 95%
confidence intervals for the change points begin at the upper end of the distribution (i.e. right end of the
horizontal line) (King and Baker 2014). Community change points of negative and positive taxa are determined
separately by tabulating and summing all z- and z+ scores for each value of environmental gradient. The values
of environmental gradient where the largest cumulative z score for negative [sum z-] and positive [sum z+] are
achieved indicate the maximum aggregated change in frequency and abundance of the respective taxa group
(Baker and King 2010). Community change points of respective taxa response (z- and z+) are visualized by
plotting sum z scores against the environmental variable (Baker and King 2010).
Online Resource 4. Pattern of taxonomic richness
Figure OR4: Number of taxa (black circle) recorded per sites at different altitudes. Line denotes second order
polynomial curve.
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Online Resource 5. Detailed results of the GLM
Multivariate generalized linear model of 10 environmental variables and benthic macroinvertebrate
assemblages. pc1: 1st axis of Principal component. Flow includes velocity, width and depth of water. Flow
pattern includes percentage of riffles, pools and runs. Substrate composition includes percentage of boulders,
cobbles, pebbles and sands. Res.Df: residual degrees of freedom; Dev: deviance. The significance is tested
based on likelihood ratio and 500 bootstrap replicates.
Multivariate test:
Res.Df
Dev
p-value
(Intercept)
47
Altitude
46
561.2
distance from source
45
169.8
0.06
Aspect
44
129.1
0.26
Canopy coverage (%)
43
194.5
0.09
pH
42
223.3
0.12
Conductivity
41
123.1
0.40
Oxygen saturation (%)
40
196.1
0.44
pc1_flow
39
199.9
0.35
pc1_ flow pattern
38
184.3
0.31
pc1_substrate composition
37
160.9
0.41
0.002**
Online Resource 6. Supplementary TITAN results
TITAN community-level change points estimated from taxa responses to altitudinal gradient of river systems in
the central Himalaya. The cp (in m asl) stands for the TITAN observed change point value of altitudinal
gradient in which the largest sum of indicator value (IndVal) z scores among all negative (z-) and positive (z+)
taxa occur, respectively. Quantiles 5%, 50%, and 95% correspond to change points (in m asl) from 500
bootstrap replicates.
Method
cp
5%
50%
95%
TITAN sum (z-)
2905
2595
2905
3496
TITAN sum (z+)
3124
2312
2912
3601
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References for Electronic Supplementary Materials
Baker ME, King RS (2010) A new method for detecting and interpreting biodiversity and ecological community
thresholds. Methods Ecol Evol 1:25-37.
Baker ME, King RS (2013) Of TITAN and straw men: an appeal for greater understanding of community data.
Freshwater Sci 32:489-506.
Dufrene M, Legendre P (1997) Species assemblages and indicator species: The need for a flexible asymmetrical
approach. Ecol Monograph 67:345-366.
Feld C, Hering D (2007) Community structure or function: effects of environmental stress on benthic
macroinvertebrates at different spatial scales. Freshwater Biology, 52, 1380–1399.
Graham,MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology 84: 2809-2815.
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate
surfaces for global land areas. International Journal of Climatology, 25, 1965–1978.
King RS, Baker ME (2014) Use, misuse, and limitations of Threshold Indicator Taxa Analysis (TITAN) for
natural resource management. Pages 231-254 in Guntenspergen GR (editor). Application of Threshold
Concepts in Natural Resource Decision Making. Springer New York.
King RS, Baker ME, Kazyak PF,Weller DE (2011) How novel is too novel? Stream community thresholds at
exceptionally low levels of catchment urbanization. Ecol Appl 21:1659-1678.
Wang Y, Naumann U, Wright ST, Warton DI (2012) mvabund– an R package for model-based analysis of
multivariate abundance data. Methods Ecol Evol 3:471-474.
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