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Using phylogenetic information and
chemical properties to predict species
tolerances to pesticides
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Research
Cite this article: Guénard G, Carsten von der
Ohe P, Carlisle Walker S, Lek S, Legendre P.
2014 Using phylogenetic information and
chemical properties to predict species
tolerances to pesticides. Proc. R. Soc. B 281:
20133239.
http://dx.doi.org/10.1098/rspb.2013.3239
Received: 9 January 2014
Accepted: 11 June 2014
Subject Areas:
ecology, evolution, computational biology
Keywords:
phylogenetic modelling, bilinear model,
tolerance, pesticide, effect prediction model
Author for correspondence:
Guillaume Guénard
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rspb.2013.3239 or
via http://rspb.royalsocietypublishing.org.
Guillaume Guénard1, Peter Carsten von der Ohe2,3, Steven Carlisle Walker4,
Sovan Lek5 and Pierre Legendre1
1
Département de Sciences Biologiques, Université de Montréal, CP 6128, Succursale Centre-Ville, Montréal,
Québec, Canada H3C 3J7
2
Department of Ecological Chemistry, Helmholtz Centre for Environmental Research –UFZ, Permoserstrasse 15,
Leipzig 04318, Germany
3
Amalex Environmental Solutions, Kreuzstrasse 1, Leipzig 04103, Germany
4
Department of Mathematics and Statistics, McMaster University, 1280 Main Street West, Hamilton,
Ontario, Canada L8S 4K1
5
Laboratoire Évolution et Diversité Biologique (EDB) UMR 5174, CNRS/Université Paul-Sabatier,
118 Rte de Narbonne 4R3-b1, Toulouse Cedex 9 31062, France
SCW, 0000-0002-4394-9078; PL, 0000-0002-3838-3305
Direct estimation of species’ tolerance to pesticides and other toxic organic
substances is a combinatorial problem, because of the large number of
species–substance pairs. We propose a statistical modelling approach to predict
tolerances associated with untested species–substance pairs, by using models
fitted to tested pairs. This approach is based on the phylogeny of species and physico-chemical descriptors of pesticides, with both kinds of information combined
in a bilinear model. This bilinear modelling approach predicts tolerance in
untested species–compound pairs based on the facts that closely related species
often respond similarly to toxic compounds and that chemically similar compounds often have similar toxic effects. The three tolerance models (median
lethal concentration after 96 h) used up to 25 aquatic animal species and up
to nine pesticides (organochlorines, organophosphates and carbamates). Phylogeny was estimated using DNA sequences, while the pesticides were described
by their mode of toxic action and their octanol–water partition coefficients. The
models explained 77–84% of the among-species variation in tolerance (log10
LC50). In cross-validation, 84–87% of the predicted tolerances for individual
species were within a factor of 10 of the observed values. The approach can
also be used to model other species response to multivariate stress factors.
1. Introduction
Organisms are adapted to exploit the resources of their habitats and withstand a
range of environmental conditions found therein. Stress or disturbance occurs
when conditions depart from that range [1]. Toxic stress occurs when exposure
to chemical substances is sufficient to disrupt important physiological processes,
leading to depression or complete failure of functions that are necessary for
growth, reproduction or survival. Such disruption depends on a broad array of
interactions, at the molecular level, between physico-chemical properties of the
compound and biochemical traits (e.g. receptors and enzymes) of the organism.
Hence, tolerance to toxic stress can be regarded as a complex species trait integrating an array of subordinate biochemical and physiological features. On the other
hand, chemical compound toxicity results from the physico-chemical properties
of the compounds; these properties can be regarded as features of the compounds.
Toxic stress therefore arises from complex interactions between the characteristics
of the organisms and those of the chemical compounds.
Understanding the effects of toxicity on living organisms is mandatory to
assess the impact of chemical pollution on the environment [2]. An appreciable
proportion of the numerous chemical compounds that are developed, produced
& 2014 The Author(s) Published by the Royal Society. All rights reserved.
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(a) Data sources
We obtained tolerance data from the US Environmental Protection
Agency ECOTOX database [13]. We selected data of median lethal
(b) Phylogenetic analysis
We estimated the phylogenies using DNA sequences obtained
online from the US National Center for Biotechnology Information’s
GenBank website (http://www.ncbi.nlm.nih.gov/genbank/; database: Nucleotide). We collected the available DNA sequences for the
whole mitochondrial genomes as well as for the nuclear 28S, 18S and
5.8S rRNA transcripts and their internal transcribed spacers (ITS 1
and ITS 2). We used sequences for the complete genes, whenever
possible, and took the longest sequences available when only partial
sequences could be found. When a sequence for a widespread gene
was not available for a given species and no other species in the same
genus or family was in the dataset, we used that of a related species
instead. Then, we performed multiple DNA sequences alignment separately for each individual gene using the program
MUSCLE v. 3.8.31 ([15]; http://www.drive5.com/muscle). After
alignment, we concatenated the sequences for all genes into a
single super-alignment and estimated the phylogeny using the
maximum-likelihood method [16,17], which was computed by
program fdnaml within software EMBOSS v. 6.3.1 [18].
(c) Modelling approach
Phylogenetic eigenvector regression [19,20] is an approach
whereby eigenvectors are used as descriptors in a regression
model. For this study, we used a particular type of such eigenvectors called phylogenetic eigenvector maps (PEM) [12]. PEM
allows one to calculate phylogenetic eigenvectors that are finetuned to the processes through which traits have evolved
(neutral evolution or natural selection). In its simplest form,
PEM calculation involves the estimation of a selection strength
parameter a that take values between 0 ( purely neutral evolution)
and 1 (strong natural selection).
Calculation of PEMs and of the species scores used to make
predictions is described in [12]; here, we will only summarize the
method. These calculations involve the singular value decomposition of a weighted influence matrix (i.e. a mathematical
representation of the edges linking the nodes and leaves of the
tree) whose columns are centred on 0. These eigenvectors allow
one to decompose species trait variation into components associated with different phylogenetic resolution levels; they are used as
descriptors in the bilinear models for predicting tolerance to toxic
substances. One can then obtain scores for new species for which
predictions are to be made (we will hereafter refer to them as the
‘target’ species) using calculations similar to those that are made
when a new point is added to an ordination diagram [21,22].
These scores can be used in models to predict tolerance (figure 1).
Note that these are true out-of-sample predictions because
the target species are not used to estimate the parameters of the
bilinear model.
In this study, we used phylogenetic eigenvectors to explain
the among-species variation in LC50; let U ¼ [ui,k] be a design
2
Proc. R. Soc. B 281: 20133239
2. Material and methods
concentrations (LC50) after 96 h of exposure excluding entries with
inequality indications (i.e. greater than or smaller than). The mortality was selected as the endpoint because it is the one for which
data are the most abundant. We performed a quality check when
multiple LC50 values were found for one substance-species
combination (see section ‘Data quality check’ in the electronic supplementary material for details). From the resulting dataset, we
selected the species and pesticides that allowed us to build the
species-by-pesticides table having the largest number of elements,
without missing information. The data for any given model were
assembled in a multivariate response matrix LC50 ¼ [LC50i,j ]
whose rows i represent individual species and whose columns j
represent a set of pesticides, and LC50i,j is the log10 of the concentration of pesticide j causing 50% death for species i after 96 h.
We obtained the octanol–water partition coefficients (kow) for
each pesticide from the KOWWIN software [14].
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and used by humans eventually reach ecosystems. These ecosystems are themselves populated by many different species,
each of them having its particular susceptibilities towards
each of those compounds. The exhaustive testing of every possible species-substance combination is impossible in practice
because ecosystems are generally inhabited by hundreds to
thousands of species and affected by environmental mixtures
containing hundreds, sometimes thousands, of different
compounds [3].
To our knowledge, most approaches proposed so far have
addressed this problem as two separate issues: compound
multiplicity and species diversity. Quantitative structure–
activity relationships (QSAR) [4–10] seek to address compound multiplicity by predicting physiological effects of
chemical compounds on single biological endpoints from
their molecular structure. QSAR parameters are estimated
using observed effects of compounds (e.g. their level of toxicity from a given mode of action) on target organisms and
descriptors of its chemical structure and properties. Phylogenetic modelling [3], on the other hand, seeks to address
species diversity by estimating species’ unknown tolerances
to toxic compounds or other stressors from that of other
related species with known values. This approach relies on
the fact that traits which influence tolerance are subject to evolutionary change, and therefore that the effects of these traits can
be modelled as phylogenetic signals. These signals can be
regarded as a combination of large-scale phylogenetic structures and small-scale structures. A large-scale structure arises
from an evolutionary change that occurred early in the phylogeny, with observable effects on lineages shared by a large
number of species. A small-scale structure, on the other hand,
arises from changes that occurred recently in the phylogeny,
thereby only affecting a few, closely related, species. Here, we
demonstrate that chemical compound multiplicity and species
diversity can be addressed as a single problem.
Bilinear modelling is an extension of multivariate linear
regression where a table of response variables is fitted
using two different tables of descriptors [11]. The first table
contains descriptors of the variation among the rows of the
multivariate response data (i.e. as in multiple regression),
whereas the second table contains descriptors of the variation
among the columns of the multivariate response. A bilinear
model can therefore be used to relate a multivariate response,
such as the tolerance of many species to multiple compounds,
to descriptors of the species’ phylogenetic signal component
[3,12] and descriptors of the molecular structure and/or
physico-chemical properties [10], as those used for QSAR.
The objective of this study is to demonstrate how bilinear
modelling can be used to develop models addressing the
issue of predicting the tolerance of multiple species to multiple compounds. To exemplify the modelling approach, we
applied it to the modelling of multiple species tolerances to
acute toxicity by multiple pesticides, using phylogenetic
eigenvectors and physico-chemical properties as predictors.
The ability of the models to make external predictions of
tolerance to pesticides was also assessed.
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u1
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U : row-wise explanatory variable (e.g. eigenvectors)
Z : column-wise explanatory variable (e.g. properties)
C : matrix of billinear regression coefficients
Utarget : predictors (e.g. phylogenetic eigenfunctions)
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·...Ò : concatenation of the columns of a matrix
learning
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property of each of
the responses
·ŶfitÒ = (Z ƒ U) ·CÒ
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B
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Figure 1. Example flowchart of bilinear model calculation. (a) Y is a multivariate response matrix whose values for y1, y2, y4, and y5 are known for seven model
species A 2 G (black markers), whereas the values that are being predicted (grey markers) are those of three target species X, Y and Z for all the responses ( yX,. . .,
yY,. . . and yZ,. . ., respectively) as well as those of all species for response y3. Two kinds of information are available in the form of row-wise and column-wise
descriptors and predictors. (b) The row-wise design matrix U contains the species loadings of the model species on the phylogenetic eigenvectors uk (black markers),
whereas the matrix of row-wise predictors U target contains the target species scores on the phylogenetic eigenfunctions (grey markers). (c) The column-wise design
matrix Z contains a descriptor z l (e.g. a chemical property; there could have been many column-wise descriptors); here, it is also used as a predictor as it is known
for all response variables. Model building proceeds as follows: using the observed response variables (1) together with the species loadings (2) and the column-wise
descriptor(s) (3), we calculate C, the matrix of bilinear regression coefficients. This matrix contains the intercept, the main effects of the individual phylogenetic
eigenvectors uk, the main effects of the individual properties zl and the interactions among the row-wise and column-wise descriptors. The model interrogation
proceeds as follows: we pre-multiply C, which was estimated from data (4), with the design matrix containing U target (5) and post-multiply it with the transpose of
Z (6), yielding predicted values (7).
matrix of phylogenetic eigenvectors whose elements ui,k are
the loadings of species i (rows) on eigenvectors k (columns). As
mentioned in the Introduction, a bilinear model requires a
second set of descriptors. In our study, these descriptors are
properties of the pesticides that we used to explain variation in
the columns of matrix LC50. Let Z ¼ [zj,l ] be a design matrix of
descriptors of the chemical properties l of pesticides j. LC50 can
be represented as the matrix product
LC50 ¼ UCZT þ E,
(2:1)
where C ¼ [cj,l ] is a matrix of bilinear regression coefficients,
and E ¼ [1i,j ] is a matrix of error terms (see section ‘Estimating
the matrix of bilinear regression coefficients’ in the electronic
supplementary material for details).
The design matrix describing the among-row variation (U) had
a centring variable (i.e. a column of ones to estimate the intercept)
in addition to the phylogenetic eigenvectors. The design matrix
describing the among-column variation (Z) included a centring
variable, contrasts variables depicting the different modes of
action of the pesticides and the log10 of the octanol–water partition
coefficient (i.e. the proportion of the compound dissolving in octanol solvent compared with that dissolving in water solvent) as a
descriptor of pesticides in LC50, and p q columns, where p and
q are the numbers of columns in U and Z, respectively. We regularized the bilinear models in order to maximize the ability of the
models to predict LC50. Regularization was performed by the forward selection of a subset of the modelling terms (elements cj,l ) on
the basis of the Akaike information criterion with correction for
small sample sizes (AICc [23]). We chose the subset that minimized
AICc, thereby obtaining models minimizing information loss.
Elements of C that were found to be irrelevant for prediction
were given a value of 0, resulting in a sparse bilinear model.
We used cross-validation to evaluate the ability of models to
make dependable and accurate predictions. For each model,
cross-validation was performed by first selecting a given species
i, then performing the aforementioned model construction procedure using the n 2 1 remaining species, predicting the LC50 for
species i, and finally repeating the procedure for each and every
species in the dataset. In addition to cross-validation of the species,
we obtained predictions for tolerance to other pesticides not
used in model estimation and compared them to their observed values. Following these predictive validations, we assessed
models performance at the global and observation scales using
the prediction coefficient P 2 and the deviation factor di,j, respectively (see section ‘Metrics of model performance’ in the
electronic supplementary material for calculation details).
Model calculation and cross-validation were performed
using the R language and environment v. 3.0.1 [24] and R
package ape v. 3.0– 8 [25].
(d) Candidate models
To explore the performance of our modelling approach under different circumstances of data availability, we defined three bilinear LC50
models from the data described in table 1. All models span three
Proc. R. Soc. B 281: 20133239
c00 : intercept
c0u : main effect of phylogenetic eigenfunction uk
k
cz 0 : main effect of compound property zl
l
cz u : interactions between uk and zl
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fy
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Y : multivariate response matrix (e.g. LC50)
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name
log10 kow
mode of action
used
50293
DDT
6.91
NaM
123
55389
56382
fenthion
parathion
4.09
3.83
AChE
AChE
2, 3
123
58899
lindane
3.72
GABA
123
60571
63252
dieldrin
carbaryl
5.40
2.36
GABA
AChE
123
2, 3
115297
121755
endosulfan
malathion
3.83
2.75
GABA
AChE
123
122145
fenitrothion
3.30
AChE
123
333415
2921882
diazinon
chlorpyrifos
3.81
4.96
AChE
AChE
2, 3
123
1, 2
8001352
toxaphene
3.30
GABA
3
1, 2
modes of action: acetylcholinesterase inhibition (AChE), g-amino
butyric acid-triggered chloride channel antagonism (GABA) and
sodium channel modulation (NaM).
The first model (M1) incorporates five pesticides: malathion
and parathion (AChE), lindane and dieldrin (GABA) and DDT
(NaM), as well as 25 species. The 25 species included two amphibians, 13 fish species, six crustaceans and four insects; total
number of observations: 125. The two other models included
more pesticides; the cost was that they had smaller numbers of
species, given the available data. We obtained a second model
(M2) by adding the pesticides carbaryl and fenthion (AChE)
and dropping six species, which left 19 species for modelling (13 fish species, five crustaceans and one insect, total
number of observations: 126). In a third model (M3), we added
chlorpyrifos (AChE) and toxaphene (GABA), which required
dropping four more species, leaving 15 species for modelling
(10 fish species, four crustaceans and one insect, total number
of observations: 117).
We obtained predictions for each of these three models by using
data for the pesticides that were not used to estimate model parameters. These other pesticides span three additional compounds:
endosulfan (GABA), fenitrothion and diazinon (both AChE).
To represent the difference in the effects of the three modes
of actions in the column-wise descriptor matrices (Z), we defined
two orthogonal contrast variables called MOA1 and MOA2
(see section ‘Orthogonal contrast variables’ in the electronic
supplementary material for more details).
We found DNA sequences for most species, with the exception of the Isopode (Crustacea) Asellus brevicaudus and the
Hemiptera (Insecta) Notonecta undulata, for which we substituted
the congeneric species Asellus aquaticus in the first case, and for
which information was only available at the genus level in the
second case. The most widespread DNA sequences were
the mitochondrial large ribosomal subunit (16S; 25 species, of
which 12 were complete and 13 were partial), the cytochrome
oxidase subunit 1 (COX1; 24 species: 13 complete, 11 partial),
the mitochondrial small ribosomal subunit (12S; 23 species: 13
complete, 10 partial), the nuclear small ribosomal subunit
(18S; 21 species: height complete, 13 partial) and cytochrome b
(CYTB; 21 species: 13 complete, height partial). Most of the
remaining 39 sequences that were available for fewer than 19
species were complete mitochondrial DNA sequences for species
whose entire mitochondria can be found on GenBank.
predictions
1
1
123
3. Results
With a few exceptions, the phylogeny we estimated from
DNA data placed most species within their recognized taxonomic group (see section ‘Estimated phylogeny’ in the
electronic supplementary material for more details on the
phylogeny and common species names). The PEM used to
calculate models M1 and M2 had small selection strength
parameters (a, table 2) indicating nearly neutral and neutral
trait evolution, respectively, whereas M3 had much higher
value of a, indicating a stronger intervention of natural selection, on average, when pesticides chlorpyrifos and toxaphene
are used in the model. The number of bilinear regression
terms involved in the models ranged from 24 (M1, five
main effect and 19 interaction terms; table 2; see ‘tables of
bilinear model coefficients’ in the electronic supplementary
material for more details on bilinear model parameters) to
11 (M3, five main effect and six interaction terms; M2 had
19 terms, with nine main effect and 10 interaction terms).
The models had adjusted R 2 (R2adj: ) ranging from 0.77 to
0.84 (from 0.16 to 0.30 when using only chemical descriptors). The prediction coefficients (P 2) obtained by
comparing observed LC50 values with those predicted following leave-one-out jack-knives ranged from 0.49 to 0.73
(table 2 and figure 2). The percentages of these individual
jack-knifed predictions having an absolute deviation factor
(jdj) lower than 10 (i.e. a variation often observed between
studies for the same species –compound combination)
ranged from 84 to 87%, whereas from 0 to 2.3% of the predictions deviated from observations by a factor of 100 or more.
The most extreme deviations by model were observed for
species A. brevicaudus and pesticide malathion in models
M1 (d ¼ 2246) and M2 (d ¼ 2313), as well as for Cyprinus
carpio and dieldrin in model M3 (d ¼ 297).
The numbers of predictions made for pesticides not used
in the models were 118 for M1, 68 for M2 and 31 for M3. They
had P 2s ranging from 0.48 to 0.74 (table 2 and figure 3). From
61 to 87% of the predictions were within an absolute deviation factor of 10 of the observed LC50 values, whereas
from 0 to 3% of the predictions deviated by a factor 100 or
Proc. R. Soc. B 281: 20133239
CAS no.
4
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Table 1. The pesticides, their properties used for phylogenetic bilinear modelling (mode of action, NaM: Naþ channel modulator, AChE: Acetylcholinesterase
inhibitor and GABA: GABA-gated Cl2 channel antagonist; and log10 octanol– water partition coefficient), the models in which they are used and the model(s)
for which predictions were calculated.
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parameter
sample size
adjusted R 2
P
2
deviation factor
M2
M3
species
25
18
13
compounds
total
5
125
7
126
9
117
chemistry
0.06
1
0.00
3
0.57
2
phylogeny
interaction
4
19
6
10
3
6
chemistry alone
0.16
0.30
0.19
all terms
jack-knife
0.80
0.49
0.84
0.64
0.77
0.73
jdj , 10
0.54
84%
0.48
87%
0.74
86%
jdj , 100
98%
98%
100%
jdj , 10
jdj , 100
61%
97%
65%
97%
87%
100%
external
jack-knife
external
greater. Most of the extreme predictions by model were overestimates of Ictalurus punctatus tolerance to endosulfan (M1:
d ¼ 152) and diazinon (M2: d ¼ 127, M3: d ¼ 58). A small prediction bias was evidenced for model M2 (the 95% confidence
limits of the regression line did not include the 1 : 1 line in the
whole range of observed values), the latter having a tendency
to predict LC50s were smaller than the observed values. For
models M1 and M3, the 1 : 1 line fit within the 95% confidence limits of the regression line and no prediction bias
was evident.
4. Discussion
In this paper, we developed an approach to predict tolerance
using species phylogenetic patterns of pesticide tolerance
coupled with properties used as proxies of the activity of the
compounds. The models presented here as examples used relatively simple column-wise descriptor matrices encompassing
only the mode of toxic action (in the form of orthogonal
contrast variables) and log10 kow. This modelling approach
(i.e. using phylogenetic eigenvectors within a bilinear model)
has, however, a much wider application scope; it can be
applied whenever one wants to model multiple species attributes towards sets of environmental conditions that species
are exposed to. For example, one may want to predict multiple
species reactions (e.g. physiological, behavioural and immunological endpoints) to a mixture of toxic stresses observed in situ
using descriptors for this mixture in addition to phylogenetic
eigenvectors. Still, despite the fact that we used relatively
simple bilinear models for expository purposes, we were able
to predict from half to two-thirds of the out-of-sample variation
in LC50 values (0.50–0.66) for jack-knifes and from half to
three-quarters (0.48–0.74) for a set of external compounds.
We stress that these were true out-of-sample predictions, and
therefore are honest assessments of the information content
in phylogenetic trees about the relationships between pesticide
properties and species tolerances.
(a) Candidate models
The challenge in selecting tolerance data was to obtain a
broad phylogeny that encompassed species from as many
higher taxonomic groups as possible with, ideally, several
representative species for each of these groups, while
having complete sets of values for as many pesticides as possible. The three models covered the following cases: (M1)
many species, but few substances, (M3) many substances,
but few species, and (M2) an intermediate model between
these two extremes. As a consequence of data availability
and the requirement that the response matrix (Y) be complete
(no missing value for any element yi,j ), all three models were
implemented using modest numbers of species, compared
with those typically inhabiting ecosystems, and very few
compounds, compared with the many chemicals that are
found in ecosystems.
Although these models may be useful for several applications, we hope that enhanced models, with many more
species and compounds, will be developed. We propose
the following four steps to maximize the development of
phylogenetic bilinear models in the near future. First, it is
necessary to pinpoint, within the pool of currently unavailable data, those data whose estimation using bioassays
would maximize the size of the response matrix Y in the
training data. To do so, an online database can be queried
to highlight unavailable species – pesticide pairs associated
with either many species or many pesticides. For example,
if a particular species is lacking tolerance data about only
one pesticide, then a bioassay on that species –pesticide pair
will permit the addition of all measured tolerances for that
particular species to the response matrix. Second, the suites
of bioassays to estimate LC50 should be performed for the
Proc. R. Soc. B 281: 20133239
selection strength (a)
number of terms
M1
5
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Table 2. Comparison of three phylogenetic bilinear model: sample size, selection strength (a), number of bilinear regression terms, adjusted coefficient of
determination (R2adj: ), prediction coefficient (P 2) and the percentages of deviation factor values (d ) that are below given values for jack-knife (performed for one
species at a time using a model developed using the remaining species) and external (using all the species of a given model and predicting for pesticides that
were not in the models) predictions.
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(a)
6
lindane
malathion
DDT
parathion
dieldrin
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
lindane
malathion
DDT
parathion
dieldrin
carbaryl
fenthion
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
lindane
malathion
DDT
parathion
dieldrin
carbaryl
fenthion
chlorpyrifos
toxaphene
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
10–3 1 103
Proc. R. Soc. B 281: 20133239
(b)
rspb.royalsocietypublishing.org
O. mykiss
O. mossambicus
O. latipes
G. affinis
P. reticulata
M. salmoides
L. macrochirus
L. cyanellus
M. saxatilis
C. carpio
C. auratus
P. promelas
I. punctatus
B. woodhousei
P. triseriata
C. dipterum
Peltodytes ssp.
P. californica
N. undulata
D. pulex
D. magna
M. monoceros
G. fasciatus
G. lacustris
A. brevicaudus
O. mykiss
O. mossambicus
O. latipes
G. affinis
P. reticulata
M. salmoides
L. macrochirus
L. cyanellus
M. saxatilis
C. carpio
C. auratus
P. promelas
I. punctatus
P. californica
D. pulex
D. magna
G. lacustris
A. brevicaudus
(c)
O. mykiss
G. affinis
P. reticulata
L. macrochirus
L. cyanellus
C. carpio
C. auratus
P. promelas
I. punctatus
P. californica
D. pulex
D. magna
G. lacustris
LC50 (µmol l−1)
Figure 2. The observed LC50 (filled squares) and values predicted using phylogeny (unfilled circles) following leave-one-out jack-knife runs on the datasets used to
build model M1 (a; 25 species and five pesticides), M2 (b; 19 species and seven pesticides) and M3 (c; 15 species and nine pesticides).
still missing species –pesticide combinations with the most
useful predictive information. Third, the quality of the explanatory variables could be improved. For example, the
phylogeny could be re-estimated using DNA sequence for
the poorly resolved species. Furthermore, more descriptors
of chemical activity (e.g. new metrics of molecular structure,
enzymatic assays) should also be considered in order
to improve models. It is noteworthy that using the toxic
mode of action limits the scope of application of models to
compounds with modes of action that are known and represented in the models. Future efforts to seek better descriptors of
chemical activity for use in phylogenetic bilinear tolerance
models should therefore focus on those that are easy to
estimate (e.g. using descriptors of molecular structure and
activity, in vitro tests) and applicable to many (ideally all) compounds. Fourth, the phylogenetic bilinear model should be
re-estimated to account for any new information provided by
the previous three steps. We view these four steps as an
Downloaded from http://rspb.royalsocietypublishing.org/ on June 18, 2017
model 1
2.0
(b)
2.0
P2 = 0.54
1.0
1.0
–0.5
–0.5
k
–2.0
j
vk
−3.5
g
P2 = 0.74
1.0
–0.5
–2.0
j
vk
k
−3.5
−3.5 –2.0 –0.5 1.0 2.0
log10[observed LC50 (µmol l−1)]
m
−3.5 –2.0 –0.5 1.0 2.0
log10[observed LC50 (µmol l−1)]
a A. brevicaudus
b B. woodhousei
c C. auratus
d C. dipterum
e C. carpio
f D. magna
g D. pulex
h G. affinis
i G. fasciatus
j G. lacustris
k I. punctatus
l L. cyanellus
m L. macrochirus
endosulfan
fenitrothion
fenthion
toxaphene
n M. monoceros
o M. salmoides
p M. saxatilis
q N. undulata
r O. mykiss
s O. mossambicus
t O. latipes
u Peltodytes ssp.
v P. promelas
w P. reticulata
x P. triseriata
y P. californica
Figure 3. (a – c) Observed LC50 values as a function of those predicted using three phylogenetic bilinear models, for pesticides other than that used to build the
models, with regression lines (solid; dotted: 95% confidence limits; dashed: 1 : 1 relationship), and coefficients of prediction (the letters used as markers represent
the different pesticides). Species labels are only shown when predictions deviated from observations by a factor of more than 50.
iterative procedure of model building. The tools for such a procedure could conceivably be implemented within a web-based
interface, with data provided from collaborators worldwide.
Another relevant matter to increase the scope of phylogenetic bilinear LC50 models beyond pesticides is to focus the
efforts on a standard set of species and compounds. Standard
species could be chosen, for instance, on the basis of their representativeness of the taxonomic group to which they belong and
to maximize the number of high-order taxonomic groups that
the models represent. Similarly, standard chemical compounds
can be chosen on the basis of their representativeness of the
chemical group they belong to (e.g. alkanes, ketones, alcohols,
etc.). By increasing sample size, orienting sampling effort to
add more representative species and compounds, and developing better descriptors of chemical activity, it would be possible
to extend the scope of applications of phylogenetic bilinear tolerance models. A potential future application could be to
accurately estimate the toxicity of compounds that have not
yet been synthesized, including designer drugs. We anticipate
that such models would be very useful to the chemical industry
and government regulation authorities by giving scientists an
early warning to avoid or ban harmful compounds and make
the industry focus on more benign alternatives.
(b) Model performance
The developed models explained between 77 and 84% of the
variability in the acute compound toxicity (table 2) and were
therefore in the range of previous models (61–85%) that used
existing empirical data to make predictions [3]. Regarding the
ability of the three models to make predictions, it became
apparent that the local representation of phylogeny was very
important for the final performance. Most outliers in M1
were related to single branches in the phylogenetic tree,
e.g. Morone saxatilis (DDT), A. brevicaudus and Metapenaeus
monoceros (for both malathion and parathion). In M2, the
latter species was removed, while for A. brevicaudus, another
outlier for fenthion was included. For M3 finally, both
M. saxatilis and A. brevicaudus were removed, with the best prediction results, respectively. Hence, to increase the prediction
power for species not included in the model, it is suggested
to have at least one related species in the phylogenetic tree.
Generally, a number of other ecotoxicological models for
single biological endpoints (e.g. acute toxicity for Daphnia
magna or Pimephales promelas) are available, applying physicochemical properties or read-across methodologies to make
quantitative predictions [6,9,10]. Thereby, models predicting
baseline toxicity from kow usually explain more variability
than those for diverse chemical classes using read-across. However, these models are restricted to single species, while the
proportions of variability they explain (e.g. 73–87% for fishes
and 85% for Daphnids) are in the same range as that of the
models presented here (table 2). Hence, owing to the wide taxonomic range of species covered, the models developed in this
paper are particularly useful to identify the taxonomic groups
at highest risk for a certain compound. Given the widespread
distribution of pollution [26], these models might also help
explain why certain species have been lost from certain areas
Proc. R. Soc. B 281: 20133239
2.0
k
–2.0
carbaryl
chlorpyrifos
diazinon
model 3
(c)
7
P2 = 0.48
−3.5
−3.5 –2.0 –0.5 1.0 2.0
log10[observed LC50 (µmol l−1)]
log10[predicted LC50 (µmol l−1)]
model 2
rspb.royalsocietypublishing.org
log10[predicted LC50 (µmol l−1)]
(a)
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(c) Assumptions
(d) Directions
We hope that this study will inspire scientists to pursue the
implementation of phylogenetic models in various fields,
such as ecotoxicology, environmental sciences, pharmacology
and beyond, to other disciplines where it is relevant. We also
hope that it will inspire ecotoxicologists to develop more
powerful tolerance models and make them available to the
people who need them [31].
Acknowledgements. We would like to thank our fellow scientists at Pierre
Legendre’s laboratory and NSERC Hydronet for their useful advice
and fruitful discussion during the elaboration of the manuscript.
Funding statement. G.G. was funded by NSERC grant no. 7738–07 to P.L.,
and S.C.W. was funded by an NSERC PDF and NSERC grant no. 7738–
07 to P.L. The work of P.v.d.O. was supported by the European Commission through the Project HEROIC (FP7–282896; Coordination Action
funded under the 7th Framework Program).
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