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An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models


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Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variables. However, approaches that only consider values of the environmental variables that are likely to be observed in real-world conditions, given the underlying ecological relationships with other variables, have not yet been proposed. Here, a constrained sensitivity analysis procedure is presented, which evaluates the importance of the environmental variables considering only their plausible changes, thereby exploring only ecological meaningful scenarios. To demonstrate the procedure, we applied it to an ANN model predicting fish species richness, as identifying relationships between environmental variables and fish species occurrence in river ecosystems is a recurring topic in freshwater ecology. Results showed that several environmental variables played a less relevant role in driving the model output when that sensitivity analysis allowed them to vary only within an ecologically meaningful range of values, i.e. avoiding values that the model would never handle in its practical applications. By comparing percent changes in MSE between constrained and unconstrained sensitivity analysis, the relative importance of environmental variables was found to be different, with habitat descriptors and urbanization factors that played a more relevant role according to the constrained procedure. The ecologically constrained procedure can be applied to any sensitivity analysis method for ANNs, but obviously it can also be applied to other types of empirical ecological models.
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An ecologically constrained procedure for
sensitivity analysis of Artificial Neural
Networks and other empirical models
Simone FranceschiniID
*, Lorenzo Tancioni
, Massimo LorenzoniID
, Francesco Mattei
Michele Scardi
1Department of Biology, University of Rome Tor Vergata, Rome, Italy, 2Department of Chemistry, Biology
and Biotechnology, Perugia, Italy
Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of
empirical ecological models allows assessing the importance of environmental predictive
variables in affecting species distribution or other target variables. However, approaches
that only consider values of the environmental variables that are likely to be observed in
real-world conditions, given the underlying ecological relationships with other variables,
have not yet been proposed. Here, a constrained sensitivity analysis procedure is pre-
sented, which evaluates the importance of the environmental variables considering only
their plausible changes, thereby exploring only ecological meaningful scenarios. To demon-
strate the procedure, we applied it to an ANN model predicting fish species richness, as
identifying relationships between environmental variables and fish species occurrence in
river ecosystems is a recurring topic in freshwater ecology. Results showed that several
environmental variables played a less relevant role in driving the model output when that
sensitivity analysis allowed them to vary only within an ecologically meaningful range of val-
ues, i.e. avoiding values that the model would never handle in its practical applications. By
comparing percent changes in MSE between constrained and unconstrained sensitivity
analysis, the relative importance of environmental variables was found to be different, with
habitat descriptors and urbanization factors that played a more relevant role according to
the constrained procedure. The ecologically constrained procedure can be applied to any
sensitivity analysis method for ANNs, but obviously it can also be applied to other types of
empirical ecological models.
1. Introduction
Fish assemblage diversity in freshwater ecosystems constitutes a valuable natural resource in
economic, scientific, cultural and educational terms [1]. Its conservation and management
face threats as overexploitation of inland waters, flow modification, water pollution, habitat
degradation and invasion by exotic species [2], [3]. Identifying the relationships between fish
PLOS ONE | January 30, 2019 1 / 15
Citation: Franceschini S, Tancioni L, Lorenzoni M,
Mattei F, Scardi M (2019) An ecologically
constrained procedure for sensitivity analysis of
Artificial Neural Networks and other empirical
models. PLoS ONE 14(1): e0211445. https://doi.
Editor: Thilo Gross, University Of Bristol, UNITED
Received: October 23, 2017
Accepted: January 15, 2019
Published: January 30, 2019
Copyright: ©2019 Franceschini et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
species richness and habitat complexity at a local scale is one of the primary concerns in under-
standing how environmental descriptors actually affect fish biodiversity [4], [5], [6].
In this respect, the ecological variables that can be taken into account are often character-
ized by complex and non-linear dependencies [7]. Ecological models have been increasingly
applied in the management and conservation of freshwater fish communities, especially to pre-
dict spatial patterns of fish occurrence [8], [9]. In particular, Artificial Neural Networks
(ANNs) modeling has proved to be a valuable method in order to assess whether predictable
relationship between environmental descriptors and fish species richness exist in small stream
environments [10], [11], [12].
While in the past ANNs were defined as “black boxes” since the computational processes
taking place inside them are not easy to untangle, at present several methodologies have been
developed to assess the contribution of each variable to the prediction process. For deeper elu-
cidations, Olden et al. [13] provided a comprehensive review and comparison of these
In particular, sensitivity analysis is the term used to define a collection of methods that
evaluate how sensitive model output is to changes in the values of predictive variables [14]. In
ecology, the main sensitivity analysis methods applied to ANNs can be classified into four cate-
gories: (i) the Lek’s profiles method [15], [16]; (ii) the Perturbation method [17], [18]; (iii) the
Partial Derivatives method [19], [20], [21], [22]; (iv) the Weights method, developed by Gar-
son [23] and then implemented by Olden & Jackson [24]. Lek’s profiles study each input vari-
able by keeping all other parameters at fixed values, while in Perturbation method each input
variable is perturbed according to empirically established ranges while all others are kept
untouched. The Partial Derivatives method involves small changes in each input variable and
the evaluation of their relative contribution by computing the partial derivatives of the ANN
output with respect to changes in the input. In the Weights method the connection weights of
the ANN model are partitioned to evaluate the relative importance of each input variable and
its positive or negative contribution to the model output. In the application of the first three
methods, the values assigned to input variables can be devoid of real ecological meaning, i.e.
they can be out of the range that is likely to be observed in real-world conditions. In these
cases, environmental variables are forced to values that are only aimed at evaluating the model
output, with no attention to the actual probability of recording those values given the (fixed)
values of all the other variables. In fact, while of course the above-mentioned methods may
provide valuable information about the way the “black-box” model works, the role of ecologi-
cal relationships in constraining the multidimensional space where meaningful data patterns
exist is not fully taken into account. With regard to the Weights method instead, the estimation
of the input variables importance based on the connection weights may result unbalanced in
certain cases where constrained training procedure may be applied to the ANN model for opti-
mization purposes [25] (NB: in this sentence the term constrained is referred to the training
procedure developed by Scardi [25] and it has nothing to do with the constrained perturbation
of input variables here illustrated).
Therefore, although all those methodologies proved to be means of determining the overall
numerical influence of each predictor variable to the model output, approaches that only con-
sider changes consistent with the ecological relationships among environmental variables have
never been proposed. It is well known in ecology that most environmental variables are far
from independent of each other [26], [27]and therefore not all the combinations of their values
are likely to occur (e.g. river slope tends to increase with elevation, as does the water oxygen
concentration, and cannot be very steep in a floodplain). As these relationships constrain each
variable in the complex multidimensional space that represents the abiotic conditions found in
an ecosystem, some combinations of values are more easily found, while others just cannot
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occur. In fact, for instance, it would be highly unlikely for the maximum width of a stream
channel to occur in a headwaters reach.
These issues raise the question: what is the point of perturbing or fixing variables at values
which are ecologically meaningless? Evaluating the model output response in areas of the mul-
tidimensional space where environmental descriptors take far-fetched values may not be useful
from an ecological perspective. Indeed it would make more sense to evaluate how sensitive
model output is to changes in predictive variables values taking into account only plausible
perturbations, i.e. changes which are consistent with the ecological relationships between envi-
ronmental variables.
This study demonstrates an example of a new type of sensitivity analysis, using a case study
about an ANN model aimed at predicting fish species richness in central Italian rivers. The
goal of this work is to evaluate the real contribution of each predictive variable to species rich-
ness estimates by taking into full account the underlying ecological relationships and con-
straints. This way, all the perturbations applied to predictive variables reflect plausible
environmental conditions, thus evaluating shifts in fish species richness only among ecological
meaningful scenarios.
2. Material and methods
2.1. Study area and data collection
Data have been obtained from 368 sites that have been sampled from 2009 to 2014 in central
Italy [28], [29] (Fig 1). Most rivers in this area are characterized by a Mediterranean climate,
hydrological regimes affected by rainfall variability and strong seasonal discharge variation,
with high flows in spring and fall, and droughts in summer [30].
Fish sampling and environmental data acquisition were carried out according to the official
Italian sampling protocol [31]. It generally consists of electrofishing sampling using a standard
electro-fish shoulder-bag (4KW, 0.3–6 Ampere, 150–600 Volt). All available habitats were
sampled along a stream channel 40–70 m long (the transect length was about 20 times the
width of the wetted channel). Field activities were carried out beyond parks or protected areas.
No endangered or protected species were involved and no specimen were harmed during the
study nor collected. The occurrence of 55 fish species and values for 27 environmental vari-
ables (Table 1) were recorded at each site during sampling activities. Most of these variables
had been already considered in previous studies [9], [32], [33].
Channel width was always less than 20 m, since sample sites were primarily located within
foothills and mountain zones. Thus, sampling methods (electrofishing) was standardized
across sites, where wider river widths would have required nets or other gears.
2.2. Data set processing
All quantitative or semi-quantitative environmental data were normalized in the [0, 1], interval
while qualitative data (e.g. wetlands or islands presence) were coded as binary values (0–1).
Data normalization is a common procedure in ANNs model development [16], [17], since it
transposes the predictive input variables into the data range on which sigmoid activation func-
tions are based, thereby helping to approach to global minima at the error surface. As very
steep slopes were only observed at two sites (13.4% and 23.4% respectively), slope data were
normalized, omitting these two values, relative to third steepest slope value (9%). The maxi-
mum normalized value, i.e. 1, was assigned these outliers after normalization. This solution
was adopted to prevent the compression of the normalized slope values into a very narrow
range because of a couple of cases that cannot be regarded as part of a continuum. Species rich-
ness values were also normalized in the [0, 1] interval.
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The whole data set was divided into three subsets (i.e. training, validation and test). The
training set included 50% of records, while validation and test set included both 25% of rec-
ords. Records were assigned to each subset by sorting all data according to ascending values of
fish species richness and by dividing the resulting ordered sequence into groups of four rec-
ords. Then the first and third record in each group of four records were assigned to the train-
ing set, while the second assigned to the validation test and the fourth to the test set. This
procedure allowed to avoid unbalanced levels of species richness in the three data subsets.
2.3. Artificial neural network modeling
In this study, a three-layered feedforward network with bias has been trained in order to predict
species richness. The optimal number of neurons in the hidden layer was determined by com-
paring the performance of different networks with 1 to 30 hidden neurons. A sigmoid transfer
function was used both for hidden and output layers, thus enabling the network to learn non-
linear relationships between input and output vectors [34]. Mean Square Error (MSE) was com-
puted for the validation set to quantify the goodness of fit of the ANNs during training. The
training procedure was terminated as soon as the MSE stopped decreasing monotonically, thus
preventing the overtraining of the model during the learning process. This approach favors bet-
ter generalization of ANN models while predicting new cases, as previously described in several
ecological papers [25], [26]. Several values of learning rate and momentum (range 0.1–0.5) were
tested to optimize learning performances. ANNs training and testing were performed in R envi-
ronment [35] by using the functions of the package h2o [36].
Fig 1. Sampling sites. Elevation map of the river basins of latium and umbria administrative regions in central Italy.
Black dots mark the position of sampling sites. The image was obtained by using QGIS 2.18 (
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2.4. Constrained sensitivity analysis
In order to use a sensitivity analysis aimed at perturbing environmental predictive variables in
an ecologically sound perspective, the dependencies between all environmental variables were
first investigated.
In particular, for each j
environmental variable, the following steps were performed:
A Euclidean distance matrix was computed between the test set observations taking into
account all the environmental variables but excluding the j
For the i
observation, neighboring observations were selected by taking those within the
first quartile of the (d
) distribution, where d
and d
were respectively the
maximum and the minimum distance between the i
observation and all other
The minimum (j
) and maximum (j
) values of the j
environmental variable were
selected within the neighboring, i.e. most similar, observations. This defined the range of
values that the j
variable can take for the i
The j
variable was perturbed in the [j
] range while all other variables were kept
Table 1. Environmental variables used as input to the ANN model. All environmental data have been obtained according to the official Italian sampling protocol [31].
Variable Label Min Max Mean Median
Slope (%) SLP 0 23.4 1.46 0.83
Channel width (m) CHW 0.8 20 6.04 4
Elevation (m) ELV 0 973 236.61 212.5
Depth (m) DET 0.05 20 0.49 0,35
Runs (area %) RUN 0 100 50.78 50
Pools (area %) POL 0 100 23.99 20
Riffles (area %) RIF 0 100 24.32 15
Wetlands (0/1) WEL 0 1 0.07 0
Bars & islands (0/1) BAS 0 1 0.05 0
Boulders (area %) BOL 0 70 9.86 0
Rocks & pebbles (area %) ROK 0 80 30.38 30
Gravel (area %) GRV 0 90 24.47 20
Sand (area %) SAD 0 80 20.20 20
Silt & clay (area %) SIT 0 100 15.17 0
Velocity (0–5) VEO 0 5 1.89 2
Vegetation cover (area %) VEC 0 90 13.84 10
Shade (area %) SHD 0 90 41.2 40
Anthropic disturbance (0–4) AND 0 4 2.36 2
Upstream barrier (Km, 0–100, 100 if no barrier) UPB 0.01 100 62.41 100
Downstream barrier (0/1) DOB 0 1 0.51 1
Upstream lake (Km, 0–50, 50 if no lake) UPL 0.2 50 45.85 50
Temperature (˚C) TEP 2.56 28.3 14.89 14.65
pH PHP 4.88 9.45 8.04 8.09
Conductivity (mS/cm) COD 229.2 1659 639.02 594.5
O2 (%) O2O 7.31 160 88.76 93.08
Source distance (km) SOD 0.01 233 21.49 11.99
Sampled area (m
) SAA 30 3500 530.35 400
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Five perturbed values in the [j
] range for each predictive variable were then
passed to the data pattern fed to the ANN model, whose output was compared to the tar-
get (i.e. observed) fish species richness.
The same process was iterated for each observation (i.e. for each sampling site in the test
The results of this constrained sensitivity analysis were then compared to those obtained
from simple input perturbation, i.e. by adding white noise in the [-0.5, 0.5] range to each input
variable while keeping all the others untouched.
The method was entirely implemented in R programming language [35]. An example code
is provided in the S1 File.
3. Results and discussion
3.1. Artificial neural network model
The best ANN architecture for predicting fish species richness on the basis of our environmen-
tal predictive variables had 8 hidden neurons and therefore a 27-8-1 structure. It explained a
fairly large share of variance, ranging from R
= 0.771 for the training/validation set to R
0.675 for the test set (Fig 2).
The MSE (obtained from normalized data) varied correspondingly: MSE = 0.00756 for the
training/validation set and MSE = 0.01001 for the test set. It seems that very low observed val-
ues of species richness are hardly reproduced by the model, possibly because the absence of
species that could have been found on the basis of their ecological niche might depend on
other factors (e.g. pressures not described by the available environmental variables) in species-
Fig 2. Predicted vs. observed species richness. Values on axes refer to normalized species richness. The determination coefficient for the ANN model was R
= 0.771
for the training/validation set and R
= 0.675 for the test set.
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poor situations. On the contrary, the highest values in the training set are slightly underesti-
mated, while they match the observed values in the test set. However, the overall agreement
between observed and predicted values is quite good with both data sets and is comparable to
the level obtained in similar cases [12], [37], [38].
The average residuals relative to the normalized training data set as well as those relative to
the normalized test set were very small (0.0017 and 0.0016, respectively), thus showing that the
model was not systematically biased. In fact, when compared to the test set data, model predic-
tions about species richness differed in only one species in 46% of the cases.
Although all the levels of species richness were included in both training and test data set,
the model was less accurate when the highest species richness values were involved. This effect
was most likely related to the difficulty of the ANN in identifying less frequent patterns (those
with high species richness in this case), as already evidenced by Ozesmi et al. [39], thereby
more easily leading to incorrect estimations. In fact, species richness values higher than 11
(normalized value = 0.631) were not frequently found, amounting to less than 5% of the whole
data set.
3.2. Sensitivity analysis
3.2.1. Constrained perturbations. All the methods for analyzing the sensitivity of ANNs
relative to predictive variables are based on the assessment of changes in output values
obtained as a consequence of known changes in input values. The procedure we present here
has been implemented by constraining the random permutation method [17], [18], but its
rationale (i.e. the same constraints) can be applied to any other method [21], [24].
In order to outline the differences between the way input data are perturbed by any uncon-
strained procedure and the way they are by our constrained approach, Fig 3 shows observed
(dark circle) and perturbed (light circle) values for three environmental variables (Slope, Riffles
and Conductivity) in scatter plots against elevation. Elevation is obviously not independent of
some environmental variables and constrains their values according to the procedure outlined
in section 2.4. In particular, in this example, constrained ranges are clearly visible on slope
(positively correlated to elevation) and conductivity (negatively correlated to elevation), while
perturbations of riffles values are very close to the maximum potential range in the ether upper
quartiles of the elevation range, as a consequence of a much looser dependence of this variable
from elevation.
Fig 3. Constrained perturbations for slope, riffles and conductivity vs. elevation values. Perturbed values were obtained by applying the procedure outlined in
section 2.4. The effect of the constraint is more evident for Slope and Conductivity, given their stronger dependence from elevation, than for Riffles, where it only limits
the variability at low Elevation. Both observed (dark dots) and perturbed (lighter dots) values are shown.
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The effect of random perturbations of slope and conductivity (i.e. complete independence
between variables) would have been to fill up all graphs, while points representing perturba-
tions of the two environmental variables occupy only a portion of the two-dimensional space,
thus showing that some combinations of values are very unlikely to be observed. Perturbations
showed in Fig 3 consist only of values that are more likely to be found in real-world conditions,
although their range is large enough to allow assessing their impact on model behavior. Fig 3 is
obviously depicting a very simplified set of relationships (only 3 out of 27 predictive variables).
In practice, however, the same concept was applied to an n-dimensional space, where nis the
number of environmental predictive variables used for the model development, thus defining
an n-dimensional envelope that constrains the random perturbation of each environmental
variable, excluding very unlikely patterns (e.g. very steep slope at very low elevation) from the
sensitivity analysis.
3.2.2. MSE percentages differences. The percent increase in MSE obtained by con-
strained perturbation of each variable for the test set is shown in Fig 4 versus the percent
increase obtained by unconstrained perturbation. Unconstrained perturbations obviously
induce larger increases in MSE, as they modify known data patterns to a larger extent.
Although ANNs may respond to changes in a single input variable in a non-monotonic way,
thus potentially making a large change in an input value less influential than a smaller one, in
practice larger changes in input variables are clearly associated with larger increases in MSE.
However, very large increases in MSE obtained from data patterns that are unlikely to occur in
practical applications of the model are not useful–and possibly misleading–when it comes to
the very purpose of sensitivity analysis, i.e. at inferring the role each input variable plays rela-
tive to the target variable.
While all the input variables are more sensitive to unconstrained perturbations, some show
negligible differences between the two perturbation strategies, while others exhibit sharp dif-
ferences. According to changes in MSE, the input variables that showed largest differences
between the two perturbation methods were Slope (101.1% and 17.1%, for unconstrained and
constrained perturbations, respectively), pH (57.8%; 9.3%), Source distance (52.7%; 14.5%)
and Sampled area (38.5%; 10.1%).
Variables whose perturbations affected the model to a very limited extent (less than 10%
increase in MSE), i.e. those in the lower left corner of Fig 4, do not deserve any further com-
ment, because they certainly play a less important role. Other variables, however, are associ-
ated with changes in MSE between 10% and 30% and their constrained perturbation in some
cases (e.g. Conductivity, Pools and Anthropic disturbance) induces changes in MSE almost as
large as unconstrained and even more than the constrained perturbation of the “most influen-
tial” unconstrained (Slope, pH, Source distance and Sampled area).
In ecology, it is well known that fish species composition in lotic ecosystems tends to follow
a typical longitudinal pattern [4] (i.e. differences in fish guilds occurrences and abundances)
and generally fish species richness generally tends to increase with the distance from the river
source [40]. Of course, there are field conditions that can be regarded as exceptions to this gen-
eral trend. In fact, habitat features [41], [42], hydrological factors [43] or urbanization [44]
may highly affect fish species diversity. It is clear that environmental variables like slope, pH or
distance from source may provide information about the riverine trait where a site to be mod-
eled is located (e.g. mountain or hilly region) [45], thereby providing valuable input informa-
tion to the ANN model about expected species richness and inducing large changes in MSE
when their values are perturbed. However, unconstrained perturbations, especially with those
variables, may result in combinations of values, e.g. a steep slope too far from the source, that
are unlikely or even impossible to occur in real-world situations, but that could trigger large
changes in MSE.
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Sensitivity analysis based on unconstrained perturbations can be deeply affected by this
problem and the reason is that any model (and ANNs are no exception) is fitted to known data
patterns, which obviously include only the combination of input values that actually occur in
real-world situations. Extreme values may occur, but only in combination with a narrow range
of values for other variables. Moreover, environmental variables are often strongly correlated
with each other and their correlations make the range of ecologically meaningful variation in
their values even narrower. For instance, pH usually decreases as the distance from river
source increases, while conductivity increases [46]. These relationships make perturbations
for Slope, pH, Sampled area, Source distance and Elevation strictly related to the ecological
context, thereby defining a narrower, but more realistic range of values that can be safely
used in practical applications of the model. Therefore, the MSE increase associated to large
Fig 4. Percent increase in MSE obtained by constrained vs. unconstrained perturbations. Constrained sensitivity analysis clearly
reduces maximum perturbations for the environmental variables, thus resulting in smaller increases in MSE for all of them (all points
are below the unit slope line). However, the effect of the constraint is larger for some variables (e.g. Slope, SLP; pH, PHP; Source
distance, SOD; Sampled area, SAA). See Table 1 for the names of environmental variables corresponding to other point labels.
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perturbations of these variables has very little importance relative to real world applications of
the model.
3.2.3. Importance of the environmental variables. Changes in MSE after perturbation of
each environmental variable were sorted in decreasing order after the application of a conven-
tional scheme for sensitivity analysis and after the application of the constrained procedure.
The outcome relative to the unconstrained procedure can be regarded as a different and sim-
plified view relative to Fig 4. In fact, the bar diagram in Fig 5, just shows the increase in MSE
caused by the perturbation of each variable. On the left after unconstrained perturbation and
on the right after constrained perturbation. MSE% scales show the percent increase in MSE
and are different in the two cases, as constrained perturbation cannot induce a level of increase
in MSE as large as that induced by unconstrained perturbations.
In fact, all variables were obviously associated with smaller changes in MSE when the con-
strained procedure for sensitivity analysis was applied and the largest differences in the rank of
variable importance occurred for Slope, Conductivity, pH, Sampled area, Pools and Anthropic
disturbance, while less important environmental variables showed only minor shifts in their
relative importance. pH was one of the most important variables according to the conventional
Fig 5. Unconstrained and constrained sensitivity analysis compared. Bars show the percent increase in MSE caused by
the perturbation of each variable. Black bars (left) are for unconstrained perturbations while blue bars (right) are for
constrained ones. Environmental variables are ordered according to the rank of their importance in the unconstrained
sensitivity analysis. As the increase in percent MSE was smaller in constrained sensitivity analysis, the MSE axis was scaled
accordingly to better show the relative length of the bars.
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procedure of sensitivity analysis based on unconstrained variable perturbation, but it only
ranked eighth in sensitivity analysis based on constrained perturbations. Similar downgrades
in importance were also observed for Slope and Sampled area. They are not surprising, as they
occurred because of the narrower range of perturbed values these variables can assume under
the constrained procedure for sensitivity analysis. In fact, this procedure takes only into
account an amount of variability that is consistent with the observed relationships between
variables and with the environmental context of each data pattern. As a consequence, environ-
mental variables that had an intermediate relative importance according to the unconstrained
procedure (e.g. Conductivity, Pools and Anthropic disturbance), gained a more relevant role
as potential drivers of the local fish species richness.
While this result cannot be formally validated, as the true relative importance of the envi-
ronmental variables is obviously unknown, it demonstrated an important feature of the con-
strained sensitivity analysis. The unconstrained procedure suggested a ranking of variables
importance that showed what made the ANN model learn to recognize the riverine trait where
sampling sites are located, thus obtaining estimates for fish species richness. However, species
richness was also affected by variables that convey information about some relevant local con-
ditions, like habitat features, hydrologic factors or urbanization. As a matter of fact, several
studies evidenced that, at local scale, urbanization and/or flow regulation may strongly modify
the expected fish species richness [40], [47]. Results obtained from the constrained sensitivity
analysis showed indeed how, at any given site, fish species diversity is highly affected by envi-
ronmental factors as habitat descriptors (e.g. Pools; Bars & islands) and anthropic disturbance
(Conductivity; Anthropic disturbance). As conductivity can be considered as an indirect mea-
sure of water pollution [48], [49] and anthropic disturbance in most cases is related to urbani-
zation, it is reasonable that they had a strong impact on fish assemblage diversity and
In this work we focused on the estimation of variables importance taking into account first-
order effects, as one input variable at a time was perturbed, while all other variables were kept
untouched. Estimating the model output response to two-way [22] or more complex interac-
tions between variables is certainly feasible in a constrained sensitivity analysis, but the prob-
lems related to the complexity of the procedure remain unsolved, making the analysis of
higher order interactions between predictive variables practical only when their number is
very small.
A very common goal in ANN modeling is the reduction of the number of input variables.
The reason for that reduction is twofold: it might reduce the cost of predictive information
and it might help to fight the curse of dimensionality [50]. The first problem depends on the
way predictive information is collected: if all predictive data are already available, or if they are
collected with no additional costs, e.g. during the same field activities, then the overall cost of
predictive information will not be affected. The second problem is strictly related to the ratio
between the number of available records and the number of input variables. According to
Theodoridis & Koutroumbas [51], acceptable values for that ratio are in the 2 to 10 range, with
smaller values that might result in a reduced prediction ability of the model.
As our data set was already available and all the predictive variables are routinely included
in monitoring activities, no reduction in the cost of information could be achieved. Moreover,
the number of available records (N = 368) is quite large relative to the number of predictive
variables (p = 27) and therefore the ratio between the two (N/p = 13.63) is even larger than the
upper limit of the above-mentioned range. Therefore, reducing the number of input variables
was not needed, while preserving the full set allowed testing the constrained sensitivity analysis
on a wider spectrum of variables. Moreover, preserving the full set of input variables allowed
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to exploit all the potential high-order relationships between variables that a trained ANN is
able to capture and embed in its synaptic weights.
However, selecting the most important variables on the basis of a sensitivity analysis can be
needed in data-limited scenarios and therefore we checked the effects of a reduced set of input
variables, selected through a constrained sensitivity analysis, on the performance of the result-
ing ANN model. A subset of input variables was selected, including only those whose con-
strained perturbation induced increases in MSE larger than 10% (Fig 5), i.e. conductivity,
pools, slope, anthropic disturbance, source distance and sampled area. Then a new ANN
model with a 6-4-1 structure was trained and the determination coefficient for the test set was
= 0.44. Even if model accuracy in predicting fish species richness values considerably
decreased, the variance explained by the model using the selected variables was still acceptable,
especially in the light of the exclusion of 21 variables out of 27.
As far as we know, problems related to the scaling of ANN input variables (e.g. because of
heterogeneity in their units) have been already tackled [52], [53], but methods aimed at defin-
ing to what an extent normalized input variable can be perturbed or changed in a sensitivity
analysis, while preserving reasonable quantitative relationships with each other have never
been implemented. From an ecological point of view, the method we propose showed what
environmental variables, in real-world conditions (i.e. with values that vary within a realistic
range) may actually induce changes in fish species richness. Looking at the results from a con-
servation perspective, assigning the highest degree of importance to variables that are very
unlikely to change at local scale (e.g. slope) would be meaningless, while considering as more
influential variables that may have a real impact on the fish assemblage richness, such as the
level of water pollution or alterations of river traits due to urbanization [54], [55] is certainly
more appropriate.
4. Conclusions
While several methods are available to test the sensitivity of ANNs or of any other type of
model, we based our analysis on the perturbation method, because it is the one that most
closely matches the rationale of the procedure we propose. However, the same rationale may
be adapted to any other method (e.g. Partial Derivatives or Lek’s profiles method), as its only
goal is to avoid data patterns that are not likely to occur in real-world conditions and that
therefore are not really useful to open the ANN “black-box” as well as any other type of empiri-
cal model and to elucidate the way it worked and the ecological relationships it captured.
Of course, it was not possible to validate the approach we proposed by means of statistical
analyses or by any other method. However, it showed that variables that influence fish species
richness according to a procedure that takes into account only combinations of values that are
likely to occur in real-world situations are not the same that would have been selected accord-
ing to a procedure that does not take the ecological relationships between environmental vari-
ables into due account. Thus, our constrained approach to sensitivity analysis can be regarded
as more realistic way to look into the model behavior, focusing on a meaningful subset of the
multidimensional space in which the model can be theoretically applied. In fact, investigating
how a model behaves in a region of its potential input space that will never be used in practical
applications seems definitely pointless.
Needless to say, the procedure we proposed is only aimed at demonstrating a concept and
therefore further developments can be imagined in its future applications, particularly as
regards the selection of the number of neighboring observations or the maximum distance to
them, thus investigating the effect of different levels of constrained perturbations and their
effects in the resulting ranking of environmental variables importance.
An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks
PLOS ONE | January 30, 2019 12 / 15
Supporting information
S1 File. Constrained sensitivity analysis algorithm. Here, the R code algorithm of the con-
strained sensitivity analysis is provided.
S2 File. Data set. Data set used for the Artificial Neural Network modeling. All values were
normalized as described in the Material and Methods section.
Author Contributions
Conceptualization: Lorenzo Tancioni, Michele Scardi.
Data curation: Simone Franceschini, Lorenzo Tancioni, Massimo Lorenzoni, Francesco
Formal analysis: Simone Franceschini, Michele Scardi.
Investigation: Simone Franceschini.
Methodology: Simone Franceschini, Michele Scardi.
Project administration: Michele Scardi.
Supervision: Lorenzo Tancioni, Michele Scardi.
Validation: Simone Franceschini.
Visualization: Simone Franceschini.
Writing – original draft: Simone Franceschini.
Writing – review & editing: Simone Franceschini, Michele Scardi.
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An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks
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Classical sensitivity analysis of machine learning regression models is a topic sparse in literature. Most of data-driven models are complex black boxes with limited potential of extracting mathematical understanding of underlying model self-arranged through the training algorithm. Sensitivity analysis can uncover erratic behaviour stemming from overfitting or insufficient size of the training dataset. It can also guide model evaluation and application. In this paper, our work on data-driven sensitivity analysis of complex machine learning models is presented. Rooted in one-at-a-time method it utilizes training, validation and testing datasets to cover the hyperspace of potential inputs. The method is highly scalable, it allows for sensitivity analysis of individual as well as groups of inputs. The method is not computationally expensive, scaling linearly both with the available data samples, and in relation to the quantity of inputs and outputs. Coupled with the fact that calculations are considered embarrassingly parallel, it makes the method attractive for big models. In the case study, a regression model to predict inclinations using recurrent neural network was employed to illustrate our proposed sensitivity analysis method and results.
Enhancing the understanding of marine phytoplankton primary production is paramount due to the relationships with oceanic food webs, energy fluxes, carbon cycle and Earth's climate. As field measurements of this process are both expensive and time consuming, indirect approaches, which can estimate primary production from remotely sensed imagery, are the only viable large-scale solution. We boosted the quality of phytoplankton primary production estimates, with respect to a previously developed model, by embedding ecological knowledge into the training of an artificial neural network. In order to achieve this goal, we drove the training procedure on the basis of both theoretical and data-derived ecological knowledge about phytoplankton primary production. A “single peak” constraint exploits the theoretical knowledge about the vertical shape of the production profile; a “depth-weighted error” procedure was based on available information about the production magnitude along the water column; a variable learning rate and momentum approach allowed to better exploit the available data to train our artificial neural network. Thanks to this customized procedure, we improved the quality of the primary production estimates from both a theoretical and a numerical point of view. Accordingly, the new artificial neural networks not only provided ecologically sounder estimates, but also explained up to 4% more variance with respect to the traditional error back-propagation solution. This result was achieved exclusively through an ecologically-driven customization of the basic algorithm, since the dataset and predictive variables were the same utilized in the conventional counterpart, trained with a classic error back-propagation algorithm. We suggest that the proposed rationale could lead to improved performances in similar modelling applications.
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Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artificial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fish species occurrence estimates and a case study for Leucos aula in NE Italy is presented as a demonstration case. Potentially useful biotic information (i.e. occurrence of other species) was selected by means of tetrachoric correlation analysis and on the basis of the improvements it allowed to obtain relative to models based on environmental variables only. The prediction accuracy of the L. aula model based on environmental variables only was improved by the addition of occurrence data for A. arborella and S. erythrophthalmus. While biotic information was needed to train the ANNs, the final cascaded ANN model was able to predict L. aula better than a conventional ANN using environmental variables only as inputs. Results highlighted that biotic information provided by occurrence estimates for non-target species whose distribution can be more easily and accurately modeled may play a very useful role, providing additional predictive variables to target species distribution models.
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Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes–recommended in a related study and that includes a measure of relative position, slope, two measures of orientation , topographic mean and a measure of rugosity–yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at and search on "Theodoridis" to access resources for instructor. Published: December 2010.
Biological assessment of flowing water has undergone a conceptual change from the use of biological indicators of water quality to the assessment of 'biological quality' (Wright et al. 2000) or 'biological integrity' (Karr 1991). This change from biological indicators to the assessment of biological integrity marks a shift towards ecosystem level evaluation and these approaches are often referred to as measures of 'ecosystem health' (Chessman et al. 1999). These new concepts come from a recognition that water quality is the result of many factors including biological interactions, flow regimes and habitat structure and that these are all dependant on modification by human activities (Karr 1991, 1995). It follows from this movement to an ecosystem approach that impacts on the biological condition of rivers exposed to human impacts can be judged by comparing the river biota with that from relatively unimpacted reference ecosystems. The prerequisite is that the sites occur in similar geomorphological and climatic settings referred to as reference conditions (Chessman et al. 1999). Thus, we can assess the condition of human disturbed sites by measuring their structural attributes and then comparing them with relevant reference conditions that are pristine or, at worst, relatively undisturbed (Hughes et al. 1986). This reference site method of river assessment has been in use for some time although different approaches have been used in different parts of the world. In the USA, a reference site approach has been applied with 'indices of biotic integrity' (e.g. Karr 1981). Whereas in the United Kingdom and more recently Australia, multivariate reference site predictive models have been used (Simpson and Norris 2000). The predictive reference condition approach (RIVPACS: River InVertebrate Prediction And Classification System) and its derivatives developed originally by Wright et al. (1984) and later advanced by Reynoldson et al. (1995), Simpson and Norris (2000) and Hawkins et al. (2000b) have been successfully applied to streams worldwide. The output from the models is a list of taxa expected to be at a site in the absence of human impacts predicted from a suite of environmental variables. The predictions come from a database of least impacted sites selected to cover all stream types within a region. In the bioassessment of a site, the final output is a measure of the relationship between the fauna collected at a site and that predicted. That is the observed number of taxa (O) is compared to the number expected (E) in the absence of stress as a measure of departure from expected conditions, and is thus, a measure of biological impairment. Although the predictive models described above have been developed using macroinvertebrates they have the potential for use with other biotic groups (Reynoldson and Wright 2000) and have been developed for use with fish (Joy and Death 2002), diatoms (Chessman et al. 1999) and stream habitat features (Davies et al. 2000). New Zealand has a limited native fish and macro-crustacean fauna of 39 recognised species, dominated by Galaxiidae and Eleotridae as well as a non-migratory crayfish and a diadromous shrimp. The fauna is characterised by a high proportion of diadromous species such that there are marked longitudinal trajectories of fish distribution with species richness reducing with elevation (Joy et al. 2000). These distributional patterns negate the application of bioassessment methods using fish employed elsewhere in the world such as the index of biotic integrity (IBI). These index approaches would be problematical in New Zealand because they rely on relationships between community metrics and habitat quality and would not account for the overriding longitudinal distributional patterns caused by diadromy (McDowall and Taylor 2000). Migratory fish and crustacea can however be used in bioassessment if the method used takes into account their longitudinal distribution patterns. A predictive modelling approach using reference sites allows for the migration driven distributional patterns to be incorporated in the bioassessment of fish and macrocrustaceans (Joy and Death 2002). We took a reference site predictive modelling approach to the use of fish and macrocrustaceans in the assessment of biological quality in the rivers of Auckland, New Zealand. To achieve this we modeled the presence or absence of 10 fish and 2 macro-crustacean species using individual artificial neural network models, one for each taxon based on environmental variables. To make the predictions from the model independent of human impacts we used only environmental variables unlikely to be influenced by human impacts and used only data from minimally disturbed reference sites. The predictions from these models were combined to predict the fish and macro-crustacean assemblage to be expected at sites.