175
simplification of the analysis (e.g., carrying out the ENA for
the whole year) might lead to overlook patterns, e.g., cycling
(Bondavalli et al. 2006). The analysis over the seasons is useful for studying temporal dynamics. Consequently, it helps to
disentangle the changes driven by natural variability from
stress, e.g., eutrophication (Bondavalli et al. 2006).
The second recommendation are the software tools for
ENA, NETWRK 4.2 (Ulanowicz and Kay 1991) and Ecopath
with Ecosim (Christensen and Pauly 1992). NETWRK 4.2
runs the ENA and the outputs include the indices and properties described above. It was written for DOS, however, there
are Windows user-friendly versions like EcoNetwrk developed by NOAA Great Lakes Environmental Research Lab
and WAND (Allesina and Bondavalli 2004). Ecopath is
widely used for fishery management and includes intuitive
functions to model incomplete dataset with algorithms that
allow balancing the networks.
A final recommendation focuses on which data should be
used to run the model, not only for ENA but also for
DEB. Authors have used data from the literature and/or
expert opinion only (Christian et al. 2009), but it could represent a limiting factor for the analysis. Although literature
data is a valuable resource it is not possible to find updated
data in many cases, which can alter the accuracy of the models. Therefore, we emphasize that generation of data broads
the potential of the models. Experiments exposing organisms
or even biological communities to environmental gradients
or even testing the synergetic effects of possible stressors
allow us to model the energetic flow and find optimal conditions for targeted organisms or ecosystems. Also the use of
monitoring data is recommended in order to understand how
the species or communities respond to seasonal or annual
variability they go through. The use of experimental and
monitoring data to feed the models enables us to understand
the thresholds of tolerance range (plasticity) and make better
predictions for future climatic changes and possible biological invasions.
Towards Functional Trait Assessment Using
Loop Analysis (LA)
Even though ENA shows great potential for analyzing the
functioning of ecosystems, there are some aspects that are
not covered. The model is restricted to the application of
only one type of currency to represent the interactions. When
we refer to functional traits, the species may be grouped
according to diverse characteristics depending on the function you are looking at. In this subsection, we aim to introduce the application of qualitative analysis as a tool to handle
such complexity. In the same framework, it incorporates
predator-prey, mutualistic and symbiotic relationships, while
at the same time creating connections between human activities and ecosystems (Dee et al. 2017). Qualitative analysis is
able to predict the response of the ecosystems to inputs (disturbances), e.g., biological invasions (Raymond et al. 2011)
and overfishing (Rocchi et al. 2016).
LA is a holistic and qualitative analysis that is based on
positive, negative, and absence of interactions between nodes
(Levins 1974). It allows predicting how the impacts from
perturbations that occur on target nodes may propagate
through the interaction network, thus generating indirect
effects on other nodes of the system. It has been used for
many purposes: from explaining the interactions between
organisms in a food web (Bodini et al. 1994) to modelling
the effects that ecological processes have on society (Martone
et al. 2017). A loop or circuit is defined as a pathway that
crosses the nodes only once and finishes where it started,
creating positive or negative feedbacks (Fig. 2). The pathways and feedbacks are determined based on the interactions
described in the literature (Bodini 2000). For our purpose,
the most interesting part in the analysis is calculating the
sign of the feedbacks, since LA detects the cascade effects of
the inputs on the functioning and predicts whether the nodes
are going to increase, decrease or remain the same under the
impact of different perturbations (Bodini 2000). Levins
(1974) showed that the systems are stable when there are
more negative feedbacks than positive ones. The predictions
generated by LA are displayed in a matrix that presents the
response of all nodes to the positive input of each variable
(Martone et al. 2017; Fig. 2). Software solutions to run these
models are available as pakages in R and GUI versions.
10
The software tools usually provide a matrix and a schematic
figure (see Fig. 2) with the pathways and types of feedbacks
that connect the nodes.
LA has proved to be a useful tool to bring together variables of different kind. Thus, as long as the type of interaction (positive, negative or neutral) is known, it can be a
powerful tool to analyze the effect of functional traits independently on the functions used to define them. In addition,
the traits can be connected to measure the efficiency of various management strategies, ecosystem functioning and services provided to society (Martone et al. 2017).
Conclusions
The functioning of ecosystems is modulated by the
responses of different compartments (e.g., primary producers, herbivores), which determine how species interact.
Thus, the horizontal analysis of single compartments using
DEB models could help to understand the basis of ecosystems functioning. Nevertheless, a more holistic approach
10 The software and additional information can be found here: https://
www.alexisdinno.com/LoopAnalyst/
Biodiversity and the Functioning of Ecosystems in the Age of Global Change: Integrating Knowledge Across Scales
simplification of the analysis (e.g., carrying out the ENA for
the whole year) might lead to overlook patterns, e.g., cycling
(Bondavalli et al. 2006). The analysis over the seasons is useful for studying temporal dynamics. Consequently, it helps to
disentangle the changes driven by natural variability from
stress, e.g., eutrophication (Bondavalli et al. 2006).
The second recommendation are the software tools for
ENA, NETWRK 4.2 (Ulanowicz and Kay 1991) and Ecopath
with Ecosim (Christensen and Pauly 1992). NETWRK 4.2
runs the ENA and the outputs include the indices and properties described above. It was written for DOS, however, there
are Windows user-friendly versions like EcoNetwrk developed by NOAA Great Lakes Environmental Research Lab
and WAND (Allesina and Bondavalli 2004). Ecopath is
widely used for fishery management and includes intuitive
functions to model incomplete dataset with algorithms that
allow balancing the networks.
A final recommendation focuses on which data should be
used to run the model, not only for ENA but also for
DEB. Authors have used data from the literature and/or
expert opinion only (Christian et al. 2009), but it could represent a limiting factor for the analysis. Although literature
data is a valuable resource it is not possible to find updated
data in many cases, which can alter the accuracy of the models. Therefore, we emphasize that generation of data broads
the potential of the models. Experiments exposing organisms
or even biological communities to environmental gradients
or even testing the synergetic effects of possible stressors
allow us to model the energetic flow and find optimal conditions for targeted organisms or ecosystems. Also the use of
monitoring data is recommended in order to understand how
the species or communities respond to seasonal or annual
variability they go through. The use of experimental and
monitoring data to feed the models enables us to understand
the thresholds of tolerance range (plasticity) and make better
predictions for future climatic changes and possible biological invasions.
Towards Functional Trait Assessment Using
Loop Analysis (LA)
Even though ENA shows great potential for analyzing the
functioning of ecosystems, there are some aspects that are
not covered. The model is restricted to the application of
only one type of currency to represent the interactions. When
we refer to functional traits, the species may be grouped
according to diverse characteristics depending on the function you are looking at. In this subsection, we aim to introduce the application of qualitative analysis as a tool to handle
such complexity. In the same framework, it incorporates
predator-prey, mutualistic and symbiotic relationships, while
at the same time creating connections between human activities and ecosystems (Dee et al. 2017). Qualitative analysis is
able to predict the response of the ecosystems to inputs (disturbances), e.g., biological invasions (Raymond et al. 2011)
and overfishing (Rocchi et al. 2016).
LA is a holistic and qualitative analysis that is based on
positive, negative, and absence of interactions between nodes
(Levins 1974). It allows predicting how the impacts from
perturbations that occur on target nodes may propagate
through the interaction network, thus generating indirect
effects on other nodes of the system. It has been used for
many purposes: from explaining the interactions between
organisms in a food web (Bodini et al. 1994) to modelling
the effects that ecological processes have on society (Martone
et al. 2017). A loop or circuit is defined as a pathway that
crosses the nodes only once and finishes where it started,
creating positive or negative feedbacks (Fig. 2). The pathways and feedbacks are determined based on the interactions
described in the literature (Bodini 2000). For our purpose,
the most interesting part in the analysis is calculating the
sign of the feedbacks, since LA detects the cascade effects of
the inputs on the functioning and predicts whether the nodes
are going to increase, decrease or remain the same under the
impact of different perturbations (Bodini 2000). Levins
(1974) showed that the systems are stable when there are
more negative feedbacks than positive ones. The predictions
generated by LA are displayed in a matrix that presents the
response of all nodes to the positive input of each variable
(Martone et al. 2017; Fig. 2). Software solutions to run these
models are available as pakages in R and GUI versions.
10
The software tools usually provide a matrix and a schematic
figure (see Fig. 2) with the pathways and types of feedbacks
that connect the nodes.
LA has proved to be a useful tool to bring together variables of different kind. Thus, as long as the type of interaction (positive, negative or neutral) is known, it can be a
powerful tool to analyze the effect of functional traits independently on the functions used to define them. In addition,
the traits can be connected to measure the efficiency of various management strategies, ecosystem functioning and services provided to society (Martone et al. 2017).
Conclusions
The functioning of ecosystems is modulated by the
responses of different compartments (e.g., primary producers, herbivores), which determine how species interact.
Thus, the horizontal analysis of single compartments using
DEB models could help to understand the basis of ecosystems functioning. Nevertheless, a more holistic approach
10 The software and additional information can be found here: https://
www.alexisdinno.com/LoopAnalyst/
Biodiversity and the Functioning of Ecosystems in the Age of Global Change: Integrating Knowledge Across Scales