DISCUSSION
Multispecies models have huge potential as tools for understanding and
predicting the dynamics of interacting species, and helping to
disentangle the effects of these interactions from other processes
affecting population dynamics, such as climate change, habitat changes
and harvesting (Kinzey & Punt 2009; Péron & Koons 2012; Swallowet al. 2016). Rapid progress is made towards fulfilling this
potential, but important challenges remain. Currently, the greatest
limitations to multispecies model development are data availability and
model complexity, leading to difficulties in parameterization and large
uncertainty.
Data availability is improving (McCallen et al. 2019), but data
collection will always be burdened by logistical limitations. It is
therefore important that the right types of data are collected to
maximize their use for multispecies modelling, for instance by ensuring
that data collection gives attention to species interactions and data
are collected for multiple cohabiting species simultaneously (Trijouletet al. 2019). Still, collecting data on all components and
processes is virtually impossible in most ecosystems. Therefore, it is
important to consider grounded ecological knowledge about a system
before making decisions regarding the community components that are
sampled or omitted during data collection. This may be achieved by, for
example, identifying community modules and impossible or missing links
(Dormann & Strauss 2014; Terry & Lewis 2020). Additionally, prior
assumptions about our knowledge of community functioning must be
challenged, given that ecosystems are ever-changing and discrepancies
between experts’ knowledge can have important impacts on the models
(Picard et al. 2012; Terry & Lewis 2020). Evaluations of ongoing
multispecies monitoring programs can help identify weaknesses in the
data collection (e.g., poor sampling of a particular species or region),
which, when addressed, could greatly benefit multispecies model
performance (Carvalho et al. 2016; Zhang et al. 2020). In
addition, it would be extremely beneficial to foster collaborations
among researchers and improve organization of data collection. By
increasing collaborations, researchers working on different species
could coordinate sampling efforts in the same region, thereby producing
data more useful to multispecies modelling without incurring additional
costs. Finally, development of methods that promote more efficient use
of limited data, such as data integration, have an important role to
play in multispecies modelling development.
Model complexity is another major challenge for the development of
multispecies models. Attempting to capture the inherent complexity of
natural ecosystems in mathematical models causes issues related to
computational and data requirements, model parameterization, number of
potential error sources, and interpretability and transferability of
model outputs. Assumptions and simplifications will continue to be
necessary. For example, multispecies population dynamics models need to
rely on the assumption that the subset of species and processes included
in the models are sufficient to describe the dynamics of interest, and
simplified community models must rely on the criteria chosen to group
certain species or simplify species interactions. It is crucial that
such simplifications are grounded on robust ecological knowledge, rather
than on data limitations (Lafferty et al. 2008), and that they
are transparently reported so that future studies can compare and assess
the effects of different simplifying strategies on model outputs (Fultonet al. 2003).
The lack of adequate quantification and reporting of uncertainty
currently represents a major challenge for the development of
multispecies models. Uncertainty is inevitable in ecological modelling,
and it is often accentuated in increasingly complex models. In
multispecies models, uncertainty comes associated with many sources,
which makes the quantification and propagation of individual sources of
uncertainty more difficult. There is a great need for methods to assess
uncertainty consistently so that models can be compared and evaluated.
Although uncertainty is often viewed as a negative model characteristic
with regard to practical applications (Pappenberger & Beven 2006), a
model with unknown uncertainty is certainly less useful than a model
with high, but reported, uncertainty (Keenan et al. 2011). In
some cases, high uncertainty in multispecies models can be a positive
outcome if it means that the model accounts for important external
processes affecting the populations rather than regarding these as
random variation, as single-species models would do (Hollowed et
al. 2000; Kinzey & Punt 2009).
Ultimately, a promising way forward that maximizes data use is the
combination of modelling approaches with contrasting strengths and
weaknesses (Strauss et al. 2017). For example, Schmolke et al.
(2019) built a hybrid model consisting of a food web simulation model
for an aquatic system coupled with an IBM for a single species of
interest (Fig. 3). For each time step, the food web model provided
biomass estimates for different parts of the food web, based on
environmental variables, estimates of vital rates, and expected
interactions. Estimates of prey biomass from this model were then fed
into the IBM, which modelled in detail the transfer of prey biomass to
individuals of the focal species, and the resulting biomass estimate for
the focal species was fed back into the food web model. While this model
did not include stochastic effects and focused on a single species, the
general approach of coupling more detailed population models with a
system-wide community model could represent a promising method for
incorporating species interaction effects into predictive models of
population dynamics (Ernest et al. 2011). Other types of hybrid
modelling frameworks have also been proposed, for example, by linking a
spatially explicit dynamic model of a plant community to an IBM
describing the behaviour of frugivorous birds to account for the effect
of the birds on seed dispersal (Vincenot et al. 2011). As
hybridizing models is a relatively new approach within ecology, and
their development requires a diverse range of expertise, their potential
and benefits remain largely unexplored, representing an exciting path
moving forward (Kim et al. 2019).
In this review, we have identified common themes in relation to the
challenges faced by different subfields of ecology aiming to model
multispecies dynamics. Working together on shared challenges across
subfields should promote faster and more fruitful progress towards a
better understanding of multispecies dynamics. It has become customary
for modelling studies to compile their developed models as readily
available open-source software and code, which will undoubtedly promote
development (Powers & Hampton 2019; Alston & Rick 2021). Moving
forward, we emphasize that it is important that multispecies studies
clearly outline the model objectives and assumptions. We also encourage
increased communication across multispecies modelling disciplines, and
hope that this leads to the development of new hybrid frameworks that
successfully combine models with contrasting strengths and weaknesses,
representing cost-effective ways to advance our understanding of
multispecies dynamics (Gray & Wotherspoon 2015; Strauss et al.2017).
Acknowledgements
This study was funded by the Research Council of Norway through the
Centre of Excellence Centre for Biodiversity Dynamics (project 223257)
and research project SUSTAIN (244647). MVH was supported by the
Norwegian Research Council (280715).
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Captions
Figure 1. Diagram illustrating four methods for reducing complexity of
natural systems. Centre figure shows a hypothetical system with the true
interactions and variables. Functional groups/trophospecies illustrates
the idea of grouping species by trophic/functional similarity and
treating each group as one component. Modules represent sub-groups of
mostly mutually interacting species which can be modelled independently.
Modules can also be linked to each other. General functions can define
interactions between individuals based on traits (e.g., size), rather
than between species. Latent variables are used to represent the
variability caused by unknown factors (e.g., species or environmental
variables).
Figure 2. Representation of potential sources of uncertainty. The
thick/grey arrows on top illustrate the propagation of uncertainty
through the modelling process. The red symbols highlight example causes
for uncertainty, such as: (a) interactions taking place at night
(represented by the moon symbol) or in difficult to observe/monitor
places, (b) incorrect assumptions about the presence or absence of
species interactions and, (c) uncertainty in estimated parameters.
Lastly, model uncertainty can be reduced through increasing
transparency.
Figure 3. Example of hybrid model that combines a food web model (brown)
and individual based model (IBM; blue) to represent a focal species
(S3) in more detail. For each model iteration, values of
the food web model components influence responses of individuals in the
IBM, like movement, metabolism, excretions, fecundity, growth or
behaviour. In turn, total changes in the population of the focal species
(S3) produced by the IBM feed back into the food web
model, affecting the species that interact with the focal species in the
following iteration (i.e., S1 and S5).
Figures