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index.qmd

@@ -69,11 +69,11 @@ Although this may seem an elementary question in the context of food webs --- a
 
 ## What is captured by an edge?
 
-At its core, links within food webs can be thought of as a representation of either feeding links between species (be that realised or potential [@dunneNetworkStructureFood2006; @pringleUntanglingFoodWebs2020]), or fluxes within a system *e.g.,* energy transfer or material flow as the result of the feeding links between species [@lindemanTrophicDynamicAspectEcology1942]. These correspond with different 'currencies' (the feasibility of links or the energy that is moving between nodes). How these links are specified will influence the resulting structure of the network. For example, taking a food web that consists of links representing all *potential* feeding links for a community will be meaningless if one is interested in understanding the flow of energy through the network as the links are not environmentally/energetically constrained. In addition to the various ways of defining the links between species pairs there are also a myriad of ways in which the links themselves can be quantified. Links between species are often treated as being present or absent (*i.e.,* binary) but it is also possible to use probabilities [which quantifies how likely an interaction is to occur, @poisotStructureProbabilisticNetworks2016; @banvilleDecipheringProbabilisticSpecies2024] or continuous measurements [which quantifies the strength of of an interaction, @berlowInteractionStrengthsFood2004].
+At its core, links within food webs can be thought of as a representation of either feeding links between species (be that realised or potential [@dunneNetworkStructureFood2006; @pringleUntanglingFoodWebs2020]), or fluxes within a system *e.g.,* energy transfer or material flow as the result of the feeding links between species [@lindemanTrophicDynamicAspectEcology1942]. These correspond with different 'currencies' (the feasibility of links, or the energy that is moving between nodes). There is also a myriad of ways in which the links themselves can be specified. Links between species can be treated present or absent (*i.e.,* binary), may be defined a, or by continuous functions which further quantify the strength of an interaction [@berlowInteractionStrengthsFood2004]. How these links are specified will influence the resulting structure of the network. For example, taking a food web that consists of links representing all *potential* feeding links for a community will be meaningless if one is interested in understanding the flow of energy through the network as the links are not environmentally/energetically constrained.
 
 ## Network representations {#sec-representation}
 
-Networks can be thought of to fall into two different 'types'; namely metawebs; traditionally defined as all of the *potential* interactions for a specific species pool [@dunneNetworkStructureFood2006], and realised networks; which is the subset of interactions in a metaweb that are *realised* for a specific community at a given time and place. The fundamental differences between these two network representations are the spatial scale at which they are constructed, and the associated processes that are assumed to drive pattern at these scales.
+Networks can be thought of to fall into two different 'types': namely metawebs; traditionally defined as all of the *potential* interactions for a specific species pool [@dunneNetworkStructureFood2006], and realised networks; which is the subset of interactions in a metaweb that are *realised* for a specific community at a given time and place. The fundamental differences between these two network representations are the spatial scale at which they are constructed, and the associated processes that are assumed to drive pattern at these scales.
 
 A metaweb provides insight as to the viability (feasibility) of an interaction between two species occurring, and captures some measure of the viability/feasibility of an interaction occurring between two species based on 1) the complementarity of their traits (a *global metaweb*) and 2) can be further refined by their co-occurrence (a *regional metaweb*). Metawebs thus provide a means to identify links that are not ecologically plausible, *i.e.,* forbidden links [@jordanoSamplingNetworksEcological2016], or provide an idea of the *complete* diet of a species [@strydomGraphEmbeddingTransfer2023].
 
@@ -87,7 +87,7 @@ In the previous section we discussed how the nuances in defining a network (in t
 
 The five core constraints we propose are evolutionary compatibility, co-occurrence, abundance, diet choice, and non-trophic interactions. In the following sections, we present details about how the constraints are defined, the scale at which they operate and how they deliver a network.
 
-![Aligning the various processes that determine interactions (right column) with the different network representations (left column). First we start with a **global metaweb** this network captures all possible interactions for a collection of species in the global context. However within the global environment different species occur in different regions (region one = yellow and region 2 = orange), and it is possible to construct two different metawebs (**regional metawebs**) for each region by taking accounting for the co-occurrence patterns of the difference species - as shown here we have two regions with some species (blue) that are found in both regions and others endemic to either region one (yellow) or region two (orange). However even within a region we do not expect that all interactions to be realised but rather that there are multiple configurations of the regional metaweb over both space and time. The 'state' of the different **realised networks** are ultimately influenced not just by the co-occurrence of a species pair but rather the larger community context such as the abundance of different species, maximisation of energy gain, or indirect/higher order interactions.](images/anatomy.png){#fig-process}
+![Aligning the various processes that determine interactions (right column) with the different network representations (left column). First, we start with a **global metaweb** this network captures all possible interactions for a collection of species in the global context. However, within the global environment different species occur in different regions (region one = yellow and region 2 = orange), and it is possible to construct two different metawebs (**regional metawebs**) for each region by taking accounting for the co-occurrence patterns of the difference species - as shown here we have two regions with some species (blue) that are found in both regions and others endemic to either region one (yellow) or region two (orange). However even within a region we do not expect that all interactions to be realised but rather that there are multiple configurations of the regional metaweb over both space and time. The 'state' of the different **realised networks** is ultimately influenced not just by the co-occurrence of a species pair but rather the larger community context such as the abundance of different species, maximisation of energy gain, or indirect/higher order interactions.](images/anatomy.png){#fig-process}
 
 ## Processes that determine the feasibility of an interaction {#sec-process-feasibility}
 
@@ -143,13 +143,13 @@ Practically there is little support that networks are truly stochastic, however
 
 ## Feasibility networks (metawebs)
 
-Metawebs (depending on the aggregation) can help us develop our understanding of the intersection of species interactions and their co-occurrence[@soberonGrinnellianEltonianNiches2007; @gravelBringingEltonGrinnell2019]. Whereby a *global metaweb* presents an approximation of the fundamental Eltonian niche of a species (*i.e.,* its relation to its food source), whereas as *regional metawebs* represent an intersection of Elton and Grinnell. As discussed in @sec-process-feasibility the feasibility of an interaction is typically assessed on a pairwise basis, and is often assessed based on the idea that interactions are governed by a set of 'feeding rules' [@morales-castillaInferringBioticInteractions2015], and are broadly elucidated in two different ways; *mechanistic models*, [*e.g.,* @shawFrameworkReconstructingAncient2024; @dunneCompilationNetworkAnalyses2008; @roopnarineEcologicalModellingPaleocommunity2017] and *pattern finding models* [*e.g.,* @strydomGraphEmbeddingTransfer2023; @pichlerMachineLearningAlgorithms2020; @strydomFoodWebReconstruction2022; @caronAddressingEltonianShortfall2022; @llewelynPredictingPredatorPrey2023; @desjardins-proulxEcologicalInteractionsNetflix2017; @eklofSecondaryExtinctionsFood2013; @cirtwillQuantitativeFrameworkInvestigating2019]. The fundamental difference between these two model groups is that *mechanistic models* rely on expert knowledge and make explicit assumptions on trait-feeding relationships, whereas the *pattern finding models* are dependent on existing interaction datasets from feeding rules can be elucidated. It perhaps also bears repeating that these models are often only presenting a list of feasible interactions and that the rresulting netowrk is 'unstructured', as it is uconstrained by any processes or conditions that generate structure. While these networks can be imprinted with external definitions of trophic position and guild identity to deliver hypothetical structure, this structure is not an emergent property of the links and species pairs [@caronTraitmatchingModelsPredict2024].
+Metawebs (depending on the aggregation) can help us develop our understanding of the intersection of species interactions and their co-occurrence[@soberonGrinnellianEltonianNiches2007; @gravelBringingEltonGrinnell2019]. Whereby a *global metaweb* presents an approximation of the fundamental Eltonian niche of a species (*i.e.,* its relation to its food source), whereas as *regional metawebs* represent an intersection of Elton and Grinnell. As discussed in @sec-process-feasibility the feasibility of an interaction is typically assessed on a pairwise basis, and is often assessed based on the idea that interactions are governed by a set of 'feeding rules' [@morales-castillaInferringBioticInteractions2015], and are broadly elucidated in two different ways; *mechanistic models*, [*e.g.,* @shawFrameworkReconstructingAncient2024; @dunneCompilationNetworkAnalyses2008; @roopnarineEcologicalModellingPaleocommunity2017] and *pattern finding models* [*e.g.,* @strydomGraphEmbeddingTransfer2023; @pichlerMachineLearningAlgorithms2020; @strydomFoodWebReconstruction2022; @caronAddressingEltonianShortfall2022; @llewelynPredictingPredatorPrey2023; @desjardins-proulxEcologicalInteractionsNetflix2017; @eklofSecondaryExtinctionsFood2013; @cirtwillQuantitativeFrameworkInvestigating2019]. The fundamental difference between these two model groups is that *mechanistic models* rely on expert knowledge and make explicit assumptions on trait-feeding relationships, whereas the *pattern finding models* are dependent on existing interaction datasets from feeding rules can be elucidated. It perhaps also bears repeating that these models are often only presenting a list of feasible interactions and that the resulting network is 'unstructured', as it is uconstrained by any processes or conditions that generate structure. While these networks can be imprinted with external definitions of trophic position and guild identity to deliver hypothetical structure, this structure is not an emergent property of the links and species pairs [@caronTraitmatchingModelsPredict2024].
 
 Feasibility networks are useful for determining all feasible interactions for a specific community, and the models that have been developed in this context have the potential to allow us to construct first draft networks for communities for which we have no interaction data [@strydomFoodWebReconstruction2022], and are valuable not only in data poor regions but also for predicting interactions for 'unobservable' communities *e.g.,* prehistoric networks [@yeakelCollapseEcologicalNetwork2014; @frickeCollapseTerrestrialMammal2022; @dunhillExtinctionCascadesCommunity2024] or future, novel community assemblages. Conceptually this is particularly valuable if we want to understand interactions between novel communitites, as well as the rewiring capacity of species. Additionally, an understanding of the role of interactions between species has allowed us to better determine the distribution of a species by accounting not only for the role of the environment but also the role of species interactions [@higinoMismatchIUCNRange2023; @pollockUnderstandingCooccurrenceModelling2014].
 
 ## 'Behavioural' networks
 
-Ultimately realised networks and capture some aspect of how the behavior of a species determines if a link is realised or not and can be modelled in two ways; models that predict realised interactions (whereby he behaiour of a secies is modelled *i.e.,* its diet choice), and models that predict the structure of realised networks (whereby the behvaiour of the system is modelled and assumptions are made with regards to the structure of a network). In terms predicting interactions current models are rooted in feeding theory and allocate the links between species based on energy *e.g.,* diet models [@beckermanForagingBiologyPredicts2006; @petcheySizeForagingFood2008] have been used construct networks based on both profitability (as determined by the handling time, energy content, and predator attack rate) as well as abundance (prey density), and modular models [@woottonModularTheoryTrophic2023; @krauseCompartmentsRevealedFoodweb2003] are based on the compartmentation and aquisition of energy for species at different trophic levels. Models that determine structure are based on the idea that networks follow a trophic hierarchy and that network structure can be determined by distributing interactions along single dimension [the “niche axis”; @allesinaGeneralModelFood2008], while parametrising an aspect of the network structure [although see @allesinaFoodWebModels2009 for a parameter-free model].
+Ultimately realised networks and capture some aspect of how the behavior of a species determines if a link is realised or not and can be modelled in two ways; models that predict realised interactions (whereby he behaiour of a secies is modelled *i.e.,* its diet choice), and models that predict the structure of realised networks (whereby the behvaiour of the system is modelled and assumptions are made with regards to the structure of a network). In terms predicting interactions current models are rooted in feeding theory and allocate the links between species based on energy *e.g.,* diet models [@beckermanForagingBiologyPredicts2006; @petcheySizeForagingFood2008] have been used construct networks based on both profitability (as determined by the handling time, energy content, and predator attack rate) as well as abundance (prey density). [@woottonModularTheoryTrophic2023]. At a 'coarser', funtional level there are models that are based on the compartmentation and aquisition of energy for species at different trophic levels [@allesinaFoodWebModels2009; @krauseCompartmentsRevealedFoodweb2003]. Models that determine structure are based on the idea that networks follow a trophic hierarchy and that network structure can be determined by distributing interactions along single dimension [the “niche axis”; @allesinaGeneralModelFood2008], while parametrising an aspect of the network structure [although see @allesinaFoodWebModels2009 for a parameter-free model].
 
 As behavioural networks are are build on the concept of dynamic processes (*e.g.,* the abundance of species will always be in flux) these networks are valuable for understanding the behaviour of networks over time, or their response to change [@lajaaitiEcologicalNetworksDynamicsJlJulia2024; @delmasSimulationsBiomassDynamics2017; @curtsdotterEcosystemFunctionPredator2019]. However, they are 'costly' to construct (requiring data about the entire community, as it is the behaviour of the system that determines the behaviour of the part) and also lack the larger diet niche context afforded by metawebs. Structural models provide a data-light (the models often only require species richness) but assumption heavy (the resulting network structure is determined by an assumption of network structure) alternative, however they do not make species specific predictions and so cannot be used to determine if an interaction is either possible *or* realised between two species (*i.e.,* one cannot use these models to determine if species $a$ eats species $b$). Although this means this suite of models are unsuitable as tools for predicting species-specific interactions, they have been shown to be sufficient tools to predict the structure of networks [@williamsSuccessItsLimits2008], and are useful in synthetic simulations.
 
@@ -161,9 +161,10 @@ It is probably both this nuance as well as a lack of clear boundaries and guidel
 
 **Methodological challenges**
 
-1.  Tools that allow us to estimate both the feasibility as well as realisation of links: Currently most approaches to modelling relaised networks fail to explicitly account for any form of evolutionary constraint [although see @vandewalleArthropodFoodWebs2023] and we need to develop either an ensemble modelling approach [@beckerOptimisingPredictiveModels2022; @terryFindingMissingLinks2020] or tools that will allow for the downsampling of metawebs into realised networks [*e.g.,* @roopnarineExtinctionCascadesCatastrophe2006].
-2.  Modelling interaction strength: Although realised networks are more closely aligned with *explicitly* capturing interaction strength we lack models that allow us to quantify this [@wellsSpeciesInteractionsEstimating2013; @strydomRoadmapPredictingSpecies2021].
-3.  How do we validate our predictions?: Progress has been made to assess how well a model recovers pairwise interactions [@strydomRoadmapPredictingSpecies2021; @poisotGuidelinesPredictionSpecies2023], but we still lack clear set of guidelines for benchmarking the ability of models to recover structure [@allesinaGeneralModelFood2008]
+1.  Tools that allow us to estimate both the feasibility as well as realisation of links: Currently most approaches to modelling relaised networks fail to explicitly account for any form of evolutionary constraint [although see @vandewalleArthropodFoodWebs2023 and @woottonModularTheoryTrophic2023] and we need to develop either an ensemble modelling approach [@beckerOptimisingPredictiveModels2022; @terryFindingMissingLinks2020] or tools that will allow for the downsampling of metawebs into realised networks [*e.g.,* @roopnarineExtinctionCascadesCatastrophe2006].
+2.  Is there something in generalisable models that 'combine' different processes/aspects (*e.g.,* using body size as a catch all) versus limited models that allow you to unpack things bit-by-bit (*i.e.,* process by process). So @woottonModularTheoryTrophic2023 *may* (TBD) span the gamut but it lcks the ability to unpack... Although myabe the terms do?
+3.  Modelling interaction strength: Although realised networks are more closely aligned with *explicitly* capturing interaction strength we lack models that allow us to quantify this [@wellsSpeciesInteractionsEstimating2013; @strydomRoadmapPredictingSpecies2021].
+4.  How do we validate our predictions?: Progress has been made to assess how well a model recovers pairwise interactions [@strydomRoadmapPredictingSpecies2021; @poisotGuidelinesPredictionSpecies2023], but we still lack clear set of guidelines for benchmarking the ability of models to recover structure [@allesinaGeneralModelFood2008]
 
 **Theory challenges**