🎤 ref drop

index.qmd

@@ -57,7 +57,7 @@ number-sections: true
 
 At the heart of modern biodiversity science are a set of concepts and theories about biodiversity, stability and function. These relate to the abundance, distribution and services that biodiversity provides, and how biodiversity – as an interconnected set of species – responds to multiple stressors. The interaction between species is one of the fundamental building blocks of ecological communities, providing a powerful abstraction that can help quantify, conceptualise, and understand biodiversity dynamics, and ultimately, make predictions, mitigate change, and manage services [@windsorUsingEcologicalNetworks2023]. Such network representations of biodiversity (including within species diversity) are increasingly argued to be an asset to predictive ecology, climate change mitigation and resource management, with the argument that characterising biodiversity in a network will afford a deeper capacity to understand and predict the abundance, distribution, dynamics and services provided by multiple species facing multiple stressors. However, there is a growing discourse around limitations to the interpretation and applied use of networks [@dormannRisePossibleFall2023; @bluthgenWhyNetworkAnalysis2010], primarily as the result of shortcomings regarding the conceptualisation of networks [@bluthgenCriticalEvaluationNetwork2024].
 
-An 'interaction network' can be defined and conceptualised in a myriad of ways, which means that different networks will be embedding different processes (or determinants) of interactions, ultimately influencing the patterns and mechanisms that are inferred [@proulxNetworkThinkingEcology2005]. The different ways in which a network can be represented is the result of *how* the network is constructed, which itself rests on two pillars: the data used to construct the network [of which there has been a plethora of discussions as to the challenges relating to the scale and nature of data collection/observation *e.g.,* @bluthgenCriticalEvaluationNetwork2024; @brimacombeShortcomingsReusingSpecies2023; @moulatletScalingTrophicSpecialization2024; @pringleResolvingFoodWebStructure2020; @polisComplexTrophicInteractions1991] and the underlying theory as to what drives the occurrence of interactions between species. The latter represents an expression of mechanism and process that gives rise to the patterns that emerge from collating interactions among species, and will ultimately inform which data are deemed important in the determination of interactions occurring. Each of these pillars carries with it a set of practical, semantic and conceptual constraints that not only influence progress in making network ecology more valuable and potentially predictive, but help define the spatial, temporal, and evolutionary scale of assumptions we make and the predictions we might generate from different network representations.
+An 'interaction network' can be defined and conceptualised in a myriad of ways, which means that different networks will be embedding different processes (or determinants) of interactions, ultimately influencing the patterns and mechanisms that are inferred [@proulxNetworkThinkingEcology2005]. The different ways in which a network can be represented is the result of *how* the network is constructed, which itself rests on two pillars: the data used to construct the network [of which there has been a plethora of discussions as to the challenges relating to the scale and nature of data collection/observation *e.g.,* @bluthgenCriticalEvaluationNetwork2024; @brimacombeShortcomingsReusingSpecies2023; @moulatletScalingTrophicSpecialization2024; @pringleResolvingFoodWebStructure2020; @polisComplexTrophicInteractions1991; @saberskiImpactDataResolution2024] and the underlying theory as to what drives the occurrence of interactions between species. The latter represents an expression of mechanism and process that gives rise to the patterns that emerge from collating interactions among species, and will ultimately inform which data are deemed important in the determination of interactions occurring. Each of these pillars carries with it a set of practical, semantic and conceptual constraints that not only influence progress in making network ecology more valuable and potentially predictive, but help define the spatial, temporal, and evolutionary scale of assumptions we make and the predictions we might generate from different network representations.
 
 In this perspective we aim to provide an overview of the different **food web** representations (*a note on how there has been developments in the 'bipartite space' and it would be flawed to try and view them in tandem as food webs and non-trophic webs are two very different conceptualisations*), particularly how these relate to the terminology used to define a network, and how this influenced by both the processes that determine networks as well as how this relates to the way in which we construct networks. The provision of this detail ultimately leads to a set of insights and conclusions about whether, when. and under what conditions network representations of biodiversity can contribute to the advancement of ecological theory and generate value in predictive ecology. Specifically, we finish this perspective with an overview of fundamental questions in ecology that we think can benefit from network thinking and a proposal that such thinking can accelerate our capacity to predict the impact of multiple stressors on biodiverse communities.
 
@@ -144,7 +144,7 @@ There has been a suite of models that have been developed to predict feeding lin
 
 ## At what scale should we be predicting and using networks?
 
-We lack an understanding of which processes drive interactions at different scales [@saraviaEcologicalNetworkAssembly2022], as well as to what the appropriate level of aggregation for a 'network' is [@estayEditorialPatternsProcesses2023; @moulatletScalingTrophicSpecialization2024]. Thus we need an understanding of not only how time and scale influence the interpretation of networks [@moralesEffectSpacePlant2008; @bluthgenEcologyMammalsInteraction2021], but how this is in turn influenced by the type of networks used. Which presents a challenge both in deciding what the appropriate spatial and time scales are for constructing not only a network but also which type of network representation. Space influences both network properties [@galianaSpatialScalingSpecies2018], as well as dynamics [@rooneyLandscapeTheoryFood2008; @fortinNetworkEcologyDynamic2021], and time has implications when it comes to accounting for seasonal turnover in communities [@brimacombeInferredSeasonalInteraction2021; @laenderCarbonTransferHerbivore2010] as well as thinking about co-occurrence, particularly the records that are used to determine co-occurence [@brimacombeApplyingMethodIts2024]. Although multilayer networks may allow us to encode the nuances of space and time [@hutchinsonSeeingForestTrees2019] we still need to understand the implications of *e.g.,* constructing networks that are not at ecologically but rather politically relevant scales [@strydomFoodWebReconstruction2022] and what the implications of this disconnect may be.
+We lack an understanding of which processes drive interactions at different scales [@saraviaEcologicalNetworkAssembly2022], as well as to what the appropriate level of aggregation for a 'network' is [@estayEditorialPatternsProcesses2023; @moulatletScalingTrophicSpecialization2024; @saberskiImpactDataResolution2024]. Thus we need an understanding of not only how time and scale influence the interpretation of networks [@moralesEffectSpacePlant2008; @bluthgenEcologyMammalsInteraction2021], but how this is in turn influenced by the type of networks used. Which presents a challenge both in deciding what the appropriate spatial and time scales are for constructing not only a network but also which type of network representation. Space influences both network properties [@galianaSpatialScalingSpecies2018], as well as dynamics [@rooneyLandscapeTheoryFood2008; @fortinNetworkEcologyDynamic2021], and time has implications when it comes to accounting for seasonal turnover in communities [@brimacombeInferredSeasonalInteraction2021; @laenderCarbonTransferHerbivore2010] as well as thinking about co-occurrence, particularly the records that are used to determine co-occurence [@brimacombeApplyingMethodIts2024]. Although multilayer networks may allow us to encode the nuances of space and time [@hutchinsonSeeingForestTrees2019] we still need to understand the implications of *e.g.,* constructing networks that are not at ecologically but rather politically relevant scales [@strydomFoodWebReconstruction2022] and what the implications of this disconnect may be.
 
 # The future value of networks
 

references.bib

@@ -2681,6 +2681,25 @@ @article{rossbergFoodWebsExperts2006
   file = {/Users/tanyastrydom/Zotero/storage/TKM2X2U2/Rossberg et al. - 2006 - Food webs Experts consuming families of experts.pdf;/Users/tanyastrydom/Zotero/storage/7H8PCX8N/S0022519305005606.html}
 }
 
+@article{saberskiImpactDataResolution2024,
+  title = {The Impact of Data Resolution on Dynamic Causal Inference in Multiscale Ecological Networks},
+  author = {Saberski, Erik and Lorimer, Tom and Carpenter, Delia and Deyle, Ethan and Merz, Ewa and Park, Joseph and Pao, Gerald M. and Sugihara, George},
+  year = {2024},
+  month = nov,
+  journal = {Communications Biology},
+  volume = {7},
+  number = {1},
+  pages = {1--10},
+  publisher = {Nature Publishing Group},
+  issn = {2399-3642},
+  doi = {10.1038/s42003-024-07054-z},
+  urldate = {2024-11-11},
+  abstract = {While it is commonly accepted that ecosystem dynamics are nonlinear, what is often not acknowledged is that nonlinearity implies scale-dependence. With the increasing availability of high-resolution ecological time series, there is a growing need to understand how scale and resolution in the data affect the construction and interpretation of causal networks---specifically, networks mapping how changes in one variable drive changes in others as part of a shared dynamic system (``dynamic causation''). We use Convergent Cross Mapping (CCM), a method specifically designed to measure dynamic causation, to study the effects of varying temporal and taxonomic/functional resolution in data when constructing ecological causal networks. As the system is viewed at different scales relationships will appear and disappear. The relationship between data resolution and interaction presence is not random: the temporal scale at which a relationship is uncovered identifies a biologically relevant scale that drives changes in population abundance. Further, causal relationships between taxonomic aggregates (low-resolution) are shown to be influenced by the number of interactions between their component species (high-resolution). Because no single level of resolution captures all the causal links in a system, a more complete understanding requires multiple levels when constructing causal networks.},
+  copyright = {2024 The Author(s)},
+  langid = {english},
+  file = {/Users/tanyastrydom/Zotero/storage/2Z9FLMT6/Saberski et al. - 2024 - The impact of data resolution on dynamic causal in.pdf}
+}
+
 @article{saraviaEcologicalNetworkAssembly2022,
   title = {Ecological Network Assembly: {{How}} the Regional Metaweb Influences Local Food Webs},
   shorttitle = {Ecological Network Assembly},