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docs/_tex/index.tex

@@ -212,7 +212,7 @@
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 }%
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-\date{2024-10-02}
+\date{2024-10-03}
 
 \usepackage{setspace}
 \usepackage[left]{lineno}
@@ -622,8 +622,8 @@ \section{Network construction is
 representations in the context of trying to understand the feeding
 dynamics of a seasonal community.
 
-\begin{tcolorbox}[enhanced jigsaw, toptitle=1mm, left=2mm, opacityback=0, toprule=.15mm, colframe=quarto-callout-note-color-frame, coltitle=black, bottomrule=.15mm, leftrule=.75mm, colbacktitle=quarto-callout-note-color!10!white, breakable, opacitybacktitle=0.6, rightrule=.15mm, arc=.35mm, titlerule=0mm, bottomtitle=1mm, colback=white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Box 1 - Why we need to aggregate networks at different scales: A
-hypothetical case study}]
+\begin{tcolorbox}[enhanced jigsaw, opacitybacktitle=0.6, coltitle=black, colback=white, colbacktitle=quarto-callout-note-color!10!white, colframe=quarto-callout-note-color-frame, bottomrule=.15mm, arc=.35mm, left=2mm, toprule=.15mm, breakable, toptitle=1mm, opacityback=0, bottomtitle=1mm, titlerule=0mm, leftrule=.75mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Box 1 - Why we need to aggregate networks at different scales: A
+hypothetical case study}, rightrule=.15mm]
 
 Although it might seem most prudent to be predicting, constructing, and
 defining networks that are the closest representation of reality there
@@ -700,14 +700,17 @@ \subsection{Models that predict metawebs (feasible
 finding' models are dependent on existing datasets from which to
 elucidate feeding rules. These models are useful for determining all
 feasible interactions for a specific community, and owing to the
-availability of datasets (Gray et al., 2015; \emph{e.g.,} Poelen et al.,
-2014; Poisot, Baiser, et al., 2016), as well as the development of model
-testing/benchmarking tools (Poisot, 2023), means that these models can
-be validated and (with relative confidence) be used to construct first
-draft networks for communities for which we have no data (Strydom et
-al., 2022), and are valuable for constructing networks where we lack any
-interaction data \emph{e.g.,} prehistoric networks (Fricke et al., 2022;
-Yeakel et al., 2014).
+availability of empirical interaction datasets (Gray et al., 2015;
+\emph{e.g.,} Poelen et al., 2014; Poisot, Baiser, et al., 2016), as well
+as the development of model testing/benchmarking tools (Poisot, 2023),
+means that these models can be validated and (with relative confidence)
+be used to construct first draft networks for communities for which we
+have no data (Strydom et al., 2022), and are valuable for constructing
+networks where we lack any interaction data \emph{e.g.,} prehistoric
+networks (Fricke et al., 2022; Yeakel et al., 2014) or even to predict
+interactions for contemporary species that do not currently co-occur and
+thus we have no way of empirically evaluating if the interaction is
+feasible or not.
 
 \subsection{Models that predict realised networks (realised
 interactions)}\label{models-that-predict-realised-networks-realised-interactions}
@@ -818,15 +821,14 @@ \subsection{At what scale should we be predicting and using
 biggest barriers that is affecting the use of networks in applied
 settings\ldots{} By define I mean both delimiting the time and
 geographic scale at which a network is aggregated at (Estay et al.,
-2023). We know that space plays a role - the motility of different
-species will influence both the dynamics of networks but also serve to
-link smaller `subnetworks'/community (Fortin et al., 2021; Rooney et
-al., 2008). And so does time \emph{e.g.,} seasonal rewiring (Brimacombe
-et al., 2021; Laender et al., 2010). There is also a bit of an interplay
-with time and data and the different scales that they may be integrated
-at - co-occurrence may span decades and just because two species have
-been recorded in the same space does not mean it was at the same
-timescale (Brimacombe et al., 2024).
+2023). We know that space plays a role influence both network properties
+(Galiana et al., 2018), as well as dynamics (Fortin et al., 2021; Rooney
+et al., 2008). And so does time \emph{e.g.,} seasonal rewiring
+(Brimacombe et al., 2021; Laender et al., 2010). There is also a bit of
+an interplay with time and data and the different scales that they may
+be integrated at - co-occurrence may span decades and just because two
+species have been recorded in the same space does not mean it was at the
+same timescale (Brimacombe et al., 2024).
 
 \subsection{Feasible, realised, or
 sustainable?}\label{feasible-realised-or-sustainable}
@@ -1083,6 +1085,12 @@ \section*{References}\label{references}
 \emph{Science}, \emph{377}(6609), 1008--1011.
 \url{https://doi.org/10.1126/science.abn4012}
 
+\bibitem[\citeproctext]{ref-galianaSpatialScalingSpecies2018}
+Galiana, N., Lurgi, M., Claramunt-López, B., Fortin, M.-J., Leroux, S.,
+Cazelles, K., Gravel, D., \& Montoya, J. M. (2018). The spatial scaling
+of species interaction networks. \emph{Nature Ecology \& Evolution},
+\emph{2}(5), 782--790. \url{https://doi.org/10.1038/s41559-018-0517-3}
+
 \bibitem[\citeproctext]{ref-garcia-callejasNonrandomInteractionsGuilds2023}
 García-Callejas, D., Godoy, O., Buche, L., Hurtado, M., Lanuza, J. B.,
 Allen-Perkins, A., \& Bartomeus, I. (2023). Non-random interactions

docs/_tex/references.bib

@@ -83,7 +83,7 @@ @misc{banvilleDecipheringProbabilisticSpecies2024
   file = {/Users/tanyastrydom/Zotero/storage/BYNFU5RC/Banville et al. - 2024 - Deciphering probabilistic species interaction netw.pdf}
 }
 
-@article{banvilleMangalJlEcologicalNetworks2021,
+@article{banvilleMangaljlEcologicalNetworksjlTwo2021,
   title = {Mangal.Jl and {{EcologicalNetworks}}.Jl: {{Two}} Complementary Packages for Analyzing Ecological Networks in {{Julia}}},
   shorttitle = {Mangal.Jl and {{EcologicalNetworks}}.Jl},
   author = {Banville, Francis and Vissault, Steve and Poisot, Timoth{\'e}e},
@@ -1066,6 +1066,23 @@ @article{frickeCollapseTerrestrialMammal2022
   file = {/Users/tanyastrydom/Zotero/storage/C6BQGXPV/Fricke et al. - 2022 - Collapse of terrestrial mammal food webs since the.pdf}
 }
 
+@article{galianaSpatialScalingSpecies2018,
+  title = {The Spatial Scaling of Species Interaction Networks},
+  author = {Galiana, N{\'u}ria and Lurgi, Miguel and {Claramunt-L{\'o}pez}, Bernat and Fortin, Marie-Jos{\'e}e and Leroux, Shawn and Cazelles, Kevin and Gravel, Dominique and Montoya, Jos{\'e} M.},
+  year = {2018},
+  month = may,
+  journal = {Nature Ecology \& Evolution},
+  volume = {2},
+  number = {5},
+  pages = {782--790},
+  issn = {2397-334X},
+  doi = {10.1038/s41559-018-0517-3},
+  urldate = {2020-02-09},
+  abstract = {How biotic interactions change across spatial scales is not well characterized. Here, the authors outline a theoretical framework to explore the spatial scaling of multitrophic communities, and present testable predictions on network-area relationships (NARs).},
+  copyright = {2018 The Author(s)},
+  langid = {english}
+}
+
 @article{gaoGgVennDiagramIntuitiveEasytoUse2021,
   title = {{{ggVennDiagram}}: {{An Intuitive}}, {{Easy-to-Use}}, and {{Highly Customizable R Package}} to {{Generate Venn Diagram}}},
   shorttitle = {{{ggVennDiagram}}},
@@ -1286,7 +1303,7 @@ @misc{hongDealingClassImbalance2016
   doi = {10.48550/arXiv.1607.02705},
   urldate = {2024-03-22},
   abstract = {We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tunable parameter. The threshold is the maximum value of this tunable parameter where the conditions of a certain decision are satisfied. We show that thresholding is applicable not only for linear classifiers but also for non-linear classifiers. We show that this is the implicit assumption for many approaches to deal with class imbalance in linear classifiers. We then extend this paradigm beyond linear classification and show how non-linear classification can be dealt with under this umbrella framework of thresholding. The proposed method can be used for outlier detection in many real-life scenarios like in manufacturing. In advanced manufacturing units, where the manufacturing process has matured over time, the number of instances (or parts) of the product that need to be rejected (based on a strict regime of quality tests) becomes relatively rare and are defined as outliers. How to detect these rare parts or outliers beforehand? How to detect combination of conditions leading to these outliers? These are the questions motivating our research. This paper focuses on prediction of outliers and conditions leading to outliers using classification. We address the problem of outlier detection using classification. The classes are good parts (those passing the quality tests) and bad parts (those failing the quality tests and can be considered as outliers). The rarity of outliers transforms this problem into a class-imbalanced classification problem.},
-  archiveprefix = {arxiv},
+  archiveprefix = {arXiv},
   keywords = {Computer Science - Machine Learning},
   file = {/Users/tanyastrydom/Zotero/storage/M3NTKJIV/Hong et al. - 2016 - Dealing with Class Imbalance using Thresholding.pdf;/Users/tanyastrydom/Zotero/storage/4PF94AGG/1607.html}
 }
@@ -1830,7 +1847,7 @@ @misc{peixotoScalableNetworkReconstruction2024
   publisher = {arXiv},
   urldate = {2024-05-07},
   abstract = {Network reconstruction consists in determining the unobserved pairwise couplings between \$N\$ nodes given only observational data on the resulting behavior that is conditioned on those couplings -- typically a time-series or independent samples from a graphical model. A major obstacle to the scalability of algorithms proposed for this problem is a seemingly unavoidable quadratic complexity of \${\textbackslash}Omega(N{\textasciicircum}2)\$, corresponding to the requirement of each possible pairwise coupling being contemplated at least once, despite the fact that most networks of interest are sparse, with a number of non-zero couplings that is only \$O(N)\$. Here we present a general algorithm applicable to a broad range of reconstruction problems that significantly outperforms this quadratic baseline. Our algorithm relies on a stochastic second neighbor search (Dong et al., 2011) that produces the best edge candidates with high probability, thus bypassing an exhaustive quadratic search. If we rely on the conjecture that the second-neighbor search finishes in log-linear time (Baron \& Darling, 2020; 2022), we demonstrate theoretically that our algorithm finishes in subquadratic time, with a data-dependent complexity loosely upper bounded by \$O(N{\textasciicircum}\{3/2\}{\textbackslash}log N)\$, but with a more typical log-linear complexity of \$O(N{\textbackslash}log{\textasciicircum}2N)\$. In practice, we show that our algorithm achieves a performance that is many orders of magnitude faster than the quadratic baseline -- in a manner consistent with our theoretical analysis -- allows for easy parallelization, and thus enables the reconstruction of networks with hundreds of thousands and even millions of nodes and edges.},
-  archiveprefix = {arxiv},
+  archiveprefix = {arXiv},
   langid = {english},
   keywords = {Computer Science - Data Structures and Algorithms,Computer Science - Machine Learning,Physics - Data Analysis Statistics and Probability,Statistics - Computation,Statistics - Machine Learning},
   file = {/Users/tanyastrydom/Zotero/storage/3I6Q92YT/Peixoto - 2024 - Scalable network reconstruction in subquadratic ti.pdf}