From 6bcd24a60566a70baf04cea483ab0efe3d36bcd7 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Fri, 15 Nov 2024 10:42:49 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                                 |   2 +-
 _tex/index.tex                            |   6 +++---
 _tex/references.bib                       |  19 +++++++++++++++++++
 index.docx                                | Bin 1562688 -> 1562688 bytes
 index.html                                |  11 ++++++-----
 index.pdf                                 | Bin 1771068 -> 1771078 bytes
 notebooks/model_descriptions-preview.html |   4 ++--
 notebooks/model_qualitative-preview.html  |   4 ++--
 notebooks/model_qualitative.embed.ipynb   |  12 ++++++------
 notebooks/model_qualitative.out.ipynb     |  12 ++++++------
 notebooks/model_quantitative-preview.html |   4 ++--
 notebooks/model_quantitative.embed.ipynb  |   8 ++++----
 notebooks/model_quantitative.out.ipynb    |   8 ++++----
 13 files changed, 55 insertions(+), 35 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 9b9c953..0f1f805 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-3beca097
\ No newline at end of file
+70db98e3
\ No newline at end of file
diff --git a/_tex/index.tex b/_tex/index.tex
index cf3604e..38ab399 100644
--- a/_tex/index.tex
+++ b/_tex/index.tex
@@ -227,7 +227,7 @@
 1%
 }%
 }
-\date{2024-11-14}
+\date{2024-11-15}
 
 \usepackage{setspace}
 \usepackage[left]{lineno}
@@ -654,8 +654,8 @@ \section{Network construction is
 context of trying to understand the feeding dynamics of a seasonal
 community.
 
-\begin{tcolorbox}[enhanced jigsaw, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, rightrule=.15mm, left=2mm, coltitle=black, opacityback=0, breakable, 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}, toprule=.15mm, bottomtitle=1mm, colback=white, toptitle=1mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, bottomrule=.15mm, arc=.35mm]
+\begin{tcolorbox}[enhanced jigsaw, 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}, opacitybacktitle=0.6, bottomrule=.15mm, coltitle=black, breakable, colbacktitle=quarto-callout-note-color!10!white, colback=white, colframe=quarto-callout-note-color-frame, titlerule=0mm, left=2mm, toprule=.15mm, opacityback=0, bottomtitle=1mm, toptitle=1mm, arc=.35mm, leftrule=.75mm, rightrule=.15mm]
 
 Although it might seem most prudent to be predicting, constructing, and
 defining networks that are the closest representation of reality there
diff --git a/_tex/references.bib b/_tex/references.bib
index 865cc97..d1636fa 100644
--- a/_tex/references.bib
+++ b/_tex/references.bib
@@ -1866,6 +1866,25 @@ @article{llewelynPredictingPredatorPrey2023
   file = {/Users/tanyastrydom/Zotero/storage/YKHUIRTF/Llewelyn et al. - 2023 - Predicting predator–prey interactions in terrestri.pdf;/Users/tanyastrydom/Zotero/storage/U8ZH3IC6/ecog.html}
 }
 
+@article{loreauBiodiversityEcosystemStability2013,
+  title = {Biodiversity and Ecosystem Stability: A Synthesis of Underlying Mechanisms},
+  shorttitle = {Biodiversity and Ecosystem Stability},
+  author = {Loreau, Michel and {de Mazancourt}, Claire},
+  year = {2013},
+  journal = {Ecology Letters},
+  volume = {16},
+  number = {s1},
+  pages = {106--115},
+  issn = {1461-0248},
+  doi = {10.1111/ele.12073},
+  urldate = {2024-11-15},
+  abstract = {There is mounting evidence that biodiversity increases the stability of ecosystem processes in changing environments, but the mechanisms that underlie this effect are still controversial and poorly understood. Here, we extend mechanistic theory of ecosystem stability in competitive communities to clarify the mechanisms underlying diversity--stability relationships. We first explain why, contrary to a widely held belief, interspecific competition should generally play a destabilising role. We then explore the stabilising effect of differences in species' intrinsic rates of natural increase and provide a synthesis of various potentially stabilising mechanisms. Three main mechanisms are likely to operate in the stabilising effects of biodiversity on ecosystem properties: (1) asynchrony of species' intrinsic responses to environmental fluctuations, (2) differences in the speed at which species respond to perturbations, (3) reduction in the strength of competition. The first two mechanisms involve temporal complementarity between species, while the third results from functional complementarity. Additional potential mechanisms include selection effects, behavioural changes resulting from species interactions and mechanisms arising from trophic or non-trophic interactions and spatial heterogeneity. We conclude that mechanistic trait-based approaches are key to predicting the effects of diversity on ecosystem stability and to bringing the old diversity--stability debate to a final resolution.},
+  copyright = {{\copyright} 2013 John Wiley \& Sons Ltd/CNRS},
+  langid = {english},
+  keywords = {Biodiversity,complementarity,ecosystem,fast-slow dynamics,overyielding,stability,synchrony},
+  file = {/Users/tanyastrydom/Zotero/storage/V3YR2JI9/Loreau and de Mazancourt - 2013 - Biodiversity and ecosystem stability a synthesis .pdf;/Users/tanyastrydom/Zotero/storage/C4ZQFRS2/ele.html}
+}
+
 @article{maioranoTETRAEUSpecieslevelTrophic2020,
   title = {{{TETRA-EU}} 1.0: {{A}} Species-Level Trophic Metaweb of {{European}} Tetrapods},
   shorttitle = {{{TETRA-EU}} 1.0},
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diff --git a/index.html b/index.html
index 22240a1..9f641ba 100644
--- a/index.html
+++ b/index.html
@@ -10,7 +10,7 @@
 <meta name="author" content="Jennifer A. Dunne">
 <meta name="author" content="Timothée Poisot">
 <meta name="author" content="Andrew P. Beckerman">
-<meta name="dcterms.date" content="2024-11-14">
+<meta name="dcterms.date" content="2024-11-15">
 <meta name="keywords" content="food web, network construction, scientific ignorance">
 
 <title>Unveiling the Complexity of Food Webs: A Comprehensive Overview of Definitions, Scales, and Mechanisms</title>
@@ -107,10 +107,10 @@
 <meta name="citation_author" content="Jennifer A. Dunne">
 <meta name="citation_author" content="Timothée Poisot">
 <meta name="citation_author" content="Andrew P. Beckerman">
-<meta name="citation_publication_date" content="2024-11-14">
-<meta name="citation_cover_date" content="2024-11-14">
+<meta name="citation_publication_date" content="2024-11-15">
+<meta name="citation_cover_date" content="2024-11-15">
 <meta name="citation_year" content="2024">
-<meta name="citation_online_date" content="2024-11-14">
+<meta name="citation_online_date" content="2024-11-15">
 <meta name="citation_language" content="en">
 <meta name="citation_reference" content="citation_title=A novel method for predicting ecological interactions with an unsupervised machine learning algorithm;,citation_abstract=This gap in knowledge regarding ecological interactions has prompted the development of various predictive approaches. Traditionally, ecological interactions have been inferred using traits. However, the lack of trait information for numerous organisms necessitates using phylogenetic data and statistical insights from interaction matrices for prediction. Previous studies have overlooked the validation of model-predicted interactions. This study used a novel method in predicting ecological interactions using a self-organizing map (SOM), an unsupervised machine learning algorithm. The SOM learns from the input interaction matrix by grouping the nodes into output layers based on their interactions. Subsequently, the trained model predicts the interactions as scores. To distinguish between interactions and non-interactions, we employed F1 score maximization, setting scores above a specific threshold as interactions and the remainder as non-interactions. We applied this method to three unipartite metawebs and one bipartite metaweb and subsequently validated the predicted interactions using two innovative approaches: taxonomic and interaction recovery validation. Our method exhibited outstanding predictive performance, particularly for large networks. Various binary classification performance indicators, including F1 score (0.84–0.97) and accuracy (0.97–0.99), indicated high performance. Moreover, the method generated minimal predicted interactions, signifying low noise in the predictions, particularly for large networks. Taxonomic validation excels in metawebs with a connectance $&amp;amp;amp;gt;$0.1 but performs poorly in metawebs with very low connectance. In contrast, interaction recovery was most effective in larger metawebs. Our proposed method excels at making highly accurate predictions of ecological interactions with minimal noise, solely utilizing input interaction data without relying on traits or phylogenetic information regarding interacting nodes. These predictions are particularly precise for large networks, underscoring their potential to address knowledge gaps in emerging extensive metawebs. Notably, taxonomic validation and interaction recovery methods are sensitive to connectance and network size, respectively, suggesting prospects for developing robust interaction validation methods.;,citation_author=Sagar Adhurya;,citation_author=Young-Seuk Park;,citation_publication_date=2024;,citation_cover_date=2024;,citation_year=2024;,citation_issue=n/a;,citation_doi=10.1111/2041-210X.14358;,citation_issn=2041-210X;,citation_volume=n/a;,citation_language=en-US;,citation_journal_title=Methods in Ecology and Evolution;">
 <meta name="citation_reference" content="citation_title=The marine fish food web is globally connected;,citation_abstract=The productivity of marine ecosystems and the services they provide to humans are largely dependent on complex interactions between prey and predators. These are embedded in a diverse network of trophic interactions, resulting in a cascade of events following perturbations such as species extinction. The sheer scale of oceans, however, precludes the characterization of marine feeding networks through de novo sampling. This effort ought instead to rely on a combination of extensive data and inference. Here we investigate how the distribution of trophic interactions at the global scale shapes the marine fish food web structure. We hypothesize that the heterogeneous distribution of species ranges in biogeographic regions should concentrate interactions in the warmest areas and within species groups. We find that the inferred global metaweb of marine fish—that is, all possible potential feeding links between co-occurring species—is highly connected geographically with a low degree of spatial modularity. Metrics of network structure correlate with sea surface temperature and tend to peak towards the tropics. In contrast to open-water communities, coastal food webs have greater interaction redundancy, which may confer robustness to species extinction. Our results suggest that marine ecosystems are connected yet display some resistance to perturbations because of high robustness at most locations.;,citation_author=Camille Albouy;,citation_author=Philippe Archambault;,citation_author=Ward Appeltans;,citation_author=Miguel B. Araújo;,citation_author=David Beauchesne;,citation_author=Kevin Cazelles;,citation_author=Alyssa R. Cirtwill;,citation_author=Marie-Josée Fortin;,citation_author=Nuria Galiana;,citation_author=Shawn J. Leroux;,citation_author=Loïc Pellissier;,citation_author=Timothée Poisot;,citation_author=Daniel B. Stouffer;,citation_author=Spencer A. Wood;,citation_author=Dominique Gravel;,citation_publication_date=2019-08;,citation_cover_date=2019-08;,citation_year=2019;,citation_issue=8;,citation_doi=10.1038/s41559-019-0950-y;,citation_issn=2397-334X;,citation_volume=3;,citation_language=en-US;,citation_journal_title=Nature Ecology &amp;amp;amp; Evolution;,citation_publisher=Nature Publishing Group;">
@@ -225,6 +225,7 @@
 <meta name="citation_reference" content="citation_title=Complexity and stability of ecological networks: A review of the theory;,citation_abstract=Our planet is changing at paces never observed before. Species extinction is happening at faster rates than ever, greatly exceeding the five mass extinctions in the fossil record. Nevertheless, our lives are strongly based on services provided by ecosystems, thus the responses to global change of our natural heritage are of immediate concern. Understanding the relationship between complexity and stability of ecosystems is of key importance for the maintenance of the balance of human growth and the conservation of all the natural services that ecosystems provide. Mathematical network models can be used to simplify the vast complexity of the real world, to formally describe and investigate ecological phenomena, and to understand ecosystems propensity of returning to its functioning regime after a stress or a perturbation. The use of ecological-network models to study the relationship between complexity and stability of natural ecosystems is the focus of this review. The concept of ecological networks and their characteristics are first introduced, followed by central and occasionally contrasting definitions of complexity and stability. The literature on the relationship between complexity and stability in different types of models and in real ecosystems is then reviewed, highlighting the theoretical debate and the lack of consensual agreement. The summary of the importance of this line of research for the successful management and conservation of biodiversity and ecosystem services concludes the review.;,citation_author=Pietro Landi;,citation_author=Henintsoa O. Minoarivelo;,citation_author=Åke Brännström;,citation_author=Cang Hui;,citation_author=Ulf Dieckmann;,citation_publication_date=2018-10;,citation_cover_date=2018-10;,citation_year=2018;,citation_issue=4;,citation_doi=10.1007/s10144-018-0628-3;,citation_issn=1438-390X;,citation_volume=60;,citation_language=en-US;,citation_journal_title=Population Ecology;">
 <meta name="citation_reference" content="citation_title=The Trophic-Dynamic Aspect of Ecology;,citation_author=Raymond L. Lindeman;,citation_publication_date=1942;,citation_cover_date=1942;,citation_year=1942;,citation_fulltext_html_url=https://www.jstor.org/stable/1930126;,citation_issue=4;,citation_doi=10.2307/1930126;,citation_issn=0012-9658;,citation_volume=23;,citation_journal_title=Ecology;,citation_publisher=Ecological Society of America;">
 <meta name="citation_reference" content="citation_title=Predicting predator–prey interactions in terrestrial endotherms using random forest;,citation_abstract=Species interactions play a fundamental role in ecosystems. However, few ecological communities have complete data describing such interactions, which is an obstacle to understanding how ecosystems function and respond to perturbations. Because it is often impractical to collect empirical data for all interactions in a community, various methods have been developed to infer interactions. Machine learning is increasingly being used for making interaction predictions, with random forest being one of the most frequently used of these methods. However, performance of random forest in inferring predator-prey interactions in terrestrial vertebrates and its sensitivity to training data quality remain untested. We examined predator–prey interactions in two diverse, primarily terrestrial vertebrate classes: birds and mammals. Combining data from a global interaction dataset and a specific community (Simpson Desert, Australia), we tested how well random forest predicted predator–prey interactions for mammals and birds using species’ ecomorphological and phylogenetic traits. We also tested how variation in training data quality – manipulated by removing records and switching interaction records to non-interactions – affected model performance. We found that random forest could predict predator–prey interactions for birds and mammals using ecomorphological or phylogenetic traits, correctly predicting up to 88 and 67% of interactions and non-interactions in the global and community-specific datasets, respectively. These predictions were accurate even when there were no records in the training data for focal species. In contrast, false non-interactions for focal predators in training data strongly degraded model performance. Our results demonstrate that random forest can identify predator–prey interactions for birds and mammals that have few or no interaction records. Furthermore, our study provides guidance on how to prepare training data to optimise machine learning classifiers for predicting species interactions, which could help ecologists 1) address knowledge gaps and explore network-related questions in data-poor situations, and 2) predict interactions for range-expanding species.;,citation_author=John Llewelyn;,citation_author=Giovanni Strona;,citation_author=Christopher R. Dickman;,citation_author=Aaron C. Greenville;,citation_author=Glenda M. Wardle;,citation_author=Michael S. Y. Lee;,citation_author=Seamus Doherty;,citation_author=Farzin Shabani;,citation_author=Frédérik Saltré;,citation_author=Corey J. A. Bradshaw;,citation_publication_date=2023;,citation_cover_date=2023;,citation_year=2023;,citation_issue=9;,citation_doi=10.1111/ecog.06619;,citation_issn=1600-0587;,citation_volume=2023;,citation_language=en-US;,citation_journal_title=Ecography;">
+<meta name="citation_reference" content="citation_title=Biodiversity and ecosystem stability: A synthesis of underlying mechanisms;,citation_abstract=There is mounting evidence that biodiversity increases the stability of ecosystem processes in changing environments, but the mechanisms that underlie this effect are still controversial and poorly understood. Here, we extend mechanistic theory of ecosystem stability in competitive communities to clarify the mechanisms underlying diversity–stability relationships. We first explain why, contrary to a widely held belief, interspecific competition should generally play a destabilising role. We then explore the stabilising effect of differences in species’ intrinsic rates of natural increase and provide a synthesis of various potentially stabilising mechanisms. Three main mechanisms are likely to operate in the stabilising effects of biodiversity on ecosystem properties: (1) asynchrony of species’ intrinsic responses to environmental fluctuations, (2) differences in the speed at which species respond to perturbations, (3) reduction in the strength of competition. The first two mechanisms involve temporal complementarity between species, while the third results from functional complementarity. Additional potential mechanisms include selection effects, behavioural changes resulting from species interactions and mechanisms arising from trophic or non-trophic interactions and spatial heterogeneity. We conclude that mechanistic trait-based approaches are key to predicting the effects of diversity on ecosystem stability and to bringing the old diversity–stability debate to a final resolution.;,citation_author=Michel Loreau;,citation_author=Claire Mazancourt;,citation_publication_date=2013;,citation_cover_date=2013;,citation_year=2013;,citation_issue=s1;,citation_doi=10.1111/ele.12073;,citation_issn=1461-0248;,citation_volume=16;,citation_language=en-US;,citation_journal_title=Ecology Letters;">
 <meta name="citation_reference" content="citation_title=TETRA-EU 1.0: A species-level trophic metaweb of European tetrapods;,citation_abstract=Motivation Documenting potential interactions between species represents a major step towards understanding and predicting the spatial and temporal structure of multi-trophic communities and their functioning. The metaweb concept summarizes the potential trophic (and non-trophic) interactions in a given species pool. As such, it generalizes the regional species pool of community ecology by incorporating the potential relationships between species from different trophic levels along with their functional characteristics. However, although this concept is very attractive theoretically, it has rarely been used to understand the structure of an ecological network, mostly because of data availability. Here, we provide a continental-scale, species-level metaweb for all tetrapods (mammals, breeding birds, reptiles and amphibians) occurring in Europe and in the Northern Mediterranean basin. This metaweb is based on data extracted from the scientific literature, including published papers, books and grey literature. Main type of variable contained For each species considered, we built the network of potential two-way trophic interactions. Spatial location and grain We considered all species occurring in the entire European subcontinent, from Macaronesia (including only the islands belonging politically to Spain and Portugal) to the Ural Mountains (west to east) and from Fennoscandia and U.K. islands to the Mediterranean (north to south). We included Turkey, geographically part of Asia, to provide a complete picture of the north-eastern Mediterranean coast. Time period The data represent information published and/or collected during the last 50 years. Major taxa studied and level of measurement We focused our metaweb on terrestrial tetrapods occurring in the study area. Only species introduced in historical times and currently naturalized were considered; new introductions were excluded. In total, we included 288 mammals, 509 regularly breeding birds, 250 reptiles and 104 amphibians. Software format Data are supplied as semicolon-separated text files.;,citation_author=Luigi Maiorano;,citation_author=Alessandro Montemaggiori;,citation_author=Gentile Francesco Ficetola;,citation_author=Louise O’Connor;,citation_author=Wilfried Thuiller;,citation_publication_date=2020-09;,citation_cover_date=2020-09;,citation_year=2020;,citation_issue=9;,citation_doi=10.1111/geb.13138;,citation_issn=1466-8238;,citation_volume=29;,citation_language=en-US;,citation_journal_title=Global Ecology and Biogeography;">
 <meta name="citation_reference" content="citation_title=The propagation of disturbances in ecological networks;,citation_author=Lucas P. Martins;,citation_author=David Garcia-Callejas;,citation_author=Hao Ran Lai;,citation_author=Kate L. Wootton;,citation_author=Jason M. Tylianakis;,citation_publication_date=2024-02;,citation_cover_date=2024-02;,citation_year=2024;,citation_issue=0;,citation_doi=10.1016/j.tree.2024.01.009;,citation_issn=0169-5347;,citation_pmid=38402007;,citation_volume=0;,citation_language=en-US;,citation_journal_title=Trends in Ecology &amp;amp;amp; Evolution;,citation_publisher=Elsevier;">
 <meta name="citation_reference" content="citation_title=Comparison of the predicted and observed secondary structure of T4 phage lysozyme;,citation_abstract=Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both $\beta$-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140–142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.;,citation_author=B. W. Matthews;,citation_publication_date=1975-10;,citation_cover_date=1975-10;,citation_year=1975;,citation_issue=2;,citation_doi=10.1016/0005-2795(75)90109-9;,citation_issn=0005-2795;,citation_volume=405;,citation_journal_title=Biochimica et Biophysica Acta (BBA) - Protein Structure;">
@@ -375,7 +376,7 @@ <h1 class="title">Unveiling the Complexity of Food Webs: A Comprehensive Overvie
                 <div>
             <div class="quarto-title-meta-heading">Published</div>
             <div class="quarto-title-meta-contents">
-              <p class="date">November 14, 2024</p>
+              <p class="date">November 15, 2024</p>
             </div>
           </div>
           
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diff --git a/notebooks/model_descriptions-preview.html b/notebooks/model_descriptions-preview.html
index 0a808ad..5645c35 100644
--- a/notebooks/model_descriptions-preview.html
+++ b/notebooks/model_descriptions-preview.html
@@ -5,7 +5,7 @@
 
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
-    <meta name="dcterms.date" content="2024-11-14">
+    <meta name="dcterms.date" content="2024-11-15">
 
     <title>SuppMat 1: Model Families Extended Description</title>
     <style>
@@ -182,7 +182,7 @@ <h1 class="title">SuppMat 1: Model Families Extended Description</h1>
                 <div>
             <div class="quarto-title-meta-heading">Published</div>
             <div class="quarto-title-meta-contents">
-              <p class="date">November 14, 2024</p>
+              <p class="date">November 15, 2024</p>
             </div>
           </div>
           
diff --git a/notebooks/model_qualitative-preview.html b/notebooks/model_qualitative-preview.html
index ca7f0d3..4a3b719 100644
--- a/notebooks/model_qualitative-preview.html
+++ b/notebooks/model_qualitative-preview.html
@@ -5,7 +5,7 @@
 
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
-    <meta name="dcterms.date" content="2024-11-14">
+    <meta name="dcterms.date" content="2024-11-15">
 
     <title>SuppMat 2: Looking at the ‘traits’ of the different model families</title>
     <style>
@@ -218,7 +218,7 @@ <h1 class="title">SuppMat 2: Looking at the ‘traits’ of the different model
                 <div>
             <div class="quarto-title-meta-heading">Published</div>
             <div class="quarto-title-meta-contents">
-              <p class="date">November 14, 2024</p>
+              <p class="date">November 15, 2024</p>
             </div>
           </div>
           
diff --git a/notebooks/model_qualitative.embed.ipynb b/notebooks/model_qualitative.embed.ipynb
index b532f66..d4337cf 100644
--- a/notebooks/model_qualitative.embed.ipynb
+++ b/notebooks/model_qualitative.embed.ipynb
@@ -7,7 +7,7 @@
     "# SuppMat 2: Looking at the ‘traits’ of the different model families\n",
     "\n",
     "Tanya Strydom [![](data:image/png;base64,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)](https://orcid.org/0000-0001-6067-1349)  \n",
-    "November 14, 2024\n",
+    "November 15, 2024\n",
     "\n",
     "The purpose of this document is to provide a rationalisation but also ‘formalisation’ of the different model families of sorts. This starts of by trying to construct a ‘traits’ table that focuses on what the resulting network is (is it a prediction of structure or is it a prediction of interactions), how the resulting network is encoded (*i.e.,* its anatomy) as well as trying to identify what type of data are needed to ‘parametrise’ the model. Finally we take this traits table and feed it into a hierarchical clustering model to sort of see how our broader *a priori* ideas of models being either topology generators or interaction predictors.\n",
     "\n",
@@ -29,7 +29,7 @@
     "\n",
     "Eight. What are the data sources needed for each model."
    ],
-   "id": "612b9bc3-b6c9-4688-8d4b-aea4107d5cd0"
+   "id": "b27f9299-12a0-44de-a3d1-2e982bae25ac"
   },
   {
    "cell_type": "code",
@@ -56,7 +56,7 @@
     "# this is the clustering code\n",
     "source(\"scripts/hierarchy_cluster.R\")"
    ],
-   "id": "50a35c41-14c8-4273-8892-4bccff16553f"
+   "id": "a2132f17-1ae9-4429-9a16-6f53e57d94f3"
   },
   {
    "cell_type": "markdown",
@@ -66,7 +66,7 @@
     "\n",
     "We can visualise these eight ‘character traits’ as a table"
    ],
-   "id": "0f45169a-05fa-4674-b34c-077488f43992"
+   "id": "1a2941ec-a72c-4bf1-a545-7800642c6167"
   },
   {
    "cell_type": "code",
@@ -1298,7 +1298,7 @@
     "    columns = everything()) %>% \n",
     "  opt_row_striping(row_striping = FALSE)"
    ],
-   "id": "fdafcff3-ab2f-40a9-8c39-301bb183853a"
+   "id": "1c224a77-5859-498f-b0de-c5805c94a385"
   },
   {
    "cell_type": "markdown",
@@ -1315,7 +1315,7 @@
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     }
    },
-   "id": "9d99858a-c1f0-4295-94d7-fe28cc60a7a7"
+   "id": "18cab0ab-4215-40b2-8770-bde395bdb51f"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/model_qualitative.out.ipynb b/notebooks/model_qualitative.out.ipynb
index 145aba0..7c0188c 100644
--- a/notebooks/model_qualitative.out.ipynb
+++ b/notebooks/model_qualitative.out.ipynb
@@ -7,7 +7,7 @@
     "# SuppMat 2: Looking at the ‘traits’ of the different model families\n",
     "\n",
     "Tanya Strydom [![](data:image/png;base64,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)](https://orcid.org/0000-0001-6067-1349)  \n",
-    "November 14, 2024\n",
+    "November 15, 2024\n",
     "\n",
     "The purpose of this document is to provide a rationalisation but also ‘formalisation’ of the different model families of sorts. This starts of by trying to construct a ‘traits’ table that focuses on what the resulting network is (is it a prediction of structure or is it a prediction of interactions), how the resulting network is encoded (*i.e.,* its anatomy) as well as trying to identify what type of data are needed to ‘parametrise’ the model. Finally we take this traits table and feed it into a hierarchical clustering model to sort of see how our broader *a priori* ideas of models being either topology generators or interaction predictors.\n",
     "\n",
@@ -29,7 +29,7 @@
     "\n",
     "Eight. What are the data sources needed for each model."
    ],
-   "id": "557121b4-eda3-4d15-8051-fa69a46fb318"
+   "id": "1a3ec3d9-c249-4c2c-9ad4-f12fea03a0af"
   },
   {
    "cell_type": "code",
@@ -54,7 +54,7 @@
     "# this is the clustering code\n",
     "source(\"scripts/hierarchy_cluster.R\")\n"
    ],
-   "id": "214517d5-c117-4d3c-b01c-a37f4941c113"
+   "id": "22367171-ad3a-494d-8069-3b5e34a0788b"
   },
   {
    "cell_type": "markdown",
@@ -64,7 +64,7 @@
     "\n",
     "We can visualise these eight ‘character traits’ as a table"
    ],
-   "id": "fcf4c230-69d2-49c6-b3bb-4a3aace3f2e3"
+   "id": "2b1048f9-a5ae-4742-8e0a-dfe2616d8b74"
   },
   {
    "cell_type": "code",
@@ -1292,7 +1292,7 @@
     "    columns = everything()) %>% \n",
     "  opt_row_striping(row_striping = FALSE)\n"
    ],
-   "id": "e92ff5de-614c-4364-8cfe-329fcb14201c"
+   "id": "bfe25336-f491-482b-9887-f08af7e6bf38"
   },
   {
    "cell_type": "markdown",
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     }
    },
-   "id": "57566c9f-47c0-4106-a0e1-f9450b8b34be"
+   "id": "b1594d47-d58f-4aad-9fbe-5d760109951b"
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diff --git a/notebooks/model_quantitative-preview.html b/notebooks/model_quantitative-preview.html
index 5cb97d8..8d796de 100644
--- a/notebooks/model_quantitative-preview.html
+++ b/notebooks/model_quantitative-preview.html
@@ -5,7 +5,7 @@
 
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
-    <meta name="dcterms.date" content="2024-11-14">
+    <meta name="dcterms.date" content="2024-11-15">
 
     <title>SuppMat 3: Benchmarking the different model families</title>
     <style>
@@ -219,7 +219,7 @@ <h1 class="title">SuppMat 3: Benchmarking the different model families</h1>
                 <div>
             <div class="quarto-title-meta-heading">Published</div>
             <div class="quarto-title-meta-contents">
-              <p class="date">November 14, 2024</p>
+              <p class="date">November 15, 2024</p>
             </div>
           </div>
           
diff --git a/notebooks/model_quantitative.embed.ipynb b/notebooks/model_quantitative.embed.ipynb
index 46ac705..0eae3f2 100644
--- a/notebooks/model_quantitative.embed.ipynb
+++ b/notebooks/model_quantitative.embed.ipynb
@@ -7,7 +7,7 @@
     "# SuppMat 3: Benchmarking the different model families\n",
     "\n",
     "Tanya Strydom [![](data:image/png;base64,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)](https://orcid.org/0000-0001-6067-1349)  \n",
-    "November 14, 2024\n",
+    "November 15, 2024\n",
     "\n",
     "The purpose is to act as an ‘extended data and methods’ section as well as to further describe the benchmarking process in terms of which metrics we end up using etc.\n",
     "\n",
@@ -97,7 +97,7 @@
     "\n",
     "## Visualising results"
    ],
-   "id": "eaf889b5-3547-4446-9f5a-3105252ed460"
+   "id": "d0a48b09-ade2-492e-8add-81a603e3afe0"
   },
   {
    "cell_type": "code",
@@ -121,7 +121,7 @@
     "# this is the clustering code\n",
     "source(\"scripts/benchmarking.R\")"
    ],
-   "id": "036fbee4-b51b-454a-9c96-f8ab3796c179"
+   "id": "8c34cba6-338d-4520-893a-64ff1278556a"
   },
   {
    "cell_type": "markdown",
@@ -145,7 +145,7 @@
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FijDo1auX5fkA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AAGklozsTzVqzZs03v/nNxYsX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\nW9tx1Je+9KUzzzwz4WEA4jK0M7F+/fo777xz79694c3+/fvfeuutw4YNa+W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     }
    },
-   "id": "6fa1fe78-8eb2-4225-84fe-d422a258ce37"
+   "id": "ca68069d-038d-4616-af58-46ce498d4b00"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/model_quantitative.out.ipynb b/notebooks/model_quantitative.out.ipynb
index 318eb7d..a56a2c9 100644
--- a/notebooks/model_quantitative.out.ipynb
+++ b/notebooks/model_quantitative.out.ipynb
@@ -7,7 +7,7 @@
     "# SuppMat 3: Benchmarking the different model families\n",
     "\n",
     "Tanya Strydom [![](data:image/png;base64,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)](https://orcid.org/0000-0001-6067-1349)  \n",
-    "November 14, 2024\n",
+    "November 15, 2024\n",
     "\n",
     "The purpose is to act as an ‘extended data and methods’ section as well as to further describe the benchmarking process in terms of which metrics we end up using etc.\n",
     "\n",
@@ -97,7 +97,7 @@
     "\n",
     "## Visualising results"
    ],
-   "id": "646b0b6c-66f9-4805-b1e8-cc92bba1baea"
+   "id": "e901b30e-872d-449e-870b-2c7c6dd9e6be"
   },
   {
    "cell_type": "code",
@@ -121,7 +121,7 @@
     "# this is the clustering code\n",
     "source(\"scripts/benchmarking.R\")\n"
    ],
-   "id": "e275d649-482b-4eb2-b660-f9f3fff312f4"
+   "id": "6d098eab-c630-4e8a-864b-4930de398287"
   },
   {
    "cell_type": "markdown",
@@ -169,7 +169,7 @@
      "image/png": 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FijDo1auX5fkA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AAGklozsTzVqzZs03v/nNxYsX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\nW9tx1Je+9KUzzzwz4WEA4jK0M7F+/fo777xz79694c3+/fvfeuutw4YNa+W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