✨ bits and bobs

docs/_tex/index.tex

@@ -219,7 +219,7 @@
 1%
 }%
 }
-\date{2024-06-06}
+\date{2024-06-07}
 
 \usepackage{setspace}
 \usepackage[left]{lineno}
@@ -451,7 +451,7 @@ \subsection{Putting the parts together; what does it
 research programmes (or even practical needs) that have been driving the
 construction of them.
 
-\begin{tcolorbox}[enhanced jigsaw, bottomrule=.15mm, coltitle=black, opacityback=0, bottomtitle=1mm, breakable, rightrule=.15mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Box 1 - Mechanisms that determine feeding links}, leftrule=.75mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, arc=.35mm, titlerule=0mm, toprule=.15mm, opacitybacktitle=0.6]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Box 1 - Mechanisms that determine feeding links}, opacityback=0, bottomrule=.15mm, toprule=.15mm, titlerule=0mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, bottomtitle=1mm, arc=.35mm, coltitle=black, rightrule=.15mm, colbacktitle=quarto-callout-note-color!10!white, colframe=quarto-callout-note-color-frame, colback=white, breakable]
 
 There are many ideas as to what are the underlying mechanisms that
 determine the links between species. The way one chooses to encode a
@@ -709,7 +709,8 @@ \subsection{Model families}\label{model-families}
 (2016) (f you squint?) \\
 co-occurrence & co-occurrence patterns arise from interactions so we can
 use these patterns to reverse engineer the interactions & co-occurrence
-patterns & species & association links & binary & \\
+patterns & species & association links & binary & Kusch et al. (2023)
+(although more plant-plant \emph{i.e.} non-trophic\ldots) \\
 \end{longtable}
 
 \begin{figure}
@@ -726,7 +727,7 @@ \subsection{Model families}\label{model-families}
 
 \end{figure}%
 
-\begin{tcolorbox}[enhanced jigsaw, bottomrule=.15mm, coltitle=black, opacityback=0, bottomtitle=1mm, breakable, rightrule=.15mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Box 2 - Assessing model outputs}, leftrule=.75mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, arc=.35mm, titlerule=0mm, toprule=.15mm, opacitybacktitle=0.6]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Box 2 - Assessing model outputs}, opacityback=0, bottomrule=.15mm, toprule=.15mm, titlerule=0mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, bottomtitle=1mm, arc=.35mm, coltitle=black, rightrule=.15mm, colbacktitle=quarto-callout-note-color!10!white, colframe=quarto-callout-note-color-frame, colback=white, breakable]
 
 Although understanding the underlying philosophy of the different model
 families is beneficial it is also important to understand in what
@@ -1163,6 +1164,12 @@ \section*{References}\label{references}
 Jordano, P. (2016b). Sampling networks of ecological interactions.
 \emph{Functional Ecology}. \url{https://doi.org/10.1111/1365-2435.12763}
 
+\bibitem[\citeproctext]{ref-kuschEcologicalNetworkInference2023}
+Kusch, E., Bimler, M., Lutz, J. A., \& Ordonez, A. (2023).
+\emph{Ecological network inference is not consistent across scales or
+approaches} (p. 2023.07.13.548816). bioRxiv.
+\url{https://doi.org/10.1101/2023.07.13.548816}
+
 \bibitem[\citeproctext]{ref-lindemanTrophicDynamicAspectEcology1942}
 Lindeman, R. L. (1942). The {Trophic-Dynamic Aspect} of {Ecology}.
 \emph{Ecology}, \emph{23}(4), 399--417.

docs/_tex/references.bib

@@ -1047,6 +1047,42 @@ @article{krishnaNeutralnicheTheoryNestedness2008
   langid = {english}
 }
 
+@misc{kuschEcologicalNetworkInference2023,
+  title = {Ecological Network Inference Is Not Consistent across Scales or Approaches},
+  author = {Kusch, Erik and Bimler, Malyon and Lutz, James A. and Ordonez, Alejandro},
+  year = {2023},
+  month = jul,
+  primaryclass = {New Results},
+  pages = {2023.07.13.548816},
+  publisher = {bioRxiv},
+  doi = {10.1101/2023.07.13.548816},
+  urldate = {2024-06-07},
+  abstract = {Several methods of ecological network inference have been proposed, but their consistency and applicability for use across ecologically relevant scales require further investigation. Here, we infer ecological networks using two data sets (YFDP, FIA) describing distributional and attribute information at local, regional, and continental scales for woody species across North America. We accomplish this inference using four different methodologies (COOCCUR, NETASSOC, HMSC, NDD-RIM), incorporating biological data along an occurrence-performance spectrum while accounting (or not) for various confounding parameters. We contrast 1-1 associations at each evaluated scale to quantify consistency amongst inference approaches. We also assess consistency across scales within each inference approach. Ultimately, we find that inferred networks are inconsistent across scales and methodologies, particularly at continental scales. We highlight how such inconsistencies between network inference methods may be linked to using occurrence or performance information and incorporating or not confounding factors. Finally, we argue that identifying the ``best'' inference method is non-trivial. Thus, to facilitate the choice of inference methods for a given purpose, we suggest aligning specific research questions and the scale applicability of the method when interpreting the inferred links, network topology, and ecological processes governing network assembly.},
+  archiveprefix = {bioRxiv},
+  chapter = {New Results},
+  copyright = {{\copyright} 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
+  langid = {english},
+  file = {/Users/tanyastrydom/Zotero/storage/W2KJHDCA/Kusch et al. - 2023 - Ecological network inference is not consistent acr.pdf}
+}
+
+@misc{kuschNovelSimulationFramework2023,
+  title = {A {{Novel Simulation Framework}} for {{Validation}} of {{Ecological Network Inference}}},
+  author = {Kusch, Erik and Vinton, Anna C.},
+  year = {2023},
+  month = oct,
+  primaryclass = {New Results},
+  pages = {2023.08.05.552122},
+  publisher = {bioRxiv},
+  doi = {10.1101/2023.08.05.552122},
+  urldate = {2024-06-07},
+  abstract = {Understanding how the differential magnitude and sign of ecological interactions vary across space is vital to assessing ecosystem resilience to biodiversity loss and predict community assemblies. This necessity for ecological network knowledge and their labour-intensive sampling requirements has spurred the creation of ecological network inference methodology. Recent research has identified inconsistencies in networks inferred using different approaches thus necessitating quantification of inference performance to facilitate choice of network inference approach.Here we develop a data simulation method to generate data products fit for network inference and subsequently quantify the validity of two well-established ecological interaction network inference methods -- HMSC and COOCCUR. The simulation framework we present here can be parameterised using real-world information (e.g., biological interactions observed in-situ and bioclimatic niche preferences) thus representing network inference capabilities in real-world applications. Using this framework, it is thus possible to evaluate the performance of any ecological network inference approach.We identify a concerningly large range in accuracy of inferred networks as compared to true, realisable association networks. These differences in inference accuracy are governed by a paradigm of input data types and environmental parameter estimation as previously suggested. To establish a workflow for quantification of network inference reliability, we suggest analysis procedures with which to explore inference and detection probabilities of association types of different identity and sign with respect to bioclimatic niche preferences and association strength of association partner-species.With this study, we provide the groundwork with which to validate and compare ecological network inference methods, and ultimately vastly increase our ability to understand and predict species biodiversity across space and time.},
+  archiveprefix = {bioRxiv},
+  chapter = {New Results},
+  copyright = {{\copyright} 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
+  langid = {english},
+  file = {/Users/tanyastrydom/Zotero/storage/CFVKEZL3/Kusch and Vinton - 2023 - A Novel Simulation Framework for Validation of Eco.pdf}
+}
+
 @article{laigleSpeciesTraitsDrivers2018,
   title = {Species Traits as Drivers of Food Web Structure},
   author = {Laigle, Idaline and Aubin, Isabelle and Digel, Christoph and Brose, Ulrich and Boulangeat, Isabelle and Gravel, Dominique},
@@ -1201,6 +1237,23 @@ @article{ohlmannMappingImprintBiotic2018
   file = {/Users/tanyastrydom/Zotero/storage/RUPS3ATP/Ohlmann et al. - 2018 - Mapping the imprint of biotic interactions on β-di.pdf;/Users/tanyastrydom/Zotero/storage/K3R3BHFT/ele.html}
 }
 
+@article{ovaskainenUsingLatentVariable2016,
+  title = {Using Latent Variable Models to Identify Large Networks of Species-to-Species Associations at Different Spatial Scales},
+  author = {Ovaskainen, Otso and Abrego, Nerea and Halme, Panu and Dunson, David},
+  year = {2016},
+  journal = {Methods in Ecology and Evolution},
+  volume = {7},
+  number = {5},
+  pages = {549--555},
+  issn = {2041-210X},
+  doi = {10.1111/2041-210X.12501},
+  urldate = {2024-06-07},
+  abstract = {We present a hierarchical latent variable model that partitions variation in species occurrences and co-occurrences simultaneously at multiple spatial scales. We illustrate how the parameterized model can be used to predict the occurrences of a species by using as predictors not only the environmental covariates, but also the occurrences of all other species, at all spatial scales. We leverage recent progress in Bayesian latent variable models to implement a computationally effective algorithm that enables one to consider large communities and extensive sampling schemes. We exemplify the framework with a community of 98 fungal species sampled in c. 22 500 dead wood units in 230 plots in 29 beech forests. The networks identified by correlations and partial correlations were consistent, as were networks for natural and managed forests, but networks at different spatial scales were dissimilar. Accounting for the occurrences of the other species roughly doubled the predictive powers of the models compared to accounting for environmental covariates only .},
+  copyright = {{\copyright} 2015 The Authors. Methods in Ecology and Evolution {\copyright} 2015 British Ecological Society},
+  langid = {english},
+  keywords = {biotic interaction,co-occurrence,correlation,hierarchical model,joint species distribution model,partial correlation}
+}
+
 @article{parkStatisticalMechanicsNetworks2004,
   title = {Statistical Mechanics of Networks},
   author = {Park, Juyong and Newman, M. E. J.},