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\IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available
\urlstyle{same} % disable monospaced font for URLs
\hypersetup{
pdftitle={La Palma Earthquakes},
pdfauthor={Steve Purves; Rowan Cockett},
pdfkeywords={La Palma, Earthquakes},
pdftitle={T is for Topology},
pdfauthor={Tanya Strydom; Andrew P. Beckerman},
pdfkeywords={food web, network construction},
colorlinks=true,
linkcolor={blue},
filecolor={Maroon},
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\draftfalse

\begin{document}
\title{La Palma Earthquakes}
\title{T is for Topology}

\authors{Steve Purves\affil{1}, Rowan Cockett\affil{1}}
\affiliation{1}{Curvenote, }
\correspondingauthor{Steve Purves}{steve@curvenote.com}
\authors{Tanya Strydom\affil{1}, Andrew P. Beckerman\affil{1}}
\affiliation{1}{University of Sheffield, }
\correspondingauthor{Tanya Strydom}{t.strydom@sheffield.ac.uk}


\begin{abstract}
In September 2021, a significant jump in seismic activity on the island
of La Palma (Canary Islands, Spain) signaled the start of a volcanic
crisis that still continues at the time of writing. Earthquake data is
continually collected and published by the Instituto Geográphico
Nacional (IGN). \ldots{}
Pending\ldots{}
\end{abstract}

\section*{Plain Language Summary}
Earthquake data for the island of La Palma from the September 2021
eruption is found \ldots{}
We want to know a bit more about the different network topology
generators (predict tools) and how they differ - \emph{i.e.,} their
strengths and weaknesses



\section{Introduction}\label{introduction}

\textsubscript{Source:
\href{https://BecksLab.github.io/ms_t_is_for_topology/index.qmd.html}{Article
Notebook}}

\textsubscript{Source:
\href{https://BecksLab.github.io/ms_t_is_for_topology/index.qmd.html}{Article
Notebook}}

\begin{figure}[H]

\centering{

\includegraphics{index_files/figure-pdf/fig-timeline-output-1.pdf}

}

\caption{\label{fig-timeline}Timeline of recent earthquakes on La Palma}

\end{figure}%

\textsubscript{Source:
\href{https://BecksLab.github.io/ms_t_is_for_topology/index.qmd.html}{Article
Notebook}}

\textsubscript{Source:
\href{https://BecksLab.github.io/ms_t_is_for_topology/index.qmd.html}{Article
Notebook}}

Based on data up to and including 1971, eruptions on La Palma happen
every 79.8 years on average.

Studies of the magma systems feeding the volcano, such as Marrero et al.
(2019), have proposed that there are two main magma reservoirs feeding
the Cumbre Vieja volcano; one in the mantle (30-40km depth) which
charges and in turn feeds a shallower crustal reservoir (10-20km depth).

Eight eruptions have been recorded since the late 1400s
(Figure~\ref{fig-timeline}).
The standard run of the mill that we cannot always feasibly make network
predictions because 1. hard, 2. time (prehistoric mostly), and 3.
probably something else meaningful that's just slipping my mind at the
moment.

Maybe a brief history of the development of predictive tools? Sort of
where the theory/body of work was based and how that has changed?

Maybe start here already about discussing the core mechanistic
differences that models will work at - some are really concerned (and
thus constrained by) structure, others are more mechanistic in nature
\emph{i.e.,} species \emph{a} had the capacity to eat species \emph{b},
and then you get Rohr et al. (2010) and Strydom et al. (2022) that sit
in the weird latent space\ldots{}

At some point we are going to need to discuss the key difference and
implication between a metaweb and a network realisation.

\begin{quote}
Do we need to delve into individual-based networks? (\emph{sensu} Tinker
2012, Araújo 2008) I think its probably a step too far and one starts
delving into apples and pears type of comparisons. Especially since
these work off of already existing networks and its more about about
`tweaking' those - so not so much \emph{de novo} predictions. Although
this might be useful to keep in mind when it comes to re-wiring\ldots{}
Also on that note do we opn the re-wiring door here in this ms or wait
it out a bit.
\end{quote}

Data and methods are discussed in Section~\ref{sec-data-methods}.

Let \(x\) denote the number of eruptions in a year. Then, \(x\) can be
modeled by a Poisson distribution
\section{Data \& Methods}\label{sec-data-methods}

\begin{equation}\phantomsection\label{eq-poisson}{
p(x) = \frac{e^{-\lambda} \lambda^{x}}{x !}
}\end{equation}
\subsection{Overview of topology
generators}\label{overview-of-topology-generators}

where \(\lambda\) is the rate of eruptions per year. Using
Equation~\ref{eq-poisson}, the probability of an eruption in the next
\(t\) years can be calculated.
I know table are awful but in this case they may make more sense

\begin{longtable}[]{@{}ll@{}}
\caption{Recent historic eruptions on La
Palma}\label{tbl-history}\tabularnewline
\begin{longtable}[]{@{}
>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.2235}}
>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.5412}}
>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.2353}}@{}}
\caption{Lets make a table that gives an overview of the different
topology generators that we will look
at}\label{tbl-history}\tabularnewline
\toprule\noalign{}
Name & Year \\
\begin{minipage}[b]{\linewidth}\raggedright
Model
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Reference
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Core Mechanism
\end{minipage} \\
\midrule\noalign{}
\endfirsthead
\toprule\noalign{}
Name & Year \\
\begin{minipage}[b]{\linewidth}\raggedright
Model
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Reference
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Core Mechanism
\end{minipage} \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
Current & 2021 \\
Teneguía & 1971 \\
Nambroque & 1949 \\
El Charco & 1712 \\
Volcán San Antonio & 1677 \\
Volcán San Martin & 1646 \\
Tajuya near El Paso & 1585 \\
Montaña Quemada & 1492 \\
Niche model & Cohen et al. (1997) & structural \\
Cascade model & Williams \& Martinez (2000) & structural \\
PFIM & Shaw et al. (2024) & mechanistic \\
Log-ratio & Rohr et al. (2010) & latent trait space \\
Nested hierarchy & Cattin et al. (2004) & \\
ADBM & Petchey et al. (2008) & mechanistic \\
Stochastic & Rossberg et al. (2006) & \\
Transfer learning & Strydom et al. (2022) & latent trait space \\
Trait-based & Caron et al. (2022) & mechanistic \\
\end{longtable}

Table~\ref{tbl-history} summarises the eruptions recorded since the
colonization of the islands by Europeans in the late 1400s.

\begin{figure}

\centering{

\includegraphics{images/la-palma-map.png}

}

\caption{\label{fig-map}Map of La Palma}

\end{figure}%

La Palma is one of the west most islands in the Volcanic Archipelago of
the Canary Islands (Figure~\ref{fig-map}).

\begin{figure}[H]

\centering{

\includegraphics{index_files/figure-latex/notebooks-data-screening-fig-spatial-plot-output-1.png}

}

\caption{\label{fig-spatial-plot}Locations of earthquakes on La Palma
since 2017.}

\end{figure}%

\textsubscript{Source:
\href{https://BecksLab.github.io/ms_t_is_for_topology/index.qmd.html}{Article
Notebook}}

Figure~\ref{fig-spatial-plot} shows the location of recent Earthquakes
on La Palma.

\section{Data \& Methods}\label{sec-data-methods}
\subsection{Datasets used}\label{datasets-used}

Here I think we need to span a variety of domains, at minimum aquatic
and terrestrial but maybe there should be a `scale' element as well
\emph{i.e.,} a regional and local network. I think there is going to be
a `turning point' where structural will take over from mechanistic in
terms of performance. More specifically at local scales bioenergetic
constraints (and co-occurrence) may play a bigger role in structuring a
network whereas at the metaweb level then mechanistic may make more
(since by default its about who can potentially interact and obviously
not constrained by real-world scenarios) \emph{sensu} Caron et al.
(2023)

\section{Results}\label{results}

How we want to compare and contrast. I think there won't be a `winner'
and thus we need to think of `tests' that are going to measure
performance in different situations/settings. With that in mind I think
some valuable points to consider would be:

\begin{itemize}
\tightlist
\item
Structural vs pairwise link predictions (graph vs node level)

\begin{itemize}
\tightlist
\item
\% of links correctly retrieved
\item
connectence
\item
trophic level
\item
generalism vs specialism
\item
something related to false positives/negatives
\end{itemize}
\item
Data `cost' (some methods might need a lot lot of supporting data vs
something very light weight)
\item
I think it would be remiss to also take into consideration
computational cost
\end{itemize}

\section{Conclusion}\label{conclusion}

Expand All @@ -329,12 +333,65 @@ \section*{References}\label{references}

\phantomsection\label{refs}
\begin{CSLReferences}{1}{0}
\bibitem[\citeproctext]{ref-marrero2019}
Marrero, J., García, A., Berrocoso, M., Llinares, Á., Rodríguez-Losada,
A., \& Ortiz, R. (2019). Strategies for the development of volcanic
hazard maps in monogenetic volcanic fields: The example of {La} {Palma}
({Canary} {Islands}). \emph{Journal of Applied Volcanology}, \emph{8}.
\url{https://doi.org/10.1186/s13617-019-0085-5}
\bibitem[\citeproctext]{ref-caronAddressingEltonianShortfall2022}
Caron, D., Maiorano, L., Thuiller, W., \& Pollock, L. J. (2022).
Addressing the {Eltonian} shortfall with trait-based interaction models.
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\url{https://doi.org/10.1111/ele.13966}

\bibitem[\citeproctext]{ref-caronTrophicInteractionModels2023}
Caron, D., Brose, U., Lurgi, M., Blanchet, G., Gravel, D., \& Pollock,
L. J. (2023). Trophic interaction models predict interactions across
space, not food webs.

\bibitem[\citeproctext]{ref-cattinPhylogeneticConstraintsAdaptation2004}
Cattin, M.-F., Bersier, L.-F., Banašek-Richter, C., Baltensperger, R.,
\& Gabriel, J.-P. (2004). Phylogenetic constraints and adaptation
explain food-web structure. \emph{Nature}, \emph{427}(6977), 835--839.
\url{https://doi.org/10.1038/nature02327}

\bibitem[\citeproctext]{ref-cohenStochasticTheoryCommunity1997}
Cohen, J. E., Newman, C. M., \& Steele, J. H. (1997). A stochastic
theory of community food webs {I}. {Models} and aggregated data.
\emph{Proceedings of the Royal Society of London. Series B. Biological
Sciences}, \emph{224}(1237), 421--448.
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\bibitem[\citeproctext]{ref-petcheySizeForagingFood2008}
Petchey, O. L., Beckerman, A. P., Riede, J. O., \& Warren, P. H. (2008).
Size, foraging, and food web structure. \emph{Proceedings of the
National Academy of Sciences}, \emph{105}(11), 4191--4196.
\url{https://doi.org/10.1073/pnas.0710672105}

\bibitem[\citeproctext]{ref-rohrModelingFoodWebs2010}
Rohr, R. P., Scherer, H., Kehrli, P., Mazza, C., \& Bersier, L.-F.
(2010). Modeling {Food Webs}: {Exploring Unexplained Structure Using
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\bibitem[\citeproctext]{ref-rossbergFoodWebsExperts2006}
Rossberg, A. G., Matsuda, H., Amemiya, T., \& Itoh, K. (2006). Food
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\url{https://doi.org/10.1016/j.jtbi.2005.12.021}

\bibitem[\citeproctext]{ref-shawFrameworkReconstructingAncient2024}
Shaw, J. O., Dunhill, A. M., Beckerman, A. P., Dunne, J. A., \& Hull, P.
M. (2024, January). A framework for reconstructing ancient food webs
using functional trait data. {bioRxiv}.
\url{https://doi.org/10.1101/2024.01.30.578036}

\bibitem[\citeproctext]{ref-strydomFoodWebReconstruction2022}
Strydom, T., Bouskila, S., Banville, F., Barros, C., Caron, D., Farrell,
M. J., et al. (2022). Food web reconstruction through phylogenetic
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\url{https://doi.org/10.1111/2041-210X.13835}

\bibitem[\citeproctext]{ref-williamsSimpleRulesYield2000}
Williams, R. J., \& Martinez, N. D. (2000). Simple rules yield complex
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\url{https://doi.org/10.1038/35004572}

\end{CSLReferences}

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