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2 changes: 1 addition & 1 deletion .nojekyll
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152 changes: 112 additions & 40 deletions _tex/index.tex
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\makeatletter
\def\fps@figure{htbp}
\makeatother
% definitions for citeproc citations
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% allow citations to break across lines
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\def\@cite#1#2{{#1\if@tempswa , #2\fi}}
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\newcommand{\CSLIndent}[1]{\hspace{\cslhangindent}#1}

\usepackage{url} %this package should fix any errors with URLs in refs.
\usepackage{lineno}
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\title{Omnomnomnivores}

\authors{Tanya Strydom\affil{1}, Timothée Poisot\affil{2,3}}
\affiliation{1}{Curvenote, }\affiliation{2}{Université de
\affiliation{1}{Uum??, }\affiliation{2}{Université de
Montreal, }\affiliation{3}{Québec Centre for Biodiversity Sciences, }
\correspondingauthor{Timothée Poisot}{timothee.poisot@umontreal.ca}


\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{}
TODO
\end{abstract}

\section*{Plain Language Summary}
Earthquake data for the island of La Palma from the September 2021
eruption is found \ldots{}



Expand All @@ -173,28 +199,28 @@ \section{Data \& Methods}\label{sec-data-methods}

\subsection{Metacommunity model}\label{metacommunity-model}

The metacommunity model developed by (\textbf{Thompson2017Dispersala?})
is a good starting point to use for this `case study' as it allows us
some flexibility with how we want to parameterise the system. The model
(Equation~\ref{eq-metacomm}) itself is based on a tritrophic community
(`plants', `herbivores', and `carnivores') and is a collection of
modified Lotka--Volterra equations and (broadly) models species
abundance as a function of interaction strength, environmental effect,
immigration, and emigration. The metacommunity consists of \(S\) species
with \(M\) environmental patches and looks as follows:
The model used broadly follows the metacommunity model developed by
Thompson \& Gonzalez (2017). The model (Equation~\ref{eq-metacomm})
itself is based on a tritrophic community (`plants', `herbivores', and
`carnivores'), and is essentially a collection of modified
Lotka--Volterra equations, this (broadly) models species abundance as a
function of interaction strength, environmental effect, immigration, and
emigration. The metacommunity consists of \(S\) species with \(M\)
environmental patches in the landscape and looks as follows:

\begin{equation}\phantomsection\label{eq-metacomm}{
X_{ij}(t+1)=X_{ij}(t)exp\left[C_{i} + \sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\right]+I_{ij}(t)-X_{ij}(t)a_{i}
}\end{equation}

Where \(X_{ij}(t)\) is the abundance of species \(i\) in patch \(j\) at
time \(t\). \(C_i\) is its intrinsic rate of increase (which we have set
to 0.1 for `plants' and -0.01 for `herbivores' and `carnivores').
to 0.1 for `plants' and -0.001 for `herbivores' and `carnivores').
\(B_{ik}\) is the per capita effect of species \(k\) on species \(i\).
The exact interaction strength for each species pair is drawn from a
uniform distribution with the parameters for the interaction pairs
listed in Table~\ref{tbl-interaction_strength}, the values drawn from
the uniform distribution are scaled by dividing by \(0.33S\) to yield
The exact interaction strength for each species pair is determined by
the trophic level of each species and is drawn from a uniform
distribution. The ranges for each combination of species pairs listed in
Table~\ref{tbl-interaction_strength}, the values that are drawn from the
uniform distribution are then scaled by dividing by \(0.33S\) to yield
the final interaction strength for each interacting pair.

\begin{longtable}[]{@{}lc@{}}
Expand All @@ -218,7 +244,7 @@ \subsection{Metacommunity model}\label{metacommunity-model}
Plant-herbivore & 0.0 -- 0.10 \\
Plant-carnivore & 0.0 \\
Herbivore-plant & -0.3 -- 0.00 \\
Herbivore-herbivore & -0.2-- -0.15 \\
Herbivore-herbivore & -0.2 -- -0.15 \\
Herbivore-carnivore & 0.0 -- 0.08 \\
Carnivore-plant & 0.0 \\
Carnivore-herbivore & -0.1 -- 0.00 \\
Expand All @@ -228,20 +254,16 @@ \subsection{Metacommunity model}\label{metacommunity-model}
\(A_{ij}(t)\) is the effect of the environment in patch \(j\) on species
\(i\) at time \(t\) and can be further expanded as follows:

\[
\hat{A}_{ij}(t)=h\times\frac{1}{\sigma\sqrt{2\pi}}\exp-\frac{1}{2}\left(\frac{E_{j}(t)-H_{i}}{\sigma}\right)^2
\]

\begin{equation}\phantomsection\label{eq-metacomm_env}{
A_{ij}(t)= \hat{A}_{ij}(t) - max(\hat{A})
A_{ij}(t)=\left(h\times\frac{1}{\sigma\sqrt{2\pi}}\right)\times\left(e^{-\left(E_{j}(t)-H_{i}\right)^2/{2\sigma}^2}-1\right)
}\end{equation}

Species environmental optima (\(H_i\)) are evenly distributed across the
entire range of environmental conditions for each trophic level, meaning
that species from different trophic levels will be at, or near the same
environmental optima. \(h\) is a scaling parameter (set to 300),
\(E_j(t)\) is the environment in patch \(j\) at time \(t\) and
\(\sigma\) is the standard deviation (set to 50).
Where the species environmental optima (\(H_i\)) are evenly distributed
across the entire range of environmental conditions for each trophic
level, meaning that species from different trophic levels will be at, or
near the same environmental optima. \(h\) is a scaling parameter (set to
\textbf{50}), \(E_j(t)\) is the environment in patch \(j\) at time \(t\)
and \(\sigma\) is the standard deviation (set to \textbf{50}).

\(I_{ij}(t)\) is the abundance of species \(i\) immigrating to patch
\(j\) at time \(t\) and can be expanded as follows:
Expand Down Expand Up @@ -281,8 +303,39 @@ \subsection{Metacommunity model}\label{metacommunity-model}

\subsection{Generating networks}\label{generating-networks}

More info on the baking process and the various connectivity stuff and
whatnot
In order to create a final community state the species are allowed to
persist for a total of 2000 generations. These generations are broken
down into three `phases' the first is the `proofing' phase where the
environment is uniform throughout the landscape (meaning that all
species are at their environmental optimum) for 500 generations. After
this the environment is `heated' incrementally until it reaches its
`final state', the environmental optimum of each species is also
adjusted as the environmental values begin to change. This occurs over a
period of 1 000 generations. The landscape is then held stable for a
further 500 generations until an equilibrium is reached. The final state
of the landscape is predetermined and is defined by the diamond-square
algorithm (this produces fractals with variable spatial autocorrelation)
which is generated using \texttt{NeutralLandscapes.jl} (Catchen, 2023),
here we vary the degree of landscape heterogeneity by \textbf{TODO}.

\begin{longtable}[]{@{}lc@{}}
\caption{Starting parameters for the
model.}\label{tbl-model_params}\tabularnewline
\toprule\noalign{}
Parameter & Value \\
\midrule\noalign{}
\endfirsthead
\toprule\noalign{}
Parameter & Value \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
\(S\) & 100 \\
\(M\) & 26*26 \\
\(E_{initial}\) & 40 \\
\(A_{initial}\) & 0.01 \\
\end{longtable}

\subsection{Spatial wombling}\label{spatial-wombling}

Expand All @@ -291,7 +344,7 @@ \subsection{Spatial wombling}\label{spatial-wombling}
(\(m\)) and corresponding direction (\(\theta\)) of change. This is done
by using first-order partial derivative (\(\partial\)) of the
`curvature' of the landscape as described by \(f(x,y)\) (see
Equation~\ref{eq-womble}). This essentially gives an indiaction how
Equation~\ref{eq-womble}). This essentially gives an indication how
steep the gradient (\(m\)) is between neighbouring cells as well as the
direction (\(\theta\)) of the slope.

Expand All @@ -300,8 +353,8 @@ \subsection{Spatial wombling}\label{spatial-wombling}
}\end{equation}

The spatial wombling analyses were done using
\texttt{SpatialBoundaries.jl} (\textbf{Strydom2023Spatialboundariesa?}).
The docuemntation provides a more detailed breakdown of the underlying
\texttt{SpatialBoundaries.jl} (Strydom \& Poisot, 2023). The
documentation provides a more detailed breakdown of the underlying
methodology.

\section{Conclusion}\label{conclusion}
Expand All @@ -315,6 +368,25 @@ \section*{References}\label{references}
\href{https://PoisotLab.github.io/ms_womble_ya_net/index.qmd.html}{Article
Notebook}}

\phantomsection\label{refs}
\begin{CSLReferences}{1}{0}
\bibitem[\citeproctext]{ref-catchenEcoJuliaNeutralLandscapesJl2023}
Catchen, M. D. (2023, December). {EcoJulia}/{NeutralLandscapes}.jl.
EcoJulia.

\bibitem[\citeproctext]{ref-strydomSpatialBoundariesJlEdge2023}
Strydom, T., \& Poisot, T. (2023). {SpatialBoundaries}.jl: Edge
detection using spatial wombling. \emph{Ecography}, \emph{2023}(5),
e06609. \url{https://doi.org/10.1111/ecog.06609}

\bibitem[\citeproctext]{ref-thompsonDispersalGovernsReorganization2017}
Thompson, P. L., \& Gonzalez, A. (2017). Dispersal governs the
reorganization of ecological networks under environmental change.
\emph{Nature Ecology \& Evolution}, \emph{1}(6), 0162.
\url{https://doi.org/10.1038/s41559-017-0162}

\end{CSLReferences}



\end{document}
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