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\begin{document}

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\CDRsetmeta{articletype}{opinion}

\title{ORCHAMP: an observation network for monitoring biodiversity and
ecosystem functioning across space and time in mountainous regions}

\alttitle{ORCHAMP : un observatoire \`{a} long terme d\'{e}di\'{e} au
suivi de la biodiversit\'{e} et du fonctionnement des
\'{e}cosyst\`{e}mes au travers des r\'{e}gions de montagnes}

\author{\firstname{Wilfried} \lastname{Thuiller}\CDRorcid{0000-0002-5388-5274}\IsCorresp}
\address{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA,
F-38000 Grenoble, France}
\curraddr[W. Thuiller]{Laboratoire d'Ecologie Alpine (LECA), UMR
CNRS-UGA-USMB 5553, Universit\'e Grenoble Alpes, CS 40700, 38058 Grenoble
cedex 9, France}
\email[W. Thuiller]{Wilfried.thuiller@univ-grenoble-alpes.fr}

\author{\firstname{Am\'{e}lie} \lastname{Saillard}\CDRorcid{0000-0002-7777-8211}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[A. Saillard]{amelie.saillard@univ-grenoble-alpes.fr}

\author{\firstname{Sylvain} \lastname{Abdulhak}\CDRorcid{0000-0002-3498-1404}}
\address{Alpine National Botanical Conservatory, CBNA, Domaine de
Charance, Gap, France}
\email[S. Abdulhak]{s.abdulhak@cbn-alpin.fr}

\author{\firstname{Vincent} \lastname{Aug\'{e}}}
\address{Parc national de la Vanoise, F-73000 Chamb\'{e}ry, France}
\email[V. Aug\'{e}]{vincent.auge@vanoise-parcnational.fr}

\author{\firstname{Carole} \lastname{~Birck}}
\address{Asters-CEN74, Manoir de Novel, 60 avenue de Novel, 74 000
Annecy, France}
\email[C. Birck]{carole.birck@cen-haute-savoie.org}

\author{\firstname{Richard} \lastname{Bonet}}
\address{Parc national des \'{E}crins, Domaine de Charance, Gap,
France}
\email[R. Bonet]{richard.bonet@ecrins-parcnational.fr}

\author{\firstname{Philippe} \lastname{Choler}\CDRorcid{0000-0002-9062-2721}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[P. Choler]{philippe.choler@univ-grenoble-alpes.fr}

\author{\firstname{Anne} \lastname{Delestrade}\CDRorcid{0000-0003-0249-2494}}
\address{Centre de Recherches sur les \'{E}cosyst\`{e}mes d'Altitude,
CREA, Chamonix, France}
\email[A. Delestrade]{adelestrade@creamontblanc.org}

\author{\firstname{Georges} \lastname{~Kunstler}}
\address{Univ. Grenoble Alpes, LESSEM, INRAE, F-38000 Grenoble, France}
\email[G. Kunstler]{georges.kunstler@inrae.fr}

\author{\firstname{Marie-France} \lastname{Leccia}}
\address{Parc national du Mercantour, Nice, France}
\email[M.-F. Leccia]{marie-france.leccia@mercantour-parcnational.fr}

\author{\firstname{Bertrand} \lastname{Lienard}}
\addressSameAs{2}{Alpine National Botanical Conservatory, CBNA, Domaine
de Charance, Gap, France}
\email[B. Lienard]{b.lienard@cbn-alpin.fr}

\author{\firstname{J\'{e}rome} \lastname{Poulenard}\CDRorcid{0000-0003-0810-0308}}
\address{Univ. Savoie Mont Blanc, CNRS, EDYTEM, Chamb\'{e}ry, France}
\email[J. Poulenard]{Jerome.Poulenard@univ-smb.fr}

\author{\firstname{Jean-Gabriel} \lastname{Valay}}
\address{Univ. Grenoble Alpes, CNRS, Jardin du Lautaret, F-38000
Grenoble, France}
\email[J.-G. Valay]{jean-gabriel.valay@univ-grenoble-alpes.fr}

\author{\firstname{Arthur} \lastname{Bayle}\CDRorcid{0000-0002-3442-0789}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[A. Bayle]{arthur.bayle@univ-grenoble-alpes.fr}

\author{\firstname{Nicolas} \lastname{Bonfanti}\CDRorcid{0000-0003-2390-6078}}
\addressSameAs{9}{Univ. Savoie Mont Blanc, CNRS, EDYTEM, Chamb\'{e}ry,
France}
\email[N. Bonfanti]{nbonfanti@inrae.fr}

\author{\firstname{Lenka} \lastname{Brousset}}
\address{Aix Marseille Univ, Univ Avignon, CNRS, IRD, IMBE, Marseille,
France}
\email[L. Brousset]{lenka.brousset@imbe.fr}

\author{\firstname{L\'{e}a} \lastname{~Bizard}\CDRorcid{0000-0002-2302-2021}}
\addressSameAs{2}{Alpine National Botanical Conservatory, CBNA, Domaine
de Charance, Gap, France}
\email[L. Bizard]{l.bizard@cbn-alpin.fr}

\author{\firstname{Irene} \lastname{~Calder\'{o}n-Sanou}\CDRorcid{0000-0003-4608-1187}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[I. Calder\'{o}n-Sanou]{irecalsa@gmail.com}

\author{\firstname{C\'{e}dric} \lastname{Dentant}}
\addressSameAs{5}{Parc national des \'{E}crins, Domaine de Charance,
Gap, France}
\email[C. Dentant]{cedric.dentant@ecrins-parcnational.fr}

\author{\firstname{Camille} \lastname{Desjonqu\`{e}res}\CDRorcid{0000-0002-6150-3264}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[C. Desjonqu\`{e}res]{desjonqc@univ-grenoble-alpes.fr}

\author{\firstname{Ludovic} \lastname{Gielly}\CDRorcid{0000-0001-5164-6512}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[L. Gielly]{ludovic.gielly@univ-grenoble-alpes.fr}

\author{\firstname{Maya} \lastname{Gu\'{e}guen}\CDRorcid{0000-0002-1045-2997}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[M. Gu\'{e}guen]{maya.gueguen@univ-grenoble-alpes.fr}

\author{\firstname{Fr\'{e}d\'{e}ric} \lastname{Guiter}}
\addressSameAs{11}{Aix Marseille Univ, Univ Avignon, CNRS, IRD, IMBE,
Marseille, France}
\email[F. Guiter]{frederic.guiter@imbe.fr}

\author{\firstname{Mickael} \lastname{Hedde}\CDRorcid{0000-0002-6733-3622}}
\address{Eco\&Sols, INRAE, IRD, CIRAD, Institut Agro, Montpellier, France}
\email[M. Hedde]{mickael.hedde@inrae.fr}

\author{\firstname{Eric} \lastname{~Hustache}}
\address{Natura 2000 D\'{e}voluy-Durbon-Charance-Champsaur SMIGIBA,
Veynes, France}
\email[E. Hustache]{ehustache@smigiba.fr}

\author{\firstname{Norine} \lastname{Kedhim}}
\addressSameAs{9}{Univ. Savoie Mont Blanc, CNRS, EDYTEM, Chamb\'{e}ry,
France}
\email[N. Kedhim]{khedim.norine@gmail.com}

\author{\firstname{Pierre} \lastname{Lapenu}}
\address{Parc national des Pyr\'{e}n\'{e}es, Tarbes, France}
\email[P. Lapenu]{pierre.lapenu@pyrenees-parcnational.fr}

\author{\firstname{Nicolas} \lastname{Le Guillarme}\CDRorcid{0000-0003-4559-7579}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[N. Le Guillarme]{nicolas.leguillarme@univ-grenoble-alpes.fr}

\author{\firstname{Lise} \lastname{~Marchal}}
\addressSameAs{9}{Univ. Savoie Mont Blanc, CNRS, EDYTEM, Chamb\'{e}ry,
France}
\email[L. Marchal]{lise.marchal@univ-smb.fr}

\author{\firstname{Chlo\'{e}} \lastname{Mahieu}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[C. Mahieu]{chloe-mahieu@bbox.fr}

\author{\firstname{Gabrielle} \lastname{Martin}}
\address{Centre de Recherche sur la Biodiversit\'{e} et l'Environnement
(CRBE), Univ. Toulouse, CNRS, IRD, Toulouse INP, Univ. Toulouse 3 --
Paul Sabatier (UT3), Toulouse, France}
\email[G. Martin]{gabrielle.martin@univ-tlse3.fr}

\author{\firstname{Camille} \lastname{Martinez-Almoyna}\CDRorcid{0000-0002-3624-7932}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[C. Martinez-Almoyna]{cmartinezalm@gmail.com}

\author{\firstname{Vincent} \lastname{~Miele}\CDRorcid{0000-0001-7584-0088}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[V. Miele]{vincent.miele@univ-lyon1.fr}

\author{\firstname{J\'{e}r\^{o}me} \lastname{Murienne}\CDRorcid{0000-0003-1474-7829}}
\addressSameAs{15}{Centre de Recherche sur la Biodiversit\'{e} et
l'Environnement (CRBE), Univ. Toulouse, CNRS, IRD, Toulouse INP, Univ.
Toulouse 3 -- Paul Sabatier (UT3), Toulouse, France}
\email[J. Murienne]{jerome.murienne@cnrs.fr}

\author{\firstname{Yoan} \lastname{Paillet}\CDRorcid{0000-0001-7232-7844}}
\addressSameAs{7}{Univ. Grenoble Alpes, LESSEM, INRAE, F-38000
Grenoble, France}
\email[Y. Paillet]{yoan.paillet@inrae.fr}

\author{\firstname{Maxime} \lastname{Rome}\CDRorcid{0000-0002-2937-735X}}
\addressSameAs{10}{Univ. Grenoble Alpes, CNRS, Jardin du Lautaret,
F-38000 Grenoble, France}
\email[M. Rome]{maxime.rome@univ-grenoble-alpes.fr}

\author{\firstname{Julien} \lastname{~Renaud}\CDRorcid{0000-0001-5991-4657}}
\addressSameAs{1}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS,
LECA, F-38000 Grenoble, France}
\email[J. Renaud]{julien.renaud@univ-grenoble-alpes.fr}

\author{\firstname{\unskip} \lastname{The Orchamp Consortium}}
\email[The Orchamp Consortium]{orchamp@univ-grenoble-alpes.fr}

\shortrunauthors

\keywords{\kwd{Monitoring}\kwd{Biodiversity observation}\kwd{Data
integration}\kwd{Artificial intelligence}\kwd{Environmental
DNA}\kwd{Camera-traps}\kwd{Acoustic landscape}}

\altkeywords{\kwd{Suivi et observation de la
biodiversit\'{e}}\kwd{Int\'{e}gration de donn\'{e}es}\kwd{Intelligence
artificielle}\kwd{ADN environnemental}\kwd{Pi\`{e}ges
photographiques}\kwd{Paysage acoustique}}

\thanks{French Agence Nationale de la Recherche (ANR)
(ANR-16-CE02-0009, ANR-18-CE02-0010-01), ``Investissement d'Avenir''
grant (Idex UGA: ANR-15-IDEX-02; Montane: OSUG@2020: ANR-10-LAB-56,
MIAI@Grenoble Alpes: ANR-19-P3IA-0003), Grenoble Alpes M\'etropole,
Agence de l'eau, Isere departement, Office Fran\c{c}ais de la
Biodiversit\'e (OFB), Lautaret Garden-UAR 3370, AnaEE-France
(ANR-11-INBS-0001AnaEE-Services, Investissements d'Avenir frame), LTSER
Zone Atelier Alpes}

\begin{abstract} 
Recent climate and land use change, and pollution have led to
concerning alterations in biodiversity and ecosystem functions,
jeopardizing nature's contributions to people. Mountainous regions are
not immune to these threats, experiencing the impacts of global
warming, increased recreational activities, and changes in agricultural
practices. Leveraging the natural elevational gradients of mountain
environments, the ORCHAMP program was established in 2016 as a
comprehensive initiative to monitor, understand, and predict the
repercussions of environmental changes on biodiversity and associated
ecosystem functions in the French Alps and Pyrenees.

Beyond its monitoring role, ORCHAMP has catalyzed the development of
tools for data integration, statistical analyses, visualization, and
AI-based automated data processing and predictions. Through a
combination of traditional sampling methods (e.g., botanical surveys)
and cutting-edge technologies (remote-sensing, environmental DNA,
video, and acoustic sensors), the program offers a holistic approach to
understanding how biodiversity faces environmental changes. By
showcasing examples and key results, this paper provides an overview of
ORCHAMP's advancements and outlines potential future directions. The
broad inclusion of diverse monitoring techniques and data treatments
positions ORCHAMP as a pioneering effort, paving the way for long-term
insights into biodiversity dynamics---a crucial step toward effective
conservation strategies.
\vspace*{-.3pc}
\end{abstract} 

\begin{altabstract} 
Les r\'{e}cents changements en mati\`{e}re de climat et d'utilisation
des sols, ainsi que la pollution, ont entra\^{i}n\'{e} des
alt\'{e}rations pr\'{e}occupantes de la biodiversit\'{e} et des
fonctions des \'{e}cosyst\`{e}mes, mettant en p\'{e}ril les
contributions de la nature aux populations. Les r\'{e}gions
montagneuses ne sont pas \`{a} l'abri de ces menaces, subissant les
effets du r\'{e}chauffement climatique, de l'augmentation des
activit\'{e}s r\'{e}cr\'{e}atives et des changements dans les pratiques
agricoles. Tirant parti des gradients d'altitude naturels des
environnements de montagne, le programme ORCHAMP a \'{e}t\'{e}
cr\'{e}\'{e} en 2016 en tant qu'initiative globale visant \`{a}
surveiller, comprendre et pr\'{e}dire les r\'{e}percussions des
changements environnementaux sur la biodiversit\'{e} et les fonctions
\'{e}cosyst\'{e}miques associ\'{e}es dans les Alpes et les
Pyr\'{e}n\'{e}es fran\c{c}aises.

Au-del\`{a} de son r\^{o}le de surveillance, ORCHAMP a promu le
d\'{e}veloppement d'outils pour l'int\'{e}gration des donn\'{e}es, les
analyses statistiques, la visualisation et le traitement automatis\'{e}
des donn\'{e}es et des pr\'{e}dictions bas\'{e}es sur l'IA. Gr\^{a}ce
\`{a} une combinaison de m\'{e}thodes d'\'{e}chantillonnage
traditionnelles (par exemple, des relev\'{e}s botaniques) et de
technologies de pointe (t\'{e}l\'{e}d\'{e}tection, ADN environnemental,
pi\`{e}ges photos et capteurs acoustiques), le programme offre une
approche holistique pour comprendre comment la biodiversit\'{e} fait
face aux changements environnementaux. En pr\'{e}sentant des exemples
et des r\'{e}sultats cl\'{e}s, cet article donne une vue d'ensemble des
avanc\'{e}es d'ORCHAMP et esquisse des orientations futures
potentielles. Gr\^{a}ce \`{a} l'int\'{e}gration de diverses techniques
de surveillance et de traitement des donn\'{e}es, ORCHAMP fait figure
de pionnier et ouvre la voie \`{a} une compr\'{e}hension \`{a} long
terme de la dynamique de la biodiversit\'{e}, une \'{e}tape essentielle
\`{a} la mise en place de strat\'{e}gies de conservation efficaces.
\end{altabstract} 

\maketitle

\twocolumngrid

\end{noXML}

\section{Introduction}

The Earth is undergoing rapid and unprecedented changes that surpass
recorded historical events. Despite these transformations, the
scientific community faces challenges in adequately reporting,
documenting, and quantifying their wide-ranging consequences on
biodiversity and the contributions of nature to
people~\cite{IPBES2019}. Pioneering monitoring programs have often
targeted emblematic groups such as birds (e.g., American and British
Bird Surveys) to represent the overall biodiversity, or focused on
endangered habitats or species, as advocated by the Habitat or Bird
directive of the European Union. While monitoring threatened species
and understanding the long-term survival of bird species is crucial and
has yielded significant discoveries, particularly in illustrating and
quantifying time-lags and extinction debts~\cite{Devictoretal2012,
GauzereDevictor2021}, these taxon-specific programs lack a holistic
vision integrating biodiversity and ecosystem functions.\looseness=-1

Biodiversity is not simply the sum of individual species but rather a
complex interaction network involving various organisms, from soil
bacteria to flying birds, influencing each other and collectively
\mbox{driving} ecosystem responses to environmental changes{\break}
\cite{Thompsonetal2012, Thuilleretal2023}.\ Detecting and attributing
human-induced environmental modifications to biodiversity and
ecosystems require adopting this holistic
perspective~\cite{GonzalezVihervaaraetal2023}.\ However, constructing a
monitoring program with a holistic vision is challenging, which
explains the limited number of such initiatives (e.g., Lifeplan, NEON,
eLTER,~\cite{Haaseetal2018}).

First of all, to monitor biodiversity dynamics and attribute changes to
potential pressures~\cite{GonzalezChaseetal2023}, a spatially-nested
sampling scheme is essential. As such, elevational gradients in
mountain environments provide a natural setting for tracking changes
across different biodiversity compartments over time.\ Sharp changes in
environmental conditions and human uses allow to track biodiversity
dynamics along short distances.\ Under climate warming, what we will
observe at a certain altitude in 50--70~years is likely what we
nowadays observe 200~m below.\ However, these gradients must be
replicated and distributed across multiple regions to best represent
the diversity of geological/environmental/biogeographic contexts as
well as the potential variability of biodiversity responses. Second,
monitoring the whole biodiversity of ecosystems requires adapting the
sampling protocols and tools to best capture the different compartments
(below and above grounds) and their interlinkages. Soil sampling might
require multiple samples in a given permanent plot, while a single
acoustic recorder for the whole landscape is likely enough. Third,
monitoring all biodiversity compartments across time requires another
vision of re-sampling. Microbes or invertebrate responses to
environmental changes are likely to happen at a different pace than
forest trees or large herbivores. In other words, adopting for instance
a strict 5-years interval sampling might not be the best strategy to
capture very short dynamics of invertebrates. Fourth, it also requires
combining and integrating different sampling strategies and tools to
best represent the whole biodiversity. The complexity of soil
biodiversity necessitates the use of high-throughput sequencing while
other vegetation variables can be measured with more traditional field
methods (i.e., floristic diversity, forest structure, and dead wood).
Fifth, in situ environmental measurements are critical for attributing
biodiversity changes, particularly in mountain environments where
large-scale interpolations of climate and soil data are often
inaccurate. Combining in situ measurements with high temporal and
spatial resolution satellite imagery is thus required. Sixth, the
quantification and statistical analyses of these data necessitate data
integration tools and pipelines for effective use. Finally,
visualization and validation tools are essential for engaging
stakeholders and actively involving them in data collection and\break
interpretation.

This paper presents an overview of the ORCHAMP program, a designed
initiative for spatially and temporally explicit monitoring of
biodiversity within the mountain ecosystems of the French Alps and
Pyrenees. The primary objective of this program is to address the
challenges mentioned earlier, focusing on the detection and attribution
of biodiversity changes. To facilitate efficient data processing,
archiving, and analyses for each biodiversity compartment and
environmental data acquisition, we have developed specialized tools,
pipelines and statistical analyses. Beyond this, ORCHAMP aims to
enhance our understanding of biodiversity dynamics and structure, and
to provide high-resolution predictions of biodiversity. Therefore,
ORCHAMP federates a large number of institutional and academic partners
in a framework of local collaborations with stakeholders and actors of
wildlife conservation.

\section{The elevational gradient as a primary{\hfill\break}
observation element}

ORCHAMP is conceptualized to systematically track the spatial and
temporal dynamics of biodiversity. Given the backdrop of climate
warming and land abandonment, we anticipate significant upward shifts
in species distributions, leading to community reorganization and
modified ecosystem functioning. Keeping track of these changes in
mountainous environments entails monitoring the fluctuations in the
spatial distribution and structure of biodiversity along elevational
gradients.\ To address this, \mbox{ORCHAMP} strategically deploys
permanent plots along multiple elevational gradients.

\begin{figure*}
{\vspace*{4pt}}
\includegraphics{fig01}
{\vspace*{4pt}}
\caption{\label{fig1}ORCHAMP monitoring scheme. Schematic illustration
of an ORCHAMP permanent plot, measuring 30 ${\times}$ 30~m (adjusted for
slope). The botanical survey is conducted along the central band
(highlighted in yellow), incorporating a comprehensive survey spanning
1.5~m ${\times}$ 30~m, and using 2 ${\times}$ 150 pin-points for
quantifying species abundances. Monitoring of trees and deadwood is
implemented throughout the entire plot (depicted in orange). Acoustic
and video recorders (shown in blue) are strategically positioned within
or in close proximity to the permanent plots. Climatic hobos and TOMST
are centrally located within the plot (star in the plot in dark pink),
while soil sampling for eDNA and physico-chemical analyses (in brown)
is performed 5~m down the botanical band (in yellow) and within three
pre-selected sub-plots. A soil pit is excavated in the vicinity of the
plot.}
{\vspace*{4pt}}
\end{figure*}

\subsection{From permanent plots to an elevational{\hfill\break}
gradient}

The permanent plot represents the common unit of each sampling
protocol, and within a plot, each protocol has its own location and
extent (Figure~\ref{fig1}). A permanent plot measures 30
${\times}$ 30~m.\ An \mbox{ORCHAMP} elevational gradient is thus based on
a certain number of permanent plots, spaced ${\sim}$200~m apart in
altitude, with four to nine permanent plots per gradient. Ensuring the
attribution of biodiversity changes is not compromised by uncontrolled
sampling settings, elevational gradients are selected with relatively
homogeneous slopes, minimal ruptures, changes of orientations, or
strong discontinuity. Ideally, a linear transect from the bottom of the
valley to the summit is sought, although achieving this is often
\mbox{challenging}.\looseness=-1

\begin{figure*}
\includegraphics{fig02}
\vspace*{-.2pc}
\caption{\label{fig2}ORCHAMP spatial representation. Locations of the
ORCHAMP elevation gradients through the French Alps and Pyrenees.
Permanent plots are located 200~m of altitude apart of each other.}
\vspace*{-.2pc}
\end{figure*}

\subsection{From an elevational gradient to multiple representative
gradients of the region}

Recognizing the limited statistical value of a single elevational
gradient across a large mountain region, we selected gradients to be
representative of the overall diversity of pedo-climatic conditions
(Figure~\ref{fig2}). This is achieved by collaborating with local
partners to sequentially add new elevational gradients, ensuring they
fill gaps in the multivariate environmental space of the French Alps
and the Pyrenees (taken separately). The process began in 2016 with
five gradients in the French Alps, and in subsequent years, with the
addition of new gradients (Figure~\ref{fig3}). In 2020, \mbox{ORCHAMP}
initiated the re-survey of existing elevational gradients, slowing the
addition of new ones in 2021, focusing on the complementarity of these
gradients. Elevational gradients were also started in the Pyrenees in
2021, following the same sampling strategy as in the French Alps
(Figure~\ref{fig3}).

\begin{figure}
\includegraphics{fig03}
\caption{\label{fig3}Number of ORCHAMP plots and gradients monitored.
Cumulative representation of the elevation gradients and total number
of plots across time, together with the number of plots sampled every
year for the first visit or for a resurvey.}
\end{figure}

\subsection{Resampling strategy}

Given ORCHAMP's diverse taxonomic targets, spanning bacteria to plants
to large mammals, with varying life-history traits and responses to
environmental changes, a rotative panel sampling approach was adopted.
This strategy involves randomly selecting elevational gradients to be
sampled each year, aiming for a relatively uniform time-interval across
the \mbox{gradients} over the long run. The average time-interval is set at
around 4--5 years. As ORCHAMP involves multiple academic and local
partners, constraints are introduced in the random draw to ensure
equitable distribution of workload, notably by \mbox{limiting} the number of
gradients a specific botanical society can investigate per year. To
enhance repeatability, a Shiny interface was developed to track the
planned resampling strategy over the years.

\section{Key monitored environmental features}

To have the key environmental features to be contrasted to
biodiversity, we follow relevant environmental features on each sampled
plot. In a nutshell, we follow soil temperature, and more recently soil
humidity, and soil physico-chemical properties for the in situ
measurements. Once these data are combined with local climatic models
and satellite images, they form our data-cube of environmental features
(or essential environmental variables, \cite{Schmelleretal2017},
Figure~\ref{fig4}). 

\begin{figure*}
\includegraphics{fig04}
\caption{\label{fig4}Data integration and flow of information through
ORCHAMP. Environmental features are collected through various sensors
and techniques and form the environmental data cube. From below to
above ground, environmental DNA, vegetation survey, acoustic and video
recorders provide multi-taxa biodiversity data cube than can then be
transformed into essential biodiversity variable data cube. Both
environmental and biodiversity data cube are then used to detect and
attribute biodiversity changes but also to provide predictions. Our
dedicated soil food web ontology and data integration pipeline even
allows constructing informative soil food webs.}
\end{figure*}

\subsection{In situ climatic data and local climatic{\hfill\break}
models}

A better understanding of the local climate conditions experienced by
living organisms is of paramount importance to anticipate biodiversity
response to climate change~\cite{DeFrenneetal2013, Lenoir2020,
Zellwegeretal2020}.\ The temperature measured within ecosystem
compartments (i.e., understorey, soil) substantially differs from the
air temperature measured by standardized weather station or modelled by
regional and global climate re-analysis. The extent of this decoupling
between microclimate and macroclimate exhibits large spatial variations
in relation to land cover and topographical
variations~\cite{Griletal2023}. For example, densely forested stands
play a more effective role in mitigating the diurnal and seasonal
fluctuations of air temperature compared to open habitats, and this
effect can explain contrasting responses of the understory plants to
climate warming~\cite{Griletal2023, Zellwegeretal2020}.

In mountains, near-surface temperature variations are also largely
determined by the duration of the snow cover period, which is mainly
controlled by topographical variations and wind redistribution of
snow~\cite{Choler2018, LofflerPape2020}. This leads to contrasting soil
temperature regimes over short distances in above-forest
ecosystems~\cite{Choler2018}. Understanding the patchiness of the
thermal landscape in mountains is pivotal when investigating spatial
and temporal changes in biodiversity and ecosystem processes.

All ORCHAMP sites have been equipped with miniaturized and standalone
temperature data loggers (Hobo pendant UA, Onset Computer Corporation,
Bourne, MA). Devices are buried at 5~cm below ground level to monitor
the temperature of the topsoil layer.\ Soil temperatures are measured on
an hourly basis and time series are updated every year since 2016
(Figure~\ref{fig1}).\ Data have been organized according to FAIR
principles (``Findability, Accessibility, Interoperability, and
Reusability'',~\cite{Wilkinsonetal2016}) in the framework of a larger
initiative regrouping all long-term monitoring sites in the French
Alps\footnote{\url{https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/NGC4J0}.\vspace*{-.25pc}}.\ 
Since~2022, soil moisture data loggers have been added (TMS4,
Tomst,~\cite{Wildetal2019}).\ As an example of ongoing analyses,
Figure~\ref{fig5} compares soil temperature regimes for year 2021 and
2022 in 14 elevational gradients representing 67 ORCHAMP plots. In the
South-Western Alps, the summer of 2022 stood out for the unprecedented
duration of the heat wave~\cite{Choler2023}. In 2022, the combination
of an early snow melt-out date (Figure~\ref{fig5}a) and a warm summer
(Figure~\ref{fig5}b) led to a very high accumulation of growing days
(Figure~\ref{fig5}c) in all ORCHAMP sites. The results also show that
forest vegetation has largely attenuated the impact of the 2022 heat
wave on soil temperature (Figure~\ref{fig5}). 

\begin{figure*}
\includegraphics{fig05}
\caption{\label{fig5}Impact of the 2022 heat wave on the soil
temperature regime of ORCHAMP sites. (a)~Difference in snow melt-out
date (SMOD) between 2021 and 2022. Most sites experienced an earlier
SMOD in 2022. (b,~c)~Percentage rise in the average July
temperature~(b) and the Growing Degree Days (GDD)~(c) in 2022 compared
to 2021. GDD is the sum of average daily degrees above
0~{\textdegree}C. Green and gray symbols represent forested and
non-forested vegetation, respectively. Results are displayed along the
elevational gradient covered by 67~ORCHAMP sites.}
{\vspace*{-2pt}}
\end{figure*}

In addition to in situ measurements, climatic variables from the S2M
re-analysis~\cite{Vernay2019} are also extracted for each site. This
dataset provides an hourly time series of meteorological data from 1958
to the present, including minimum, maximum, and mean temperatures for
both soil and air, precipitations, solar radiations, and snow depth.
Surface conditions are simulated as a function of elevation, slope, and
aspect in the French Alps and Pyrenees.

\subsection{Soil pit}

\looseness=-1
On each permanent plot, a soil pit is dug (20~m downstream of the
permanent plot to avoid disturbing the plot). Soils are described using
the recommendations and coding procedures from the guideline for soil
description~\cite{FAOetal2006}. Descriptions are \mbox{carried} out in
the field using a specifically developed app
(\url{http://193.48.120.232:8080/descpedo/}), which allows to
standardize soil descriptions among operators and the descriptions to
be directly integrated in the database. Soil samples are collected
based on morphological horizons, emphasizing transitions, root
profiles, and coarse elements. Where possible (and systematically for
the 0--20~cm toposoil), known-volume soil samples, taken from
cylinders, are added to reworked samples, allowing estimation of bulk
density and stoniness for carbon stock calculation and water reserve
assessment. Systematic sampling of soil parent materials is also
realized, addressing both mono-parental (the large majority) and
complex profiles with buried horizons and pedogenesis on various
substrates. The samples are dried, sieved (2~mm), and quartered, with a
portion stored in a sample library for future use (ORCHAMP pedological
database).

Three main analyses are conducted on the pit soil samples:
\vspace*{-.2pc}
\begin{enumerate}[(1)]
\item Conventional soil analyses (e.g., grain size, cation-exchange
capacity, organic caron, total nitrogen and phosphor, pH) to define
soil type and identify factors affecting \mbox{biodiversity}.
\looseness=-1
\item Organic matter characterization (using infrared spectroscopy,
Rock-Eval pyrolysis) to estimate organic carbon stock and stability.
\item Mineral geochemistry analyses (quantitative X-ray fluorescence
spectroscopy) to characterize the geochemical diversity of rocks,
estimating the degree of weathering in soils.
\end{enumerate}

Altogether these analyses provides comprehensive data on soil nature,
leading to classification in soil reference systems~\cite{WRB2014}.
Nearly twenty WRB soil references are covered, including variations in
elevation, redox features, peaty soils, and more. The provided
descriptions and pedological data are considered state variables
influencing species distributions. For example, they make it very easy
to position plots and transects within conventional gradients (acid
rock--alkaline rock; soil with high or low water reserves,
Figure~\ref{fig6}). While the initial data aids in situating plots
within conventional gradients, a second soil analysis campaign is
planned for year $n + 15$ to assess medium-term changes, particularly
in organic matter. This aims to observe statistically significant
changes in organic matter stock and stability resulting from altered
thermal regimes~\cite{Khedimetal2023}.

\begin{figure*}
{\vspace*{-.4pc}}
\includegraphics{fig06}
\caption{\label{fig6}pH of the subsurface horizon in function of
SiO$_{2}$ bedrock content. Bedrock geochemistry provides a better
estimate of soil properties than factorial information derived from
geological maps.}
\end{figure*}

\subsection{Soil physico-chemical properties}

In each of the three 2 ${\times}$ 2~m soil subplots positioned 5~m below
the inner transect of every sampling plot, ten soil cores are gathered
and combined (Figure~\ref{fig1}). A 15~g subsample is allocated for
eDNA extraction. The remaining composite sample undergo a 2~mm sieving
process and subsequent drying for the evaluation of physico-chemical
properties. A section of the soil is finely ground to a particle size
below 250~${\upmu}$m using an ultra-centrifugal grinder ZM~200 (ZM~200,
Retsch). This facilitates the determination of total soil carbon and
nitrogen contents through an elementary analyzer (Flash EA1112, Thermo
Scientific). The remainder of the samples is dedicated to pH
measurement in distilled water, adhering to the ISO 10390:2005
standard, utilizing a pH-meter (pH7110, inoLab). Soil organic matter
(SOM) content is computed through Loss on Ignition, involving a 4-h
incubation in a muffle furnace at 550~{\textdegree}C.

\subsection{Remotely-sensed biophysical variables}

Sentinel-2 has paved the way for cost-effective and high-resolution
monitoring of vegetation, snow dynamics, and landscape configuration
within ORCHAMP. This is achieved through a revisit time tailored to
handle the prevalent heavy cloud cover at high altitudes and a
ten-meter spatial resolution that aligns with the meso-topographic
variability of our elevation gradients. Despite its advantages, the
relatively short duration of the mission, commencing in 2017, poses
limitations in tracking long-term trajectories essential for grounding
biodiversity measurements in recent history. 

\begin{figure*}
\includegraphics{fig07}
\caption{\label{fig7}NDVImax trends from 1984 to 2023 along the
elevation for all ORCHAMP sites in the French Alps. Green and yellow
points correspond to forested and non-forested sites, respectively.
Curves are obtained from Generalized Additive Models (GAM) with shaded
areas corresponding to standard error estimates. Green curve is only
for forested sites while black curve is for both forested and
non-forested sites. The color bar to the right of the panel is simply a
qualitative indicator of the degree of browning or greening.}
{\vspace*{.3pc}}
\end{figure*}

The Landsat constellation offers the dual advantage of historical depth
on a climatic timescale (more than 30~years of data) and spatial
resolution \mbox{tailored} to mountain ecosystems. By combining these forces,
we are able to derive high-resolution biophysical variables on all
permanent plots and beyond, over the decades preceding the start of
ORCHAMP. Employing annual maximum compositing of vegetation indices
observations, such as Normalized Difference Vegetation Index (NDVI), we
can generate multi-decadal, annual maximum NDVI (NDVImax) time series
that act as indicators of vegetation changes. Positive trends in
NDVImax, referred to as ``greening'', signal an increase in vegetation
greenness at the pixel scale, attributable to either an increase in
vegetation biomass or productivity of preexisting
vegetation~\cite{Berneretal2020} or shift in plant community
composition~\cite{Bayleetal2023, Dentantetal2023}. In mountainous
regions, remote sensing is a favored tool due to the large study areas
involved and the \mbox{obvious} difficulty of access and is therefore
increasingly employed to observe how these ecosystems respond to
ongoing climatic and usage changes~\cite{Bayleetal2023, Choleretal2021,
Rumpfetal2022}. Notably, Choler {et~al.}~\cite{Choleretal2021}
demonstrated the spatial variability of greening trends in above-forest
habitats using MODIS satellites, with evidence that greening is
widespread at the scale of the European Alps, but that high magnitude
greening is mostly \mbox{occurring} in north-facing and sparsely vegetated
slopes between 1900 and 2400~m, a phenomenon further corroborated by
subsequent studies~\cite{Rumpfetal2022}. As an example of ongoing
research, Figure~\ref{fig7} displays the NDVImax trends observed from
1984 to 2023 along the elevation gradient across all ORCHAMP sites
obtained using all Landsat 5~TM, 7~ETM+ and 8~OLI images available in
Google Earth Engine. We obtained the annual maximum NDVI and computed
trends using a Theil-Sen estimator (R~package mblm). Our results reveal
that forests above 1800~m are positively responding to air temperature
increases over the past 40~years, whereas forests at lower elevations
are either not benefiting or adversely affected, aligning with recent
studies~\cite{Senfetal2018}. Across all above forest sites
(${>}$2100~m), a positive vegetation response is observed, with almost
no negative trends, consistent with the ecophysiology of alpine
ecosystems amid warming~\cite{KeenanRiley2018} and supported by recent
observations~\cite{Dentantetal2023, Rumpfetal2022}.

\section{Key monitored biodiversity features}

ORCHAMP mixes various biodiversity measurements from traditional
botanical surveys to camera-traps with automatic assignments. The
overall idea is to monitor biodiversity from the below-ground
compartments through environmental DNA metabarcoding targeting the
whole soil biodiversity, vegetation through botanical survey and remote
sensing, to the above-ground compartment through acoustic and
camera-traps sensor targeting mammals and~birds. Several protocols have
been defined for this purpose, some of which are mandatory and
implemented on each transect, while others are optional (see
Appendix~\ref{appA} for the full list of variables \mbox{monitored}).

\subsection{Plant vegetation survey and forest{\hfill\break}
monitoring}

\subsubsection{Vegetation survey}

On each plot, a presence--absence survey is conducted along the central
band (Figure~\ref{fig1}) to characterize the dynamics of plant species
assemblages in time, including rare species. Although this protocol
targets species presence--absence, the overall area (900~m$^{2}$) allows
the detection of rare species. So far, the lowest number of species
richness was six (a~dense spruce forest) and the largest was 93 (rich
subalpine grasslands).

\subsubsection{Pin-point transects}

To monitor relative species' abundance, a pin-point sampling is also
carried out along the central band. At every 20~cm increment, all plant
individuals in contact with the pins placed at the two measurement
points (one 25~cm upslope and the other 25~cm downslope from the
transect) are identified and recorded. This process yields a total of
300~pin-points per plot, each potentially involving multiple
contacts with individual plants. So far, the lowest number of recorded
individuals was two (a high summit at 3000~m) and the largest was 1472
(rich subalpine grasslands). This protocol is an adaptation of the
Gloria ``Downslope
plant survey''~\cite{ECetal2015}. In theory, this heavy protocol rather
targets the dominant species and allows following their relative
abundance trough space and time, but moderately rare species can also
be detected. 

{\advance\baselineskip by -.22pt

\subsubsection{Forest monitoring}

{\vspace*{-2pt}}

The structure of both alive and dead trees is a crucial element of
biodiversity and influences all other biodiversity components monitored
in ORCHAMP~\cite{Leclercetal2023}. The forest structure monitoring
protocol employed is tailored from the one utilized in the French
forest reserves monitoring network~\cite{Cateauetal2024}, ensuring
inter-comparability.

Live trees: On each plot, trees are identified and located, and their
Diameter at Breast Height (DBH, 1.30~m from the
ground). Individuals with a {diameter} larger
than 30~cm are surveyed in the entire 30 ${\times}$ 30~m plot, those with
a diameter larger than 7.5~cm are only surveyed in the area 5~m upwards
and downwards of the central band. Trees smaller than 7.5~cm with a
height ${>}$1.3~m) are surveyed on the central band only.
\looseness=-1

Deadwood: Three deadwood types are documented: fallen logs, standing
snags (height ${\geq}$ 1.30~m), and stumps (height ${\leq}$ 1.30~m). Each
deadwood piece is identified to species whenever possible, and
categorized based on (i)~wood decay level (hard, less than 50\% rotten,
more than 50\% rotten) determined by a knife penetration
test~\cite{LarjavaaraMullerLandau2010}; and (ii)~bark presence (intact,
more than 50\% attached bark, less than 50\% attached bark, no bark).
All deadwood (standing and fallen) with a minimum diameter of 30~cm is
measured in the 30 ${\times}$ 30~m plot. In the 5~m zone above and below
the central line, stumps and snags with a diameter between 7.5~cm and
30~cm are measured (mid-height for stumps, 1.30~m height for snags).
Lastly, along the central line (see Figure~\ref{fig1}), logs
intersecting the line and with a diameter between 7.5~cm and 30~cm are
measured using the Line intersect sampling method.\looseness=-1

Tree-related microhabitats: A recent addition to the forest protocol
concerns Tree-related Microhabitats~\cite{Larrieuetal2018}, that are
tree-borne singularities that \mbox{provide} food, shelter and nesting
grounds for a variety of species (Figure~\ref{fig8}). We inventory
tree-related microhabitats on all living and standing dead trees with a
DBH ${>}$ 7.5~cm (except stumps) following the typology of Larrieu
{et~al.}~\cite{Larrieuetal2018} at its finest scale (47~types). We also
quantify the abundance per tree of each \mbox{microhabitat} type.

}

\begin{figure*}
{\vspace*{1pt}}
\includegraphics{fig08}
{\vspace*{1pt}}
\caption{\label{fig8}Changes in epiphytes along elevation. Green, blue
and red lines represent bryophytes, lichens and ivy and other lianas,
respectively.}
{\vspace*{1pt}}
\end{figure*}

{\vspace*{-3pt}}

\subsection{Below-ground biodiversity from{\hfill\break} environmental
DNA}

{\vspace*{-3pt}}

For each of the three subplots, 15~g aliquot are sub-sampled from the
composite soil samples \mbox{extracted} with the drill (Figure~\ref{fig1}) and
then dried using silica gel. After each annual sampling campaign, eDNA
extractions are performed and analyzed at the Laboratoire d'Ecologie
Alpine following a standardized protocol~\cite{Taberletetal2012}.\ 
Six DNA markers are employed to
\mbox{characterize} the whole soil biodiversity, including two
universal markers (euka02 for eukaryote, bact01 for bacteria) and four
clade-specific markers (fung02 for Fungi, inse01 for Insects, olig01
for Oligochaeta, and coll02 for Collembola,~\cite{Taberletetal2018}).
DNA extraction, sequencing and all bioinformatic pipeline follow
well-established and standardized pipelines (Supplementary
Materials,~\cite{CalderonSanouetal2024}).

Environmental DNA metabarcoding offers cost-effective means to obtain
extensive data on the whole soil biota. This big data is characterized
by its heterogeneous taxonomic resolution, which is directly related to
the discriminatory power of the DNA markers
used~\cite{CalderonSanouetal2020}. To provide a more concise
representation that still provides insights into the local structure
and composition of soil communities, we \mbox{assign} the retrieved
taxonomically annotated MOTUs to trophic groups of organisms that feed
on the same food sources and have the same
consumers~\cite{CalderonSanouetal2022, Heddeetal2022}. We subsequently
link these groups based on known interactions to construct food webs
that consider multiple trophic groups (nodes) and their linkages across
trophic levels (links)~\cite{CalderonSanouetal2024}.

The construction of food webs from large eDNA datasets was so far
hampered by the lack of an \mbox{overarching} framework for classifying all
soil biota based on their feeding preferences, and the scarcity and
dispersion of trophic information for soil organisms in a multitude of
data sources. To address the first challenge, we first developed the
Soil Food Web Ontology (SFWO) to establish consensual and formal
definitions for the range of concepts relevant to soil food
webs~\cite{LeGuillarmeetal2023}. The SFWO provides logical (in the
sense of mathematical logic) definitions for more than 160 trophic
groups across different taxonomic groups. This allows using automated
reasoning to classify soil-associated consumers into trophic groups in
consistent way. In parallel, and to address the second challenge, we
developed an ontology-based data integration pipeline to facilitate the
construction of biodiversity knowledge graphs from multiple data
sources~\cite{LeGuillarmeThuiller2023}. This pipeline was used to build
a knowledge graph integrating information about the trophic
interactions and feeding-habits of soil organisms from a dozen data
sources. This graph provides a unified access to multisource trophic
information across taxonomic groups and trophic levels. Within the
knowledge graph, the integrated data are ``semantified'' (i.e., linked
to concepts in the SFWO). This solves the problem of the semantic
heterogeneity between data sources and allows the use of automated
reasoning to deduce logical consequences (e.g., belonging to a trophic
group) from explicitly-stated facts about trophic interactions and/or
diets.

This knowledge-based approach greatly facilitates the reconstruction of
informative metawebs, aka potential networks containing all the
detected trophic groups and their potential interactions, from
community composition data~\cite{CalderonSanouetal2024} and to analyze
their changes in functions of environmental factors or habitats
(Figure~\ref{fig9}), or through time.

\begin{figure*}
{\vspace*{.4pc}}
\includegraphics{fig09}
\caption{\label{fig9}Soil food web variation along ORCHAMP gradients.
Example of soil food web reconstructed through our automated pipeline
and the relative frequency of the different trophic groups. Colors
correspond to the trophic class to which the trophic groups belong to.
The size of the circle corresponds to the relative frequency compared
to the maximum ever observed through the whole set of the plots. Ew:
earthworms, B: bacteria, F: fungi, N: nematods, P: protists. P-bact:
protist bacterivores, F-ecm: ectomycorrhizal fungi etc.}
{\vspace*{.4pc}}
\end{figure*}

\subsection{Above-ground biodiversity---from{\hfill\break} camera-traps
to automatic recognition,{\hfill\break} validation and diffusion}

At ORCHAMP, our focus revolves around the observation of large mammals
during the summer as they pass through or closely inhabit the permanent
plot. To achieve this, we employ SPEC Elite HP4 (Browning) cameras
strategically positioned on trees within forest plots and on rocks or
wooden poles in open plots, ideally situated approximately 60~cm above
the ground. Two camera-traps are installed on each permanent plot,
oriented preferably towards animal tracks whenever possible.

This monitoring protocol underwent testing in 2021 across 11 permanent
plots and was officially initiated in 2022. Throughout 2022 and 2023,
we extended our surveillance to 69 plots spanning 14 gradients. The
camera traps are programmed to capture sequences of three images for
each detection event, enhancing the accuracy of species\break identification.

Leveraging recent advancements in AI technology, we employ a two-step
process, each step relying on a deep learning model. In the first step,
using the MegaDetector (version~v5.0a;
\url{https://github.com/agentmorris/MegaDetector/releases/tag/v5.0}),
we detect animals of any species, as well as humans and vehicles,
sorting out empty images. Images with the highest confidence score
above a threshold of 0.5 are retained, forming a bounding box that
contains the most crucial element for classifying the entire image. In
the second step, when the detected element is an animal, we use the
DeepFaune classifier~\cite{Rigoudyetal2023}, a deep-learning-based
species classification model (version~v1.0.0) specifically designed for
European fauna. Notably, this model was trained using images from
various partners, including ORCHAMP, ensuring excellent accuracy on our
images. Finally, the DeepFaune add-on dedicated to image sequence
management~\cite{Dussertetal2023} is employed for a single species
prediction for each sequence.

The entire pipeline for camera-trap data is designed to be end-to-end.
Images are transferred from SD cards to the Grenoble University
computing center, where they are dynamically processed using the
presented AI-based approach. Metadata, including classification results
(empty/human/vehicle or any class in DeepFaune), are stored and can be
inspected using a web interface developed with Kibana. Additionally, to
enhance the confidence in image classification, an additional
validation module inspired by citizen science initiatives like
Zooniverse (e.g., project Wild Mont-Blanc) has been integrated. ORCHAMP
staff can use a software app to review sequences with low-confidence AI
predictions (DeepFaune score below~0.8, representing less than 20\% of
non-empty images). This allows staff to vote for or against proposed
predictions and make corrections if necessary, ensuring a high level of
confidence in image classification through a combination of AI and
manual verification.

\subsection{From passive acoustic monitoring to{\hfill\break} sonotype
diversity and soundscapes}

Passive Acoustic Monitoring (PAM) is an emerging sampling method that
consists in collecting sound in the field with autonomous recording
units~\cite{Gibbetal2019, Sugaietal2019}. This method allows for a
non-invasive survey with high spatio-temporal
replication~\cite{Rossetal2023} and can target all soniferous species
(e.g., birds, mammals, anurans, arthropods, fish) but also
environmental noises and physical processes (e.g., rain, wind or
anthropogenic noise) in an environment~\cite{Pijanowskietal2011}.

Within ORCHAMP, we focus on audible terrestrial animals and sounds
present in or close to the plots. We record 1~min every 15~min, every
other day for three-four months over summer with SMmini (Wildlife
Acoustics Inc, Concord, MA, USA). The recorders are set at 48~kHz
sampling rate, which allows detecting sounds up to 24~kHz. We install
the recorders at 1.5~m from the ground on trees in forested plots or on
wooden poles in plots located in open habitats. This protocol was
tested in 2019 and initiated in 2022. Over 2022 and 2023, 52 plots over
ten gradients were recorded. The recordings are stored on the
University of Grenoble Alpes server and a pipeline automatically
calculates a set of acoustic indices, which does not require any
species annotation. The result of these analyses is stored as metadata
for each recording.

Studying the structure and dynamics of acoustic communities using
passive acoustic monitoring requires the ability to determine the
composition of the community from audio recordings. The amount of raw
audio data collected as part of ORCHAMP is growing rapidly, making
expert-based acoustic \mbox{animal} identification impractical and
motivating the search for automated approaches. Most existing
techniques for identifying animal calls from ecological soundscapes
perform a species-by-species analysis (e.g.,
BirdNet,~\cite{Kahletal2021}), with recent studies targeting birds,
anurans, mammals, and insects~\cite{Desjonqueresetal2020,
Jeliazkovetal2016, Rossetal2023}. The simultaneous detection of animal
vocalizations from multiple taxonomic groups remains a challenge,
especially in the absence of the labeled data essential for training
the state-of-the-art species identification models relying on
supervised learning. A promising alternative is the use of unsupervised
learning to identify acoustic OTUs from audio
recordings~\cite{Guerreroetal2023}. In this approach, an acoustic event
detector extracts regions of interest from noise-reduced spectrograms.
Spectral and cepstral features are then extracted and clustered, with
the different clusters being (hopefully) associated with different
species calls. We are currently developing our own version of this
unsupervised pipeline, combining deep neural network-based acoustic
event detection and feature extraction to overcome some of the
weaknesses of the original implementation.
{\vspace*{-.35pc}}

\section{Data integration and data cubes}{\vspace*{-.15pc}}

Each raw data and associated metadata collected in ORCHAMP are
collected and stored using an online web application developed at LECA
and stored in the ORCHAMP relational database, except for large and
specific data sets (eDNA, acoustic, camera) where data are stored on
independent storage platforms owned by Univ. Grenoble Alpes. For eDNA a
specific meta-database is also available to store laboratory metadata.

For common indices and summarized data calculated from raw data,
scripts are available for the ORCHAMP Consortium through an
institutional Gitlab repository.

Raw data are made available for each type of data and protocol and for
each plot using an R-Shiny application (interactive web applications
straight from~R, \url{https://orchamp.osug.fr/api/general}), where user
can select the data needed and download\ .csv files. Graphs are also
automatically generated to visualize the results. 

All collected data through time form data cubes of Essential Variables,
which are sparse given the fact we do not resample every year all plots
and that re-sampled plots are drawn at random (Figure~\ref{fig4}). Yet,
those data cubes, which contains both biological and environmental data
are extremely suited to build statistical models to understand the main
drivers of biodiversity~\cite{CalderonSanouetal2022,
MartinezAlmoynaetal2019}, to understand biotic
interactions~\cite{Ohlmannetal2018} and their effects on ecosystem
processes~\cite{MartinezAlmoynaetal2019} but also to predict them
through space and time. Last but not the least, these data can be used
to detect and attribute biodiversity changes through causal
models~\cite{GonzalezChaseetal2023}.

\section{ORCHAMP---a playground for additional{\hfill\break} projects}

ORCHAMP represents a real-world laboratory which also may serve as an
expansive playground where researchers can develop supplementary
projects, within the framework of ORCHAMP primary objectives and with
the benefit of shared datasets of the consortium. Such side projects
represent key advantages for assessing complexity and diversity of
mountain ecosystems facing global change, and offer a fertile ground
for innovative approaches beyond the common ORCHAMP design. ORCHAMP
encourages collaborative and interdisciplinary research methodologies,
where ideas from various fields can converge to study multifaceted
questionings.

Hereafter, we present some additional side-projects within ORCHAMP.

\subsection{Traditional monitoring of soil{\hfill\break}
macroinvertebrates}

Soil macroinvertebrates, whether in the topsoil or at the soil surface,
serve as crucial connectors between soil food webs (investigated
through eDNA analysis) and biodiversity in the above-ground environment
(monitored using acoustic or camera traps). The macrofauna plays a
pivotal role as regulators in both the brown and green pathways of
energy flows in terrestrial ecosystems and represents significant food
sources for various vertebrates, encompassing diverse taxonomic groups.

Sampling of soil macroinvertebrate communities occurs in early autumn
through two standardized methods. Firstly, macroinvertebrates are
manually sorted from four soil monoliths (25 ${\times}$ 25~cm), reaching
depths of up to 20~cm when feasible. During the same period,
macroinvertebrates at the soil surface are collected using six pitfall
traps. Taxonomic classification is carried out at the highest
resolution possible. Earthworms and ground beetles, representing
distinct trophic groups, are individually weighed based on a trait
directly linked to\break fitness.

Leveraging these data and the data cubes from ORCHAMP, comprehensive
analyses will reveal the key drivers influencing multi-trophic
community composition changes. It will allow disentangling bottom-up
(resource-driven) and top-down (regulation by climate, vertebrate
predators, etc.) factors at nested spatial scales, from local to
regional scales. Additionally, it provides a platform to scrutinize the
distinct roles played by intraspecific and interspecific trait
variability in explicating the observed patterns. Furthermore, changes
in intraspecific trait variability will serve as early signals of
population adaptive responses to environmental changes. More
specifically, we will assess the complementarity of sampling methods
(environmental DNA and hand-sorting) for characterizing the response of
earthworms to environmental gradients, providing elements for
implementing future large scale monitoring\break schemes. 

\subsection{Retrospective approaches}

ORCHAMP has also enabled the emergence of \mbox{retrospective}
approaches to biodiversity, using \mbox{paleoecological} and historical
ecology disciplines along selected transects. For instance, the study
of anthracological fossil contents in soil pits (across 4 transects)
will allow the assessment of the past presence (sometimes on a
multi-millennial scale) of structuring forest taxa in the vegetation
cover. Similarly, multi-proxy analysis of several Holocene lake
sediments (sampled near the transect), aims to trace past dynamics of
mountain ecosystems, changes in land-use, erosion and past climate
(across various temporal scales), focusing on the age, the intensity,
and the recurrence of high-altitude zone openings under the combined
influence of past land-use and Holocene climatic variability.

\section{Conclusions}

Started in 2016, we expect ORCHAMP to become a cornerstone for the
biodiversity monitoring in mountain environments. We are currently in
discussion to set up new elevational gradients in Corsica mountains and
a further logical expansion will involve the whole European Alps. Such
an endeavor will require building long-term international
collaborations with scientists and stakeholders from other countries.
Yet, while we have been able to foster collaborations between national
parks, reserves, local actors and researchers, building such a
consortium across several countries will require more stabilized
fundings and regulatory processes.

\section*{Declaration of interests}

The authors do not work for, advise, own shares in, or receive funds
from any organization that could \mbox{benefit} from this article, and have
declared no affiliations other than their research organizations.

\section*{Acknowledgments}

The ORCHAMP project is only possible thanks to the many people that
participate in the field work and in the lab procedures, including
students, interns, postdoctoral fellows, field assistants, park
managers and rangers. The complete list of funding is display in
Appendix~\ref{appB}, but we would like to acknowledge support from the
French Agence Nationale de la Recherche (ANR) through the GlobNet,
TransAlps \& EcoNet projects (ANR-16-CE02-0009, ANR-18-CE02-0010-01),
from two ``Investissement d'Avenir'' grant (Idex UGA: ANR-15-IDEX-02;
Montane: OSUG@2020: ANR-10-LAB-56, MIAI@Grenoble Alpes:
ANR-19-P3IA-0003), Grenoble Alpes M\'{e}tropole, the Agence de l'eau,
the Isere departement and the Office Fran\c{c}ais de la
Biodiversit\'{e} (OFB). Support was also received from the Lautaret
Garden-UAR 3370 (Univ. Grenoble Alpes, CNRS, Jardin du Lautaret, 38000
Grenoble, France), member of AnaEE-France
(ANR-11-INBS-0001AnaEE-Services, Investissements d'Avenir frame) and of
the LTSER Zone Atelier Alpes, a member of the eLTER-Europe network.

\CDRGrant[ANR]{ANR-16-CE02-0009}
\CDRGrant[ANR]{ANR-18-CE02-0010-01}
\CDRGrant[ANR]{ANR-15-IDEX-02}
\CDRGrant[ANR]{ANR-10-LAB-56}
\CDRGrant[ANR]{ANR-19-P3IA-0003}
\CDRGrant[ANR]{ANR-11-INBS-0001AnaEE-Services}

\back{}

\appendix{}

\gdef\appendixlabel{Appendix A}

\section{} \label{appA}

See Table~\ref{tabA1}.

\renewcommand\thetable{A.\arabic{table}}

\begin{sidewaystable*}[p!] %tabA.1
\vspone
\fontsize{8}{10.2}\selectfont\tabcolsep=3.5pt
\caption{\label{tabA1}List of the different measures collected within
ORCHAMP, their frequency, whether there are optional or not, to which
Essential Climate or Biodiversity variables, ECV and EBV, respectively,
they belong to\vspace*{-.5pc}}
\begin{tabular}{llllllcl}
\thead
Compartment & \parbox[t]{1.8cm}{\centering Corresponding protocol}
& \parbox[t]{1.8cm}{\centering Survey/method type} & Measured
parameters & Area & Frequency & \parbox[t]{1.8cm}{\centering Additional
information}\vspace*{2pt} & \parbox[t]{2.6cm}{\centering Essential
variables (EBV/ECV)}\\
\endthead
Climat & In situ climatic data
& \parbox[t]{3cm}{\raggedright Stand alone logger} &
\parbox[t]{3.2cm}{\raggedright Soil temperature and soil moisture measured at 5~cm below ground} 
& \parbox[t]{2.3cm}{\raggedright 1 logger per plot} & Hourly &&
\parbox[t]{2.9cm}{\raggedright ECV: Land/Biosphere/Soil moisture \& Land surface temperature}{\vspace*{7pt}}\\ 

Climat & Local climatic models & 
\parbox[t]{3cm}{\raggedright S2M meteorological and snow cover reanalysis} & 
\parbox[t]{3.2cm}{\raggedright Temperature (min/max/mean) for soil and air, precipitations, solar radiations, snow~depth, evapotranspiration\,{\ldots}}{\vspace*{7pt}} &
\parbox[t]{2.3cm}{\raggedright At the plot level (from~1958)} & Hourly &&\\

Environment & Pit soil & 
\parbox[t]{3cm}{\raggedright Cylinder sampling} &
\parbox[t]{3.2cm}{\raggedright Estimation of bulk density and stoniness}{\vspace*{7pt}} & 
\parbox[t]{2.3cm}{\raggedright 1 ``pit'' subplot outside each plot} & 
Once per plot &&\\

Environment & Pit soil & 
\parbox[t]{3cm}{\raggedright Soil physico-chemical properties} & 
\parbox[t]{3.2cm}{\raggedright Grain size, Cation exchange capacity, Corg, total N, P, pH\,{\ldots}}{\vspace*{7pt}} 
& \parbox[t]{2.3cm}{\raggedright 1 ``pit'' subplot outside each plot} & 
Once per plot &&\\

Environment & Pit soil & 
\parbox[t]{3cm}{\raggedright Soil organic carbon properties} & 
\parbox[t]{3.2cm}{\raggedright Thermograms obtained using Rock-Eval$^{\text{\textregistered}}$ thermal analysis}{\vspace*{7pt}} &
\parbox[t]{2.3cm}{\raggedright 1 ``pit'' subplot outside each plot} & 
Once per plot && 
\parbox[t]{2.9cm}{\raggedright ECV: Land/Biosphere/Soil carbon}\\

Environment & Pit soil & 
\parbox[t]{3cm}{\raggedright Mineral geochemistry analyses} & 
\parbox[t]{3.2cm}{\raggedright Elemental analysis using quantitative X-ray fluorescence spectroscopy}{\vspace*{7pt}} &
\parbox[t]{2.3cm}{\raggedright 1 ``pit'' subplot outside each plot} & 
Once per plot &&\\

Environment & Top soil & 
\parbox[t]{3cm}{\raggedright Soil physico-chemical properties} & 
\parbox[t]{3.2cm}{\raggedright Soil pH, soil organic matter, total soil carbon, nitrogen and phosphorus contents}{\vspace*{7pt}} & 
\parbox[t]{2.3cm}{\raggedright 3 ``topsoil'' subplot for $2*2$~m per plot} & 
\parbox[t]{2cm}{\raggedright Each resampling year} &&\\

Environment & Top soil & 
\parbox[t]{2.6cm}{\raggedright Soil biological activity} & 
\parbox[t]{3.2cm}{\raggedright Potential extracellular enzymatic activities} & 
\parbox[t]{2.3cm}{\raggedright 3 ``topsoil'' subplot for each $2*2$~m per plot}{\vspace*{3pt}} &
\parbox[t]{2cm}{\raggedright Each resampling year} & Optional &\\
\splittabular

Environment & Remote sensing & 
\parbox[t]{2.6cm}{\raggedright Satellite images from Sentinel-2, Landsat and MODIS} & 
\parbox[t]{3.4cm}{\raggedright Multi-spectral bands} &
\parbox[t]{2.3cm}{\raggedright At the plot, gradient and regional level} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} && 
\parbox[t]{3.2cm}{\raggedright EBV: Terrestrial/Ecosystem function/Primary productivity \& Ecosystem phenology}{\vspace*{7pt}}\\

Environment & Remote sensing & 
\parbox[t]{2.6cm}{\raggedright Satellite images from Sentinel-2 \& Landsat} & 
\parbox[t]{3.4cm}{\raggedright Multi-spectral bands} &
\parbox[t]{2.3cm}{\raggedright At the plot, gradient and regional level} &
\parbox[t]{2.2cm}{\raggedright Each resampling year} && 
\parbox[t]{3.2cm}{\raggedright EBV: Terrestrial/Community composition/Trait diversity}{\vspace*{7pt}}\\

Topography & Plot description & 
\parbox[t]{2.6cm}{\raggedright  Topographical measurements}{\vspace*{7pt}} &
\parbox[t]{3.4cm}{\raggedright Altitude, exposition, slope} & 
\parbox[t]{2.3cm}{\raggedright At the plot level} &&&\\

Biodiversity & Plant & 
\parbox[t]{2.6cm}{\raggedright Presence--absence} & 
\parbox[t]{3.4cm}{\raggedright  Species name (Taxref~V.14)} & 
\parbox[t]{2.3cm}{\raggedright 3 ``botany'' subplot of $3*10$~m per plot} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} &&
\parbox[t]{3.2cm}{\raggedright EBV: Terrestrial/Species populations/Species distributions}{\vspace*{7pt}}\\

Biodiversity & Plant & 
\parbox[t]{2.4cm}{\raggedright Pin-point transects} &
\parbox[t]{3.4cm}{\raggedright Species name for each contact (Taxref~V.14)} & 
\parbox[t]{2.3cm}{\raggedright 300 pinpoints per plot (within ``botany'' subplot)} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} &&
\parbox[t]{3.3cm}{\raggedright EBV: Terrestrial/Community composition/{\ubreak}Community abundance}{\vspace*{7pt}}\\

Biodiversity & Forest & 
\parbox[t]{2.4cm}{\raggedright Live tree inventories} &
\parbox[t]{3.2cm}{\raggedright Species name, location in the plot, Diameter at Breast Height (DBH)} & 
\parbox[t]{2.3cm}{\raggedright Entire $30*30$~m plot} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} && 
\parbox[t]{3.4cm}{\raggedright EBV: Terrestrial/Community composition/{\ubreak}Community abundance; EBV: Terrestrial/Species populations/Species distributions}{\vspace*{7pt}}\\

Biodiversity & Forest & 
\parbox[t]{2.6cm}{\raggedright Dead tree inventories} &
\parbox[t]{3.4cm}{\raggedright Deadwood type, species name if possible, wood decay level, bark presence, wood volume}{\vspace*{3pt}} & 
\parbox[t]{2.3cm}{\raggedright Entire $30*30$~m plot} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} &&\\
\splittabular

Biodiversity & Forest & 
\parbox[t]{2.6cm}{\raggedright Tree-related microhabitats inventory} & 
\parbox[t]{3.2cm}{\raggedright Inventory of tree-related microhabitats on all living and standing dead trees with a DBH ${>}$ 7.5~cm}{\vspace*{7pt}} & 
\parbox[t]{2.3cm}{\raggedright Entire $30*30$~m plot} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} & 
Optional &\\

Biodiversity & 
\parbox[t]{2.6cm}{\raggedright Below-ground} & 
\parbox[t]{2.3cm}{\raggedright Soil environmental DNA} & 
\parbox[t]{3.2cm}{\raggedright Soil biodiversity characterization, using two universal markers (eukaryote and bacteria) and four clade-specific markers (Fungi, Insects, Oligochaeta, Collembola)}{\vspace*{7pt}} &
\parbox[t]{2.2cm}{\raggedright 3 ``topsoil'' subplot of $2*2$~m per plot} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} && 
\parbox[t]{3.2cm}{\raggedright EBV: Terrestrial/Community composition/{\ubreak}Community abundance \& Interaction diversity}\\

Biodiversity & 
\parbox[t]{2.6cm}{\raggedright Large mammals} & 
Camera-traps &
\parbox[t]{3.2cm}{\raggedright Large mammal presence during the summer}{\vspace*{7pt}} & 
\parbox[t]{2.2cm}{\raggedright 2 cameras per plot} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year} & Optional &\\

Biodiversity & 
\parbox[t]{2.6cm}{\raggedright Sound producing animals (e.g., birds, arthropods, amphibians, mammals)} & 
Passive acoustic &
\parbox[t]{3.2cm}{\raggedright Acoustic diversity metrics, bird and invertebrate detection through artificial intelligence}{\vspace*{7pt}}&
\parbox[t]{2.2cm}{\raggedright 1 acoustic recorder per~plot} &
\parbox[t]{2.2cm}{\raggedright Each resampling year, 1~mn every 15~mn} &
Optional &\\

Biodiversity & 
\parbox[t]{2.6cm}{\raggedright Soil macroinvertebrates} &
Soil monoliths & 
\parbox[t]{3.2cm}{\raggedright Species name at highest resolution possible} & 
\parbox[t]{2.2cm}{\raggedright 4 monoliths $25*25$~cm ($*$20~cm deep when feasible)} & 
\parbox[t]{2.2cm}{\raggedright Each resampling year}
& Optional & 
\parbox[t]{3.2cm}{\raggedright EBV: Terrestrial/Species populations/Species distributions \& Species abundances}{\vspace*{7pt}}\\

Biodiversity & 
\parbox[t]{2.6cm}{\raggedright Soil macroinvertebrates} &
Pitfall traps & 
\parbox[t]{3.2cm}{\raggedright Species name at highest resolution possible} & 
\parbox[t]{2.2cm}{\raggedright 6 pitfalls per plot} &
\parbox[t]{2.2cm}{\raggedright Each resampling year} & 
Optional &
\parbox[t]{3.2cm}{\raggedright EBV: Terrestrial/Species populations/Species distributions \& Species abundances}{\vspace*{7pt}}\\

Biodiversity & 
\parbox[t]{2.6cm}{\raggedright Paleoecology} && 
\parbox[t]{3.2cm}{\raggedright Description of anthracological fossil contents in soil pits} &
\parbox[t]{2.2cm}{\raggedright 1 ``pit'' subplot outside each plot} & 
Once per plot & Optional & 
\parbox[t]{3.2cm}{\raggedright EBV: Terrestrial/Species populations/Species distributions \& Species abundances}{\vspace*{5pt}}
\botline
\end{tabular}
\end{sidewaystable*}

\gdef\appendixlabel{Appendix B}

\section{Consortium ORCHAMP{\hfill\break} Version 10/2024} \label{appB}
\subsection{Institutions involved in ORCHAMP} \label{appB1}

ORCHAMP is a consortium gathering a large range of actors: national and
regional park managers, botanical conservatory experts, natural area
conservatory managers, association, researchers from universities and
research institutions. The project is led by the LECA (Laboratoire
d'\'{E}cologie Alpine), located in Grenoble.

For additional information please visit our website:
\href{https://orchamp.osug.fr/home\%20}{https://orchamp.osug.fr/home}
or contact us: \href{mailto:orchamp@univ-grenoble-alpes.fr}
{orchamp@univ-grenoble-alpes.fr}

\begin{itemize}
\item \textbf{LECA} - Laboratoire d'\'{E}cologie Alpine; Univ.\ Grenoble
Alpes, Univ.\ Savoie Mont Blanc, CNRS, LECA, F-38000 Grenoble,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://leca.osug.fr/}


\item \textbf{EDYTEM} - Environnements, DYnamiques et TErritoires de la
Montagne; Univ. Savoie Mont Blanc, Univ. Grenoble Alpes, CNRS, EDYTEM,
Chamb\'{e}ry, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://edytem.univ-savoie.fr/}\vspace*{.25pc}

\item \textbf{INRAE LESSEM} - Laboratoire Ecosyst\`{e}mes et
Soci\'{e}t\'{e}s En Montagne; Univ. Grenoble Alpes, LESSEM, INRAE,
Grenoble, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www6.lyon-grenoble.inrae.fr/lessem/}\vspace*{.25pc}

\item \textbf{IMBE} - Institut M\'{e}diterran\'{e}en de
Biodiversit\'{e} et d'\'{E}cologie marine et continentale; Aix
Marseille Univ, Avignon Univ, CNRS, IRD, Marseille, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.imbe.fr/l-unite.html}\vspace*{.25pc}

\item \textbf{Jardin du Lautaret} - Univ. Grenoble Alpes, CNRS, Jardin
du Lautaret, F-38000 Grenoble, France\Tg</p>\Tg<p>\unskip\noindent
\href{https://www.jardinalpindulautaret.fr/}
{https://www.jardinalpindulautaret.com}\vspace*{.25pc}

\item \textbf{CEFE} - Centre d'Ecologie Fonctionnelle et Evolutive;
Univ. Montpellier, CNRS, EPHE, IRD, Montpellier, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.cefe.cnrs.fr/fr/}\vspace*{.25pc}

\item \textbf{CEN} - Centre d'\'{E}tudes de la Neige; Univ. Grenoble
Alpes, Universit\'{e} de Toulouse, M\'{e}t\'{e}o-France, CNRS, Centre
National de Recherches M\'{e}t\'{e}orologiques, Grenoble,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.umr-cnrm.fr/spip.php?rubrique85}

\item \textbf{INRAE ECODIV} - Laboratoire Etude et Compr\'{e}hension de
la bioDIVersit\'{e}; Univ. Rouen - Normandie, INRAE, ECODIV, Rouen,
France\Tg</p>\Tg<p>\unskip\noindent
\url{http://ecodiv.univ-rouen.fr/fr}

\item \textbf{Laboratoire de G\'{e}ologie de l'ENS}; CNRS, ENS, Univ.
PSL, Paris, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.geologie.ens.fr/spiplabocnrs/}

\item \textbf{INRAE URFM} - UR Ecologie des For\^{e}ts
M\'{e}diterran\'{e}ennes; INRAE, Avignon, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://ecologie-des-forets-mediterraneennes.paca.hub.inrae.fr/}

\item \textbf{INRAE UEFM} - UE Entomologie et For\^{e}t
M\'{e}diterran\'{e}enne; INRAE, Avignon, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://uefm.paca.hub.inrae.fr/}

\item \textbf{DYNAFOR} - Dynamiques et \'{E}cologie des Paysages
Agroforestiers; INRAE, INP, Castanet Tolosan, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.dynafor.fr/}

\item \textbf{SETE} - Station d'Ecologie Th\'{e}orique et
Exp\'{e}rimentale; Univ. Paul Sabatier, CNRS, Moulis,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://sete-moulis-cnrs.fr/fr/}

\item \textbf{CRBE} - Centre de Recherche sur la Biodiversit\'{e} et
d'Environnement; Universit\'{e} de Toulouse, IRD, INP, CNRS,
Universit\'{e} Toulouse 3 Paul Sabatier (UT3); Toulouse,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://crbe.cnrs.fr/}

\item \textbf{GEODE} - G\'{e}ographie de l'Environnement; Maison de la
recherche Univ. Jean Jaur\`{e}s, CNRS, Toulouse, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://geode.univ-tlse2.fr/} {\vspace*{.082pc}}

\item \textbf{PatriNat} - Centre d'expertise et de donn\'{e}es sur le
patrimoine naturel; OFB, MNHN, CNRS, IRD, Paris, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.patrinat.fr/fr} {\vspace*{.082pc}}

\item \textbf{AR+I} - Andorra Recerca $+$ Innovaci\'{o}, Sant Juli\`{a}
de L\`{o}ria, Andorre\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.ari.ad/en} {\vspace*{.082pc}}

\item {\textbf{LEM} - Laboratoire d'Ecologie Microbienne};
Univ. Lyon 1, CNRS, INRAe, VetAgro Sup, Lyon, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.ecologiemicrobiennelyon.fr/} {\vspace*{.082pc}}

\item \textbf{Eco\&Sols} - INRAE, IRD, CIRAD, Institut Agro,
Montpellier, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.umr-ecosols.fr/} {\vspace*{.082pc}}

\item \textbf{ZA Alpes} - Zone Atelier Alpes, Grenoble,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://za-alpes.osug.fr/} {\vspace*{.082pc}}

\item \textbf{ZA PYGAR} - Zone Atelier Pyr\'{e}n\'{e}es GARonne,
Toulouse, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://pygar.omp.eu/} {\vspace*{.082pc}}

\item \textbf{CBN Alpin} - Conservatoire Botanique National Alpin,
Domaine de Charance, 05000 Gap, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.cbn-alpin.fr/} 

\item \textbf{CBN Med} - Conservatoire Botanique National
M\'{e}diterran\'{e}en, Hy\`{e}res, France\Tg</p>\Tg<p>\unskip\noindent
\href{http://www.cbnmed.fr/src/prez.php}{http://www.cbnmed.fr/}


\item \textbf{CBN PMP} - Conservatoire Botanique National
Pyr\'{e}n\'{e}es et Midi-Pyr\'{e}n\'{e}es, Bagn\`{e}re-de-Bigorre,
France\Tg</p>\Tg<p>\unskip\noindent
\url{http://cbnpmp.blogspot.com/} 

\item \textbf{PN des Ecrins} - Parc National des Ecrins \& R\'{e}serve
int\'{e}grale du Lauvitel, Gap, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.ecrins-parcnational.fr/} {\vspace*{.082pc}}

\item \textbf{PN du Mercantour} - Parc National du Mercantour, Nice,
France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.mercantour-parcnational.fr/fr} 

\item \textbf{PN de la Vanoise} - Parc National de la Vanoise,
Chamb\'{e}ry, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.vanoise-parcnational.fr/fr} {\vspace*{.082pc}}

\item \textbf{PN des Pyr\'{e}n\'{e}es} - Parc National des
Pyr\'{e}n\'{e}es, Tarbes, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.pyrenees-parcnational.fr/fr}

\item \textbf{PNR du massif des Bauges} - Parc naturel r\'{e}gional du
massif des Bauges \& G\'{e}oparc mondial UNESCO, Le Ch\^{a}telard,
France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.parcdesbauges.com/fr/}
{\vspace*{.11pc}}

\item \textbf{PNR de Chartreuse} - Parc naturel r\'{e}gional de
Chartreuse, 11 Place de la mairie, 38380 Saint-Pierre-de-Chartreuse,
France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.parc-chartreuse.net/}
{\vspace*{.11pc}}

\item \textbf{PNR du Queyras} - Parc naturel r\'{e}gional du Queyras \&
R\'{e}serve Naturelle Nationale de Ristolas - Mont Viso, 3580 route de
l'Izoard, 05350 Arvieux, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.pnr-queyras.fr/}
{\vspace*{.11pc}}

\item \textbf{PNR du Mont-Ventoux} - Parc naturel r\'{e}gional du
Mont-Ventoux, Carpentras, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.parcduventoux.fr/}

\item \textbf{RNR du Massif de Saint-Barth\'{e}lemy} - R\'{e}serve
Naturelle R\'{e}gionale du Massif de Saint-Barth\'{e}lemy,
Monts\'{e}gur, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://ariegenature.fr/rnr/}
{\vspace*{.11pc}}

\item \textbf{RNR d'Aulon -- CEN Occitanie, La Fr\^{e}nette et Commune
d'Aulon}\Tg</p>\Tg<p>\unskip\noindent
{\url{https://www.cen-occitanie.org}}
{\vspace*{.11pc}}

\item \textbf{R\'{e}serves Naturelles Catalanes - RNN de la vall\'{e}e
d'Eyne} - F\'{e}d\'{e}ration des R\'{e}serves naturelles catalanes, 9
rue de Mahou 66500 Prades\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.reserves-naturelles-catalanes.org/}
{\vspace*{.11pc}}

\item \textbf{OFB} - Office fran\c{c}ais de la biodiversit\'{e},
gestionnaire de la R\'{e}serve nationale de chasse et de faune sauvage
d'Orlu\Tg</p>\Tg<p>\unskip\noindent
\href{https://www.ofb.gouv.fr/les-reserves/la-reserve-nationale-de-chasse-et-de-faune-sauvage-dorlu}
{https://www.ofb.{\unskip\break}gouv.fr/les-reserves/la-reserve-nationale-de-chasse-et-de-faune-sauvage-dorlu}
{\vspace*{.11pc}}

\item \textbf{Observatoire de la montagne, Commune d'Orlu}, Orlu,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.vallee-orlu.com/fr/la-reserve-nationale.html}

\item \textbf{Adyu l'Ome}, Orlu, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.adyulome.com/}

\item \textbf{ANA-CEN Ari\`{e}ge} - Conservatoire d'Espace Naturel
Ari\`{e}ge, Alzen, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://ariegenature.fr/}

\item \textbf{G\'{e}oparc du Chablais} - G\'{e}oparc mondial UNESCO du
Chablais, Thonon-les-Bains, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.geoparc-chablais.com/}

\item \textbf{Asters-CEN74} - Conservatoire d'espaces naturels de
Haute-Savoie \& R\'{e}serve Naturelle de Sixt - Fer \`{a} Cheval/Passy,
Sixt-Fer-\`{a}-Cheval, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.cen-haute-savoie.org/}

\item \textbf{CREA Mont-Blanc} - Centre de Recherches sur les
\'{E}cosyst\`{e}mes d'Altitude, Chamonix, France\Tg</p>\Tg<p>\unskip\noindent
\url{https://creamontblanc.org/fr}

\item \textbf{Natura 2000 Clar\'{e}e} - N\'{e}vache,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://inpn.mnhn.fr/site/natura2000/FR9301499}\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.nevache.fr/natura-2000/}

\item \textbf{Natura 2000 D\'{e}voluy-Durbon-Charance-Champsaur} 
\textbf{SMIGIBA} - Syndicat Mixte de Gestion Intercommunautaire du
Bu\"{e}ch et de ses Affluents Veynes, France\Tg</p>\Tg<p>\unskip\noindent
\url{http://hautes-alpes.n2000.fr/devoluy}\Tg</p>\Tg<p>\unskip\noindent
\url{http://www.smigiba.fr/}

\item \textbf{Grenoble-Alpes M\'{e}tropole} - Grenoble,
France\Tg</p>\Tg<p>\unskip\noindent
\url{https://www.grenoblealpesmetropole.fr/}

\item \textbf{ONF} - Office National des For\^{e}ts -
Grenoble\Tg</p>\Tg<p>\unskip\noindent 
\href{mailto:ag.isere@onf.fr}{ag.isere@onf.fr}
\end{itemize}

\subsection{ORCHAMP Consortium (contact persons are in italics)}

\begin{itemize}
\item \textbf{LECA}: \textit{Wilfried Thuiller, Am\'{e}lie Saillard},
Louise Boulangeat (2018--2021), Manon Bounous (2018), Irene
Calderon-Sanou, Philippe Choler, Camille Desjonqu\`{e}res, Arnaud
Foulquier, Ludovic Gielly, Priscilla Godfroy (2017--2019), Romain
Goury, Maya Gu\'{e}guen, Nicolas Le Guillarme, Cl\'{e}ment Lionnet
(2018--2023), Chlo\'{e} Mahieu, Camille Martinez-Almoyna, Marc Ohlmann
(2016-deceased in 2023), Gabin Piton (2016--2019), Julien Renaud,
Matthias Rohr, Guillaume Terpereau (Student 2021), Tristan Ubaldi
(Student 2019)

\item \textbf{EDYTEM}: \textit{J\'{e}rome Poulenard}, Nicolas Bonfanti,
Norine Khedim (2018--2022), Emmanuel Malet, Lise Marchal (2019--2023),
Erwan Messager, Yves Perrette

\item \textbf{INRAE LESSEM}: \textit{Georges Kunstler}, \textit{Vincent
Breton}, Laurent Berges, Nathan Daumergue, Adeline Fran\c{c}ois, Sophie
Labonne (retired in 2023), Laureline Leclerc (Student 2022), Eric
Mermin, Jean-Matthieu Monnet (2018--2019), Yoan Paillet, Mathias Pires,
Pascal Tardif (2016-retired in 2023)

\item \textbf{IMBE}: \textit{Fr\'{e}d\'{e}ric Guiter, Lenka Brousset},
C\'{e}cile Albert, Armin Bischoff, Manuel Cartereau, C\'{e}cile Chemin,
Emmanuel Corcket, Amandine Gasc, Rapha\"{e}l Gros, Fr\'{e}d\'{e}ric
Guibal, Fr\'{e}d\'{e}ric M\'{e}dail (2018--2019), Eric Meineri,
Jean-Philippe M\'{e}vy, Alexandre Millon (2018--2020), Pascal Mirleau,
Daniel Pavon, Yoann Pinguet, Herv\'{e} Ramone, Caroline Rocher, Arne
Saatkamp, Brigitte Talon

\item \textbf{Jardin du Lautaret}: \textit{Jean-Gabriel Valay},
\textit{J\'{e}r\^{o}me For\^{e}t (since 2023)}, Rolland Douzet, Lucie
Liger, Maxime Rome, Pascal Salze

\item \textbf{CEFE}: Jean-Fran\c{c}ois David, Cyrille Violle

\item \textbf{CEN}: \textit{Samuel Morin}, Matthieu Vernay, Matthieu
Lafaysse

\item \textbf{ECODIV}: \textit{Lauric C\'{e}cillon} (2016--2022)

\item \textbf{Laboratoire de G\'{e}ologie de l'ENS}: \textit{Lauric
C\'{e}cillon} (2016--2022), Laure Souc\'{e}marianadin (till 2022)

\item \textbf{INRAE URFM}: \textit{Bruno Fady}, William Brunetto,
Florence Courdier, Fr\'{e}d\'{e}ric Jean, Nicolas Mariotte

\item \textbf{INRAE UEFM}: \textit{Jean Th\'{e}venet}, Marianne
Corr\'{e}ard

\item \textbf{DYNAFOR}: Laurent Larrieu, \textit{Antoine Brin}, Laurent
Raison, C\'{e}lia Sirami, Catherine Bonnet, J\'{e}r\^{o}me Willm,
Alexis Carteron

\item \textbf{SETE}: Maxime Cauchoix

\item \textbf{CRBE}: \textit{J\'{e}r\^{o}me Murienne}, Gabrielle
Martin, Renan Destrade, Uxue Suescun

\item \textbf{GEODE}: Marie-Claude Bal, M\'{e}lanie
Saulnier

\item \textbf{PatriNat}: \textit{Olivier Delzons}, Philippe Gourdain,
Aur\'{e}lie Lacoeuilhe

\item \textbf{AR+I}: Benjamin Komac

\item \textbf{LBBE}: Vincent Miele

\item \textbf{LEM}: Juliana Almario, Lauren Gillespie

\item \textbf{Eco\&Sols}: \textit{Micka\"{e}l Hedde}, Matthias Brand,
Thomas Gelis, Nicolas H\'{e}non, Cyril Versavel, Luna Vion-Guibert

\item \textbf{ZA Alpes}: \textit{Mathilde Ratouis}, Isabelle Arpin
(till 2024), Renaud Jaunatre (since 2024), Marc Langenbach (since
2024), Erwan Messager (since 2024), Tamara M\"{u}nkem\"{u}ller (since
2024), J\'{e}r\^{o}me Poulenard (till 2024)

\item \textbf{ZA PYGAR}: Arnaud Elger

\item \textbf{CBN Alpin}: \textit{Bertrand Li\'{e}nard, Sylvain
Abdulhak, L\'{e}a Bizard}, Gilbert Billard, Pauline Debay, Luc Garraud,
Thomas Legland, Baptiste Merhan, Mathieu Michoulier, Gilles Pache,
David Paulin, Thomas Sanz, J\'{e}r\'{e}mie Van Es

\item \textbf{CBN Med}: \textit{Virgile Noble}, Pauline Bravet,
Beno\^{i}t Offerhaus, Henri Michaud, Ma\"{e}lle Le Berre, Mathias Pires
(till 2023), Julien Ugo, Marion Girardier

\item \textbf{CBN Pyr\'{e}n\'{e}es et Midi-Pyr\'{e}n\'{e}es}: Jocelyne
Cambec\`{e}des, \textit{Ludovic Olicard}, Micha\"{e}l Douette, Anne
Paris, Gilles Corriol

\item \textbf{PN des Ecrins \& RI du Lauvitel}: \textit{Richard Bonet},
Fran\c{c}ois Couilloud, C\'{e}dric Dentant, Damien Combrisson, Yoann
Bunz, J\'{e}r\^{o}me For\^{e}t (based in Jardin du Lautaret since 2023)

\item \textbf{PN du Mercantour}: \textit{Cl\'{e}mentine Assmann} (since
2024), S\'{e}bastien Honor\'{e}, Mathieu Krammer, Benoit
Labigand, Marie-France Leccia (till 2024), J\'{e}r\^{o}me Mansons,
Nathalie Siefert (till end 2023)

\item \textbf{PN de la Vanoise}: \textit{Vincent Aug\'{e}},
\textit{Jo\"{e}l Blanchemain}, Anne Bello (since 2024), Thierry
Delahaye, Nicolas Gomez (since 2024), Franck Parchoux (2017--2021)

\item \textbf{PN des Pyr\'{e}n\'{e}es}: \textit{Pierre Lapenu},
\textit{Olivier Jupille}, J\'{e}r\'{e}my Bauwin, Nils Paulet,
Oc\'{e}ane Pasquet, Sylvain Rollet,

\item \textbf{PNR du massif des Bauges}: \textit{Jean-Fran\c{c}ois
Lopez}, Richard Cousin

\item \textbf{PNR de Chartreuse}: \textit{Bastien Moisan, Laure
Belmont}, Jessica Bruggeman (2019--2022) 

\item \textbf{PNR du Queyras \& RN Ristolas-Mont-Viso}: \textit{Anne
Goussot, Pierpaolo Brena}, Alain Bloc (retired in 2023), Nicolas Tenoux

\item \textbf{PNR du Mont-Ventoux}: Baptiste Montesinos, Anthony Roux

\item \textbf{RNR du Massif de Saint-Barth\'{e}lemy}: \textit{Laurent
Servi\`{e}re}

\item \textbf{RNR d'Aulon}: \textit{Cyril Marmoex}, Loyann Boy,
Ma\"{e}lle Benureau, Coline Carr\'{e}, Lucyna Loriot (student 2024)

\item \textbf{R\'{e}serves Naturelles Catalanes - RNN de la vall\'{e}e
d'Eyne}: Josep Parera Casas, C\'{e}line Quelennec

\item \textbf{OFB - gestionnaire de la RNCFS d'Orlu}: \textit{Xavier
Rozec}

\item \textbf{Observatoire de la montagne, Commune d'Orlu}:
\textit{Christophe Lhez}

\item \textbf{Adyu l'Ome}: Pierre Guiton, Th\'{e}r\`{e}se Sabadie

\item \textbf{ANA-CEN Ari\`{e}ge}: \textit{Laurent Servi\`{e}re}

\item \textbf{G\'{e}oparc du Chablais}: \textit{Sophie Justice}

\item \textbf{ASTERS \& RN Sixt-Passy}: \textit{Carole Birck}, Olivier
Billant, Jean-Jos\'{e} Richard-Pomet

\item \textbf{CREA}: \textit{Anne Delestrade}, Bradley Carlson
(2017--2023), Colin Van Reeth, Jeremy Froidevaux (since 2024)

\item \textbf{N2000 Clar\'{e}e}: \textit{Laure Vuin\'{e}e}

\item \textbf{Natura 2000 D\'{e}voluy-Durbon-Charance-Champsaur \&
SMIGIBA}: \textit{Eric Hustache}

\item \textbf{Grenoble-Alpes M\'{e}tropole}: \textit{Alexandre
Mignotte}, Pierre-Eymard Biron (retired in 2021), Yann Kohler

\item \textbf{ONF}: Carole Desplanque, Laurent Lathuilli\`{e}re

\item \textbf{Ind\'{e}pendants}: Jean-Marie Dupont (Apexe),
Fran\c{c}oise Laigneau, Christophe Perrier (till 2020), Olivier Senn,
Alexandre Pailh\'{e}-Belair (Berger, estive d'Aulon), Alexis James
(Berger, estive d'Aulon)
\end{itemize}

\subsection{ORCHAMP funding} \label{appB3}

Each institution involved in the consortium is co-funding the project
either through in-kind funding or participation to specific projects. 

\unskip\noindent
\textbf{ANR - Agence Nationale de la Recherche}: GlobNets
(ANR-16-CE02-0009), Origin-Alps (ANR-16-CE93-004), TransAlps
(ANR-16-CE02-0009) \& EcoNet (ANR-18-CE02-0010-01)

\unskip\noindent
\textbf{ANR ``Investissement d'Avenir''}: Trajectories
(ANR-15-IDEX-02), Montane (OSUG@2020: ANR-10-LAB-56), Idex UGA
(ANR-15-IDEX-02); MIAI@Grenoble Alpes (ANR-19-P3IA-0003)

\unskip\noindent
\textbf{AFB - Agence Fran\c{c}aise pour la Biodiversit\'{e}}:
Sentinelles des Alpes 2018--2019

\unskip\noindent
\textbf{OFB - Office fran\c{c}ais de la biodiversit\'{e}}: Sentinelles
des Alpes 2020--2022, Sentinelles des Alpes 2023, Sentinelles des Alpes
2024

\unskip\noindent
\textbf{AURA - R\'{e}gion Auvergne-Rh\^{o}ne-Alpes}: CBNA regional
convention

\unskip\noindent
\textbf{SUD-PACA - R\'{e}gion Sud Provence-Alpes-C\^{o}te d'Azur}:
Support to CBNA and CBNMED

\unskip\noindent
\textbf{LTSER ZAA - Zone Atelier Alpes} (CNRS, INRAE; membre de eLTER)

\unskip\noindent
\textbf{IS\`{E}RE - Le D\'{e}partement}: Appel \`{a} projets
Biodiversit\'{e} 2020

\unskip\noindent
\textbf{OSUG - Service d'observation}: Appel \`{a} projets 2020

\unskip\noindent
\textbf{ECCOREV - Ecosyst\`{e}mes Continentaux et Risques
Environnementaux}: Appel \`{a} projets 2019

\unskip\noindent
\textbf{Interreg Alcotra FEDER} - PITEM Biodiv'ALP 2019-2023 PS3. -
GEBIODIV

\unskip\noindent
\textbf{Interreg POCTEFA}: FLORAPYR3D 2024--2026

\unskip\noindent
\textbf{BIOSEFAIR INRAE}: SICCCUB 2021--2023

\unskip\noindent
\textbf{R\'{e}gion Occitanie}: PAACTe R\'{e}gion Occitanie, 2022--2024

\unskip\noindent
\textbf{Interreg Alcotra FEDER}: PITEM Biodiv'ALP

\unskip\noindent
\textbf{CNRS initiative EC2CO} - Projet Microphos AAP 2021--2023

\medskip\unskip\noindent
\textbf{Other local fundings}: Parc National des Ecrins (PNE),
R\'{e}serve int\'{e}grale du Lauvitel, Parc National du Mercantour
(PNM), Grenoble Alpes M\'{e}tropole, Agence de l'eau
Rh\^{o}ne-M\'{e}diterran\'{e}e Corse (AERMC), Electricit\'{e} de France
(EDF), Mairie du D\'{e}voluy, Institut de Radioastronomie
Millim\'{e}trique (IRAM), Communaut\'{e} de communes de la vall\'{e}e
de Chamonix Mont-Blanc, Fonds europ\'{e}en agricole pour le
d\'{e}veloppement rural (FEADER).

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