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\begin{document}

\title{BACKLAND: spatially explicit and high resolution pollen-based BACKward
LANDscape reconstructions.}



\author[1]{Clara Plancher}%
\author[1]{Florence  Mazier}%
\author[2]{Thomas  Houet}%
\author[3]{Cédric Gaucherel}%
\affil[1]{GEODE}%
\affil[2]{Rennes 2 University}%
\affil[3]{UMR5120}%


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  \date{May 15, 2024}


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\begin{abstract}
Studying the interactions between humans, landscapes and biodiversity is
necessary for the sustainable management of socio-ecosystems and
requires long-term reconstructions of past landscapes, improving the
integration of slow ecological processes. The main source of information
on past vegetation is fossil pollen, but pollen data are biased by
inter-taxonomic differential production and dispersal. The Landscape
Reconstruction Algorithm (LRA) approach is today the most widely used to
correct pollen data for these biases and also allows to identify the
spatial extent of the local vegetation reconstruction zone via the
Relevant Source Area of Pollen (RSAP). While LRA estimates have already
been integrated into certain past land-cover mapping approaches, none
have been designed to allow the diachronic reconstruction of the
land-cover mosaic of a landscape over the long term combining the
following points: the direct integration of LRA estimates as a source of
variability in the composition and distribution of pollen taxa, without
resorting to multiple scenarios, and the integration of spatiotemporal
autocorrelation in the taxa distribution between two periods. In this
study, we propose an innovative approach for Backward Landscape
reconstruction (BACKLAND), combining these previous points and
estimating past landscapes within a set of RSAPs. Based on three stages
using parsimonious assumptions and easy-to-implement probabilistic and
statistical tools, the implementation of this approach requires LRA
estimates of sites with overlapping RSAPs, botanical data, a Digital
Elevation Model and two recent land-cover maps. Developed and tested on
a small study area within the mountain landscape of the Bassi\selectlanguage{ngerman}ès valley
(French Pyrenees), BACKLAND achieved the reconstruction of a past
land-cover map representing eight land-cover types at a spatial
resolution of 20m with a good level of accuracy. We showed in this study
the originality of this approach and discussed its potential for
palaeoenvironmental studies, historical ecology and the management of
socio- ecosystems.%
\end{abstract}\selectlanguage{ngerman}%



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\verb`Ecography2023_MainText.docx`  available at \url{https://authorea.com/users/596995/articles/630238-backland-spatially-explicit-and-high-resolution-pollen-based-backward-landscape-reconstructions}

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