# From Geodiversity to Geofunctionality: Quantifying Geodiversity-Based Ecosystem Services for Landscape Planning in French Guiana

Ottone Scammacca, François Bétard, David Montagne, Lucas Rivera, Célia Biancat, Geoffrey Aertgeerts, Arnauld Heuret

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## To cite this version:

Ottone Scammacca, François Bétard, David Montagne, Lucas Rivera, Célia Biancat, et al. From Geodiversity to Geofunctionality: Quantifying Geodiversity-Based Ecosystem Services for Landscape Planning in French Guiana. Geoheritage, 2024, 16, pp.3. 10.1007/s12371-023-00910-0. hal-04348387

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From geodiversity to geofunctionality: quantifying geodiversity-based ecosystem services for landscape planning in French Guiana

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4 Ottone Scammacca<sup>a*</sup>, François Bétard<sup>b</sup>, David Montagne<sup>c</sup>, Lucas Rivera<sup>d</sup>, Célia Biancat<sup>d</sup>,

5 Geoffrey Aertgeerts<sup>c</sup>, Arnauld Heuret<sup>f</sup>

6 <sup>a</sup> UMR Prodig, CNRS, Université Paris 1: Panthéon-Sorbonne, IRD, AgroParisTech,

7 Aubervilliers, France

8 <sup>b</sup> Sorbonne Université, UR Médiations, Paris, France

9 <sup>c</sup> Université Paris-Saclay, INRAE, AgroParisTech, UMR Ecosys, 91120 Palaiseau, France

10 <sup>d</sup> BRGM 93700 Cayenne, French Guiana

11 <sup>e</sup> BRGM 35700 Rennes, France

12 <sup>f</sup> Geosciences Montpellier, CNRS, Université de Montpellier, Université de Guyane

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14 *Corresponding author: Ottone Scammacca (ottone.scammacca@ird.fr)

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Reference:

18 Scammacca, O., Bétard, F., Montagne, D., Rivéra, L., Bianca, C., Aertgeerts, G., Heuret, A.,

19 From Geodiversity to Geofunctionality: Quantifying Geodiversity-Based Ecosystem Services for Landscape Planning in French Guiana. Geoheritage 16, 3 (2024).

21 https://doi.org/10.1007/s12371-023-00910-0

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Abstract

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Geodiversity assessment gained a prominent interest in the geoscientific community and beyond. However, it is not always sufficient for land planning or geoconservation. It is then pivotal to account for the contribution of functional geodiversity (i.e. geofunctionality), for instance declining the ecosystem services (ES) cascade model. However, by our knowledge geodiversity-based ES (GES) have been rarely quantified.

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This paper aims at adapting existing ES-related approaches to quantify and map GES in French Guiana, a French Overseas territory located in the Amazon, where on-going land use changes might affect ES supply. Seven GES were spatially assessed through an indicator-based approach accounting for both offered and used GES and merged into multiservices maps. Multiservices maps were then combined with a hemeroby index to highlight geofunctionality hotspots. Difference maps were finally used to compare geodiversity and geofunctionality patterns.

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The ES framework seems an effective way to quantitatively assess geofunctionality. Geodiversity and geofunctionality do not follow the same spatial patterns: very geodiverse areas can be poorly functional and vice-versa. Therefore, geodiversity and geofunctionality need to be both considered when it comes to landscape planning. This might be enhanced through hotspots mapping to highlight priority areas for planners. This study also focuses on the role of human inputs in GES supply and raises questions about the selection of proper indicators that should fit each step from the ES supply to management. High-quality datasets must be available and their occasional absence is a central matter of land planning that must be addressed before every decision-making process.

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Keywords: Geodiversity, Ecosystem Services, Hotspots, Landscape, Human Inputs, French Guiana

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# 1. Introduction

Despite their pivotal role in socio-ecological functioning, abiotic and interfacial (i.e. soils) components of natural diversity still tend to do not find their due place within land planning, environmental management and conservation strategies (Brilha et al. 2018; Boothroyd and McHenry 2019), which often focus mostly on biodiversity (Chakraborty and Gray 2020).

Such considerations converged within the development of geoecological approaches (Tandaric 2015) and of the concept of “geodiversity”, as a new prism to look at all non-living components of nature and as a new geological and geographical paradigm (Claudino-Sales 2021).

Geodiversity, abiotic equivalent of biodiversity (Gray 2011), is generally defined as “the natural range (diversity) of geological (rocks, minerals, fossils), geomorphological (landforms, topography, physical processes), soil and hydrological features,” including “their assemblages, structures, systems and contributions to landscapes” (Gray 2013).

Despite its scope encompasses a wider range of activities included in land-planning (Serrano and Ruiz-Flaño 2007; Schrodt et al. 2019), geodiversity has been mainly related to geoconservation. The possibility, conditions and usefulness of a broader operationalization of this concept need to be tested and demonstrated. Moreover, the assessment of geodiversity in terms of site-specific richness and abundance (Zwolinski et al. 2018) appears insufficient to support both land planning and geoconservation (Scammacca et al. 2022a). It is therefore critical to apprehend the ensemble of contributions that geodiversity provides to socio-ecological functioning (i.e. geofunctionality) (Volchko et al. 2020; Scammacca et al. 2023a).

Over the last years, the scientific community suggested that declining the “ecosystem services” (ES) concept to geodiversity might be an effective way to assess such contributions (Kløve et al. 2011; Gray et al. 2013; Van der Meulen et al. 2016; Reverte et al. 2020; Volchko et al., 2020; Carrión-Mero et al. 2022) and the role of geodiversity in the delivery of many ES has been widely recognized (Gray, 2011; Van Ree et al. 2017; Fox et al. 2020; Crisp et al. 2021). ES can be defined as the contributions that ecosystems provide to human well-being (Muller and Burkhard 2012; Haines-Young and Potschin 2018), sometimes through human inputs (Jones et al. 2016; La Notte et al. 2017) and which do not exist in isolation from people's needs, demand, access and priorities (Haines-Young and Potschin 2010; Heink et al. 2016).

The ES approach, particularly because of its suitability to assessment and mapping exercises (Martinez-Harms and Balvanera 2012; Burkhard and Maes 2017), is widely recognized as a
potential tool to improve environmental monitoring and land planning through holistic thinking about ecosystem processes and human well-being (Wei and Zhan 2023). This might be particularly useful in remarkable areas of natural richness and diversity such as the Amazon basin, where rapid on-going land use changes affect ES supply (Richards and Van Wey 2015; Jakovac et al. 2016; Ferreira et al. 2021). Located in this region, the only continental French and European Overseas territory of French Guiana has almost 96% of its surface covered by Amazon rainforest. Although it is one of the least densely populated areas in the world, population growth rate and the related needs in terms of infrastructures, agricultural supplies and economic growth, are exacerbating, potentially affecting ES supply. Land use impacts on ES supply are often analyzed through hotspots mapping, which also supports land planners in geographic prioritization (Orsi et al. 2020). Analogously, hotspots can be used to identify highly geodiverse and highly threatened areas (Bétard and Peulvast 2019).

Despite it might play a greater role than biotic components in the delivery of some services (Heink et al. 2016; Slabbert et al. 2022), geodiversity has been often neglected in practice in the developments of the ES concept. Although soil-related ES gained a growing interest over the last decades (Baveye et al. 2016; Fossey et al. 2020; Scammacca et al., 2023b), geodiversity-based ES (GES) are still considered as an "abiotic extension" (Gray, 2018) in current ES classification systems (Van der Meulen et al. 2016), creating a dichotomy between the role of biotic and abiotic contributions in ES supply (Fox et al. 2020).

By our knowledge, quantitative assessments of GES remain uncommon (Butorac and Buzjak 2020; Miklos et al. 2020; Reverte et al. 2020) and current studies provide often qualitative assessments of GES and of their relationships with biodiversity and geodiversity (Alahuta et al., 2018). Recently, Balaguer et al. (2023) applied the matrix-based approach to assess how land use changes might affect ecosystem services provided by geodiversity in Brazil. Nervertheless, the scarcity of quantitative studies might limit the full implementation of the geodiversity concept within the ES framework towards the accomplishment of Sustainable Development Goals (Van Ree and Van Beukening 2016; Brilha et al. 2018; Bitoun et al. 2022). French Guiana geodiversity, despite it is historically associated to gold mining, played an important role in the past dynamics of the region and it has a wider potential of contributing to the supply of multiple ES (Scammacca et al. 2022a). Because of its socio-geo-ecological features, this territory represents a major challenge for sustainable land planning and conservation (Aubertin and Pons 2017; Budoc 2017). Previous studies focused on the assessment of ES in the region (Sieber
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et al. 2021) mainly based on land-use proxies or specific biotic parameters (Trégarot et al.

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2021).

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This study has therefore the purpose to: i) attempt at a first quantification of geofunctionality
in French Guiana, in terms of GES supply; ii) analyze the spatial patterns of geodiversity and
geofunctionality and; iii) explore approaches to account for geodiversity and geofunctionality
within sustainable land planning strategies in French Guiana discussing the challenges of data
unavailability and the pertinence of potential ES management indicators.

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## 2. Applying the ecosystem service cascade model to geodiversity

The ES paradigm has been conceptualized through the “cascade model” which distinguishes between ES components (e.g. ecosystem processes, functions, services, benefits) and linking the two ends of ES supply chain (Haines-Young and Potschine 2010). This model has been often revisited, particularly to fit land planning requirements (Villamagna et al. 2013; Von Haaren et al. 2014; La Notte et al. 2017; Zhang et al. 2022). Von Haaren et al. (2014) re-adapted the cascade model proposing a practice-oriented ES evaluation model identifying: “offered” ES (or ES capacity), as the totality of ecosystem contributions that could, at least potentially, be utilized by humans, and “used” ES (or ES flow) which are those currently utilized by humans (Von Haaren et al. 2014).

This distinction might also offer complementary perspectives elucidating the aspect of human inputs within planning objectives (Albert et al. 2016). Since landscapes are often modified by societies, human-derived capital – in terms for instance of knowledge, human interventions and environmental management (Fig.1) – is often necessary for the delivery of many ES (Jones et al. 2016). However, this dimension is rarely considered in ES assessments and it is currently unclear to what extent human influence is included in the ES concept (Heink et al. 2016).

In order to quantify GES, this study follows the Von Haaren et al. (2014) model. The link between functions and services is reflected by the offered or used ES supply, depending on the intensity of the human input involved (Fig. 1). On one hand, the assessment of offered ES implies the acknowledgement (i.e. inventory, prospection, knowledge) of the capacity to deliver the service according to user needs. On the other, the assessment of used ES might imply human inputs involving planning and management practices (e.g. infrastructures construction, exploitation, transformation, conservation) that allow for the offered service to be concretely accessible and enjoyed by users according to their demands (Fig. 1).
## 3. Study area

The current approach is tested in French Guiana, a French region located in South America (Fig. 2). Its geology, locally documented (Choubert 1949; Magnien et al. 1990) and described by the scientific literature (Choubert 1974; Milesi et al. 2003; Théveniaut et al. 2012) can be framed within the formation of the Precambrian terrains of the Guiana Shield (Delor et al. 2003). French Guiana can be divided into two main geomorphological domains: i) the coastal plains of the lowlands (4% of the territory), underlined by ancient and recent Quaternary sediments (Fig. 2a); ii) the uplands of the inner regions (96% of the territory), with moderate relief energy (e.g. hills of granitic inselbergs and volcano-sedimentary peaks reaching a maximum of 850 meters a.s.l.), and composed of outcrops of the oldest crystalline Paleoproterozoic basement formed during the crustal growth of the Transamazanian orogeny (2.25-1.9 Ga) (e.g. metamorphic, magmatic, sedimentary and volcanic rocks). Of particular interest, two greenstone belts, mainly composed of meta-volcanic lithology with greenschist to amphibolite facies metamorphism and of poorly known meta-volcano-sedimentary rocks (Fig 2a), host most of gold primary and placer deposits, targeted by legal and illegal mining (Scammacca et al. 2022b). Water resources are distributed among groundwater bodies (84,000 km² in confined aquifers) and a dense and tufted network of surface waters (20,000 km of length) spread across the territory (DEAL, 2013). Soils are well documented although data are scattered and often non-harmonized. They are greatly heterogeneous as a function of petro-geochemistry diversity of parent materials, geomorphological structures, tectonics, weathering through time and hydrological dynamics (Boulet 1979; Palvadeau 1999; Ferry et al. 2003). Lowland soils, developed on coastal plains include moderately developed soils, Histosols, Gleysols, Podzols, while highland soils include Ferralsols, ferric Cambisols, Acrisols, Plinthosols and Podzols (Leprun et al. 2001).

Human settlements and activities are mostly located along the coastal areas and along the borders of Maroni and Oyapock rivers (Fig. 2b). Formal and informal human activities range from artisanal, industrial (e.g. fishing, hunting, mining, space sector, manufacturing, energy, agriculture, forestry) to commerce, construction, water management, tourism and transport. French Guiana hosts more than 280,000 inhabitants in approximately 84,000 km² but with the second highest population growth rate among French regions. With 96% of its surface covered by the Amazon rainforest and 90% under State ownership (Iedom 2021), land tenure is a major issue, leading to challenges for future land management and conservation strategies.
# 4. Materials and methods

## 4.1 Considered GES and indicators selection

Seven GES were selected in order to include the main ES classes (e.g. provisioning, regulating, cultural services) and according to the significant planning and environmental challenges in French Guiana. (Table 1): four provisioning services such as mineral commodities supply (MM), non-metallic raw materials supply (MnM), surface water for drinking purposes (WS), groundwater for drinking purposes (WU), two regulating and maintenance services such as natural habitat regulation (HAB) and flood control (FC) and one cultural service, i.e. recreational activities (GC).

GES were assessed in their offered and used dimensions and mapped firstly on a single-service basis and then combined to obtain multi-services maps (Fig. 3). GES were assessed and mapped through spatially explicit indicators selected according to existing studies (Fig. 4) and listed with the related input data in Table 2. Abiotic indicators were specifically chosen to underline the role of geodiversity in ES supply, sometimes in combination with human or social data. Input data were collected on GIS-based platforms such as *GeoGuyane* and *Guyane SIG*. More details are available in the Supplementary materials. Although their non-renewable character, raw materials supply services (MM and MnM) were addressed because it might be significant in landscape-oriented ES frameworks for planning perspectives (Kandziora et al. 2013), especially in such areas of interest. Their offered dimension was assessed respectively based on prospected mineral occurrences and lithological favorability (Fig. 4a and b) while the used dimension was based on the location of legal mines and quarries (Grêt-Regamey and Weibel 2020 (Fig. 4h and i). Water supply services (WS and WU) were quantified based on the actual good status of surface waters (Fig.4c) and aquifers location (Fig.4d) for their offered dimension (Albert et al. 2016; Reverte et al. 2020) and on drinking water points for the used dimension (Fig.4j and k). Landscape capacity to support biodiversity habitats (HAB) was assessed through biodiversity potential levels described by Guitet et al. (2015a), which identify forest habitats mainly based on geomorphology (Guitet et al. 2013), one of the main drivers of biodiversity changes in the Amazon basin (Guitet et al. 2015b) (Fig.4e). The surface of protected areas indicated the used dimension of the service (Fig.4l). Flood control (FC) was assessed based on the presence of natural barriers such as wetlands (Kandziora et al. 2013) while flood-prone areas were identified by Guitet and Brunaux (2017) through the HAND topographic algorithm
211 (Rennó et al. 2008) (Fig. 4f). The assessment of the used service was based on the location of
212 wetlands in areas covered by flood-risk prevention plans (Albert et al. 2016) (Fig. 4m).

213 Recreational activities offer (GC) was assessed according to the number of currently inventoried
214 geosites (Nontanovanh and Roig 2010; Roig and Moisan 2011; Bourbon and Roig 2013) and the
215 presence of outcrops of granitic inselbergs (Fig. 4g), considered as one of the uncommon ways
216 to observe French Guiana lithology (Ferry et al. 2003). The used dimension was characterized
217 integrating a distance parameter (Albert et al. 2016) between geosites and the road network
218 (Fig. 2n).

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220 4.2 GES assessment and mapping

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222 After initial data were pre-processed (Fig. 4) (e.g. data merging, data extraction, geometry
223 validation) using Qgis Desktop 3.28.5 and ArcMap 10.8.2 software, they were intersected, for
224 each GES, with a 10x10 km grid-cell layer covering the whole continental part of the study area
225 (922 cells).

226 Data were summarized according to each cell depending on the units of the initial data (Table
227 2). For instance, data expressed in units of surface (e.g. used MM, offered and used MnM,
228 offered WU, used HAB, offered and used FC), length (e.g. offered WS and usedGC) or volume
229 (e.g. MnM) were summed up for each cell while data expressed in terms of numbers of
230 punctual geometries were simply counted (e.g. offered MM andGC, used WS and WU).
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232 Surfaces, lengths and point counting were calculated automatically using Qgis Desktop 3.28.5
233 functions. For the GC service, distances were calculated using the "Join attributes by nearest"
234 tool and then averaged for each cell.

234 Each service map was joined by attributes to the original cell-grid layer. The values expressed
235 for each service were re-classified in four classes using Jenks natural breaks, ranging from 1
236 (i.e., low supply) to 4 (i.e., very high supply).

237 The scores of offered and used single-service maps were summed to obtain multi-service maps
238 representing, respectively, total offered and used geofunctionality (Gf). Gf maps were re-
239 classified in four classes using Jenks natural breaks, ranging from 1 (i.e., low supply; sum equal
240 to 7) to 4 (i.e., very high supply; sum superior to 18)

241 All the final maps were interpolated through kriging on ArcMap 10.8.2 in order to limit border
242 effects, often caused by the homogenization of partial data contained in bordering cells.
## 4.3 Updating the Geodiversity Index (Gt)

The Geodiversity Index (Gt) for the study area was originally assessed by Scammacca et al. (2022a), as the sum of four partial thematic sub-indices (e.g. lithological and unithified diversity, mineral diversity, hydrodiversity, and geomorphodiversity). The index was here updated following two steps:

i) A pedodiversity sub-index, was integrated in the original assessment through a coarse regional Soil Map of French Guiana (Blancaneaux 1979), recently available as a digital vector layer with a spatial scale of 1:1,000,000.

ii) The hydrodiversity sub-index was recalculated using the same input data (e.g. surface and underground waters) and counting the number of different entities in each cell. Surface waters were categorized by their Strahler rank, as suggested by the 2019 Water Planning report (OEG, 2020).

## 4.4 GES and geodiversity relationships

The relationships between offered and used GES and between Gf and Gt levels were analyzed through difference mapping. Changes in spatial patterns and levels were obtained by adding a new field in the attribute table and calculating the relative difference (expressed in %) between offered and used GES according to the following equation (Eq. 1):

$$RD_{Gf} = 100 \frac{Gf_o - Gf_u}{Gf_o}$$

Where RD<sub>Gf</sub> is the relative difference between offered (Gf<sub>o</sub>) and used (Gf<sub>u</sub>) geofunctionality. Changes between total offered and potential Gf and Gt indices were calculated according to the equation (Eq. 2):

$$RD_{Gtf} = 100 \frac{Gt - Gf_{o,u}}{Gt}$$

Where RD<sub>Gtf</sub> is the relative difference between the geodiversity index values Gt and offered (Gf<sub>o</sub>) and used (Gf<sub>u</sub>) geofunctionality.
Spatial differences were classified on a range of seven classes translating the direction and the intensity of the change: for difference maps related to offered and used GES, areas with negative values infer that used GES levels are superior to offered GES levels while areas with positive values infer that offered GES levels are superior to used GES levels. When the value is equal to zero, offered and used GES show the same levels. The same considerations can be applied to the maps of relative difference between total Gt and offered or used Gf (Eq. 3).

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## 4.5 Geodiversity and geofunctionality hotspots

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According to the approach proposed by Bétard and Peulvast (2019), a Threat Index (TI) was combined to Gt in order to obtain a Sensitivity Index (SI) and highlight geodiversity hotspots. In this study TI was assessed based on the Hemeroby "M" index (Steinhardt et al. 1999), which is an integrative measure of human impacts on ecosystems (Lausch et al. 2015) and has the advantage to be both ecologically well-founded and easily applicable (Frank et al. 2012). This index is often used to evaluate the naturalness degree of an area (Walz 2008) and can be integrated within the assessment of ecological functioning (Frank et al. 2012).

The index was calculated based on the Regional Land Use Plan (RLUP) of French Guiana (CTG 2016), at the scale of 1:100,000 as spatial input data. The RLUP defines the general allocation of areas to given land uses according to predefined planning objectives. It divides the territory in eleven land use categories (Fig. 5a) translating current and future activities. A Hemeroby degree (Fig. 5b) was assigned to each land use category as suggested by Walz and Stein (2014), (Table 3). Since the study area is cartographically divided in 922 cells with equal size, a simple area-weighted Hemeroby index was calculated using the following equation (Eq. 3) (Walz and Stein, 2014):

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$$M_w = \sum_{h=1}^{n} f_n \times h$$

Where $M_w$ is the simple area-weighted Hemeroby index, $n$ is the number of degrees of Hemeroby (here: $n = 7$), $f_n$ is the proportion (%) of category $n$, $h$ is the degree of Hemeroby.

The calculation was performed intersecting the Hemeroby degree map (Fig.5b) with the original grid layer. After summarizing the intersected values to the grid cells, Eq. 4 was applied. The TI map was then interpolated through kriging showing low and high threat areas (Fig. 5d).

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303 Finally, the SI was automatically obtained by the combination of the TI raster map with the $Gt$ raster map (Fig. 3) using ArcMap Raster Calculator tool and according to the equation (Eq. 4):

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306 $SI_{GI} = TI \times Gt$
307 The same equation was applied to offered and used $Gf(Gf_{o,u})$ raster maps as following (Eq. 5):

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$SI_{GES} = TI \times Gf_{o,u}$

311 The SI classes were normalized based on the overall minimum (i.e. 45.9) and maximum (i.e. 312 1,424.3) values of the three maps.

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314 5. Results
315 5.1 GES levels and maps

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317 Figure 6 shows the single-service offered (Fig. 6a to g) and used (Fig.6i to o) GES maps while
318 overall averaged GES levels are synthetized in Figure 7. Globally speaking, the results highlight:
319 i) some services which are generally largely used – in terms of exploitation, management or
320 conservation strategies – compared to their offer (e.g. MM, HAB, GC); ii) some services which
321 are mainly underused – which does not imply a necessity of use – such as MnM, WS, WU, FC;
322 iii) general sustainable uses with potential overuses of the resources (e.g. MM, HAB, GC) but
323 which must be analyzed very carefully according to the methodological choices and the
324 selected indicators.

325 Raw material supply for mining (MM) shows high offered levels along the two greenstone belts
326 (Fig. 6a), although used levels are only higher in the northern belt because of formal
327 interdictions in the southern one, where the Amazonian Park is located (Fig. 6i) and where
328 illegal gold mining is very active (Jébrak et al. 2021). MnM offered levels are higher in all the
329 Quaternary sedimentary formations of the coastal plain (Fig. 6b) – where used MnM levels are
330 mainly located (Fig. 6j) – and in the TTG units (Fig. 2a), particularly in the western area of the
331 territory. Water supply (e.g. WS and WU) and natural habitat regulation (i.e. HAB) are offered
332 almost in the whole study-area (Fig. 6c, d and e). WS and WU are only locally used along the
333 coastal and riverine regions (Fig. 6k and l) while HAB shows moderate to high levels in almost
334 the totality of the territory (Fig. 6m). Despite FC shows moderate levels throughout French
335 Guiana (Fig. 6f), used levels are mainly located in coastal areas (Fig. 6n). GC is supplied in specific
336 spots spread across the whole region (Fig. 6g), mainly in the coastal, eastern and southern
337 regions. The southern areas are less accessible and, therefore, show lower used levels (Fig. 6o).
338 Unlike all offered GES, which show overall higher levels with the exception of MM and GC (Gf<sub>o</sub>
339 = 1.3), used GES display globally low levels (Fig. 7). Only HAB differences map highlights multiple
340 areas where offered levels are inferior to the used ones (Fig. 6u). Nevertheless, when averaged
341 over the whole study area, levels are higher for the offered HAB service (Gf<sub>o</sub> = 3) than for the
342 used one (Gf<sub>o</sub> = 2.6) (respectively Fig. 6h, p and x).

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## 5.2 Comparing Geodiversity (Gt) and geofunctionality (Gf) levels

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Figure 8 compares the *Gt* (Fig. 8a) with offered and used *Gf* (Fig. 8b and c). Except for the northern and southern belts where *Gt* levels are at their peaks, *Gf* levels are higher than *Gt* levels (Figure 8d). Areas in the western part of French Guiana, characterized by TTG complexes (Fig. 6b) show higher offered *Gf* levels with relative differences compared to *Gt* that is locally higher than 100% (Fig. 8d). When averaged over the whole study area, offered *Gf* levels are approximatively 55% higher than *Gt* levels.

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On the contrary, considering the overall low used single-services levels (Fig. 7), Figure 8e highlights many areas where used *Gf* levels are estimated as lower than *Gt* levels. Nevertheless, when averaged over the whole study area, *Gt* levels are only 7% superior to used *Gf* levels (median equal to 0).

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## 5.3 Geodiversity and geofunctionality hotspots

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Most of the highest threat levels are located particularly along the coastal areas, where most of the human settlements and activities are located (Fig. 9a), with the highest peak of threats located particularly between the main cities of Cayenne and Kourou (Fig.5b). Moderate levels are also shown along the riverine areas of Maripasoula, when going upstream the Maroni River.

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When the TI map is combined with *Gt* (Fig. 9b) and offered (Fig. 9c) and used *Gf* (Fig. 9d), the highest levels of sensitivity (i.e. hotspots) are highlighted particularly for *Gt*, mainly along the coastal areas. *Gf* hotspots are less contrasted but still present particularly in the highest-threat areas on the coastal areas and along the Maroni River (Fig. 9c). Used *Gf* hotspots seem to follow similar patterns but, since used *Gf* levels are lower, the contrast is less enhanced (Fig. 9d).

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376 6. Discussion

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378 6.1 The added-value of geofunctionality assessment and hotspot analysis: from land

379 planning to landscape planning

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381 The assessment of geodiversity results often in the measurement of the heterogeneity of
382 landscapes abiotic features and it is generally influenced by their spatial geometry and
383 distribution within a given area. Switching to a functional dimension is uncontestably critical to
384 concretely enhance planning strategies because it allows to understand the complex
385 relationships between geodiversity-related entities and socio-ecological functioning, needs and
386 uses. As shown in Figures 8d and 8e, geodiversity and geofunctionality do not always follow the
387 same spatial patterns and thus, they must be both accounted when it comes to planning tasks.

388 Geofunctionality relates geodiversity to human activities, which can range from conservation
389 to exploitation or artificialization. Indeed, “land” planning might be defined as the systematic
390 and voluntary assessment of alternatives for land use and a territorial repartition of resources
391 reflecting socio-economic conditions, policy visions (e.g. economic development, landscape
392 protection, equal access to education and culture) and knowledge, in order to adopt the best
393 land use options (Metternicht 2017; Desjardins 2021). Since land uses and human inputs imply
394 a socio-economic and functional dimension of space and time, they dissolve within a “territorial
395 metabolism” (Desjardins, 2021) that goes beyond preservation and conservation purposes
396 alone, including also processes that might alter, exploit, artificialize, transform or even destroy
397 natural resources.

398 Therefore, “landscape” – rather than “land use”– planning implies a holistic and metabolic
399 vision of ecosystem diversity, in both its biotic and abiotic dimensions, and it allows the
400 understanding of the relationships between biodiversity, geodiversity and socio-ecological
401 functioning and needs.

402 Geodiversity and biodiversity should therefore be highlighted as equal and linked concepts (Ren
403 et al. 2021). Because geodiversity finds its synthesis in the landscape (Alexandrowicz and
404 Kozlowski 1999; Serrano and Ruiz-Flaño 2007), its operationalization should encompass the
405 landscape seen as a multifunctional complex unit (Nin et al. 2016; Englund et al. 2017;
406 Metternicht 2017; Miklós et al. 2020). Despite it raises many debates (Schröter et al. 2014), the
407 ES concept appears to be an interesting approach to analyze and assess geofunctionality. This
408 landscape-oriented analysis undoubtedly involves spatialized approaches to identify the
distribution, across space, of landscape functional units. When it comes to such assessments, it is preferable to distinguish between GES or SI levels when averaged over the whole study area and their spatial distribution. Although considering the study area as a whole entity with averaged levels might be helpful to support strategies at the national or supranational scales, it would not allow to identify clusters or priority areas of intervention at the landscape functional unit scale.

For instance, despite averaged low levels, MM supply shows high offered levels mainly along the two greenstone belts, which host most of the gold deposits (Fig. 4a). Offered WS and WU are spread along the whole region (Fig. 4c and d) confirming the fact that, as its name suggests, *Guyana* is the “land of many waters” (Clifford 2011). The Quaternary formations of the coastal areas underlaying the Paleoproterozoic basement offer for instance overlapping aquifers, increasing known offered WU levels in such regions (Fig. 7d). These portions of the territory show also the most important potential in hosting natural flood-prone areas and wetlands (Fig. 4f), mainly because of their intertidal positions, the potential influence of sedimentary aquifers and the presence of mangrove ecosystems developed on the coastal sediments.

When geofunctionality is combined with human-related threats according to the approach proposed by Bétard and Peulvast, (2019), geofunctionality hotspots maps provide information about the spatial patterns of endangered areas, thus supporting the spatial allocation of lands, priorities of intervention, while integrating the socio-environmental impacts and conflicts with other potential land uses (Nin et al. 2016). Important information could be also added through statistical analysis or generalized additive models to analyze the relationships between land use intensity, geodiversity and geofunctionality as performed by Tukianen et al. (2017). Focusing only on geodiversity hotspots, would neglect potential areas of ordinary abiotic nature or lower geodiversity that are not necessarily less important in terms of ES supply (Bétard and Peulvast 2019).
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## 6.2 The dual role of human inputs in supplying services: towards ES management indicators?

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Land use, as a human footprint on the environment, is often used to proxy threats to ecological integrity. Nevertheless, the conceptualization of land use only as a “threat” would be limiting in terms of landscape analysis since it is one of the main drivers of landscape structures and patterns (Pătru-Stupariu et al. 2017), driven by governance objectives and societal needs (Galler et al. 2016). As mentioned and conceptualized by the revised cascade model, human inputs are considered as a part of the ES production chain.

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A service relates to a demand and it is indeed often combined with built, human or social capital in terms of inventory and/or management activities (Jones et al. 2016). This can be particularly observed in two complementary dimensions of GES supply. For instance, the supply of raw materials for mining (MM) and quarrying (MnM) implies, on one hand, the construction of exploitation infrastructures and human workforce that are able to provide the final service. On the other hand, the location of the supply related to such activities is often regulated by mandatory frameworks, such as the Quarrying Regional Plan (QRP) or the Departmental Mining Plan (DMP) in French Guiana, which state where extraction can or cannot take place according to different criteria (e.g. sensitive areas, minimum distance to populated areas). Also, MnM levels are often concentrated especially along the coastal strips (Fig. 4j), since the sandy, lateritic and hard-rock materials are more accessible and closer to human settlements where on-going construction projects are located (Fig. 4j). Surface and groundwater supplies are often located next to populated areas (Fig. 4k and l), since the used service would be non-existent otherwise. Access to geoheritage areas is provided by a network of roads except for the southern areas of French Guiana, where environmental protection measures limit some human interventions (Fig. 4o). Globally, the highest levels of used geofunctionality seem to follow human population distribution, suggesting that the concept of “used” service, depending on the type of service, might be tightly related to human activities requiring interventions other than conservation.

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Thus, human inputs might act as ES co-producers and as ES managers. In the first case, they will particularly influence the future levels of offered service supply, while in the second case they might control ES spatial patterns, in both cases, to satisfy a demand.
In French Guiana, ES management seems to lead a clear distinction between two areas. The first one is composed of the littoral – and, in some case, riverine – areas, the most inhabited ones where most of needs and ES demands are located but also where geodiversity and biodiversity levels seem higher, hosting dynamic and fragile landscapes (e.g. mangroves, wetlands). The second area embraces most of the inner regions of the territory where human density is very low and where habitat protection strategies dominate, sometimes in contrast with dispersed legal or illegal gold mining activities.

Protected areas show in some cases even higher “used” levels than “offered” ones (Fig. 4m and u). The overall higher values for this service and the spatial mismatches between its offered and used levels, might imply that land planners give a priority to biodiversity conservation objectives in inner French Guiana, compared to other land uses. In such protected areas, which are also considered by the DMP, land uses such as mining are therefore forbidden because conflictual with the objectives of local and national strategies. This might explain for instance the difference between offered and used MM levels in the gold deposits of the southern greenstone belt (Fig.6 q). It must not be forgotten indeed that the supply of multiple ES depends on their management and it can result in synergies and trade-offs between single services. For instance, management strategies targeting MM or MnM supply could lead to decreasing surface or groundwater supply, because of the widely known impacts of extraction activities on water quality (Castello and Macedo 2016) and quantity (Northey et al. 2019). However, such considerations should highlight the existence of informal and illegal activities, such as illegal gold mining, which participate to the production of “used” services – for instance in the southern greenstone belt – but increasing negative impacts and trade-offs with other services (e.g. water quality, natural habitat support). The inclusion of informal human inputs and the related fuzzy-data should be considered in such approaches.

For better implementations within landscape planning, indicators selection should then fit the cascade model and it might be necessary to clearly distinguish between offered services indicators, used services indicators and management indicators. As example, Rendon et al. (2022) propose a list of non-regulatory management indicators to analyze pressures on soil-related ES, mainly in terms of agricultural practices, although management indicators should cover all the dimensions of human inputs. Table 4 attempts to satisfy such distinction for further improvements of the current study proposing a list of management indicators and their objectives for the services considered in this study. Management indicators could vary in terms
of management “intensity”, which can range from preliminary screening, inventory and baseline data acquisition, to advanced tasks of land allocation, zoning and planning. Such indicators, sometimes unlike the ES they are related to, tend to be complementary rather than discordant. For instance, management tools related to mining and water planning are often compatible and harmonized. In French Guiana, the DMP and the SDAGE (Table 4) are explicitly supportive and interrelated between each other. Management indicators, ideally, should be the result of adequate strategies where planners accounted for ES synergies and trade-offs so to find the optimal balance between economic development and ecological integrity.

Thus, a true implementation of the ES framework would require in practice “formal changes of existing planning instruments” (Albert et al. 2016) and it would be therefore pivotal to address in the future all the dimensions of human inputs in ES production chain to support prospective studies for ES assessment, monitoring and landscape planning.

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6.3 Unavailable data are a matter of landscape planning

Human inputs include the inventory of data that can be provided by all the stakeholders in a territory (Jones et al. 2016), through various methods and tools, to supply the baseline of knowledge used to quantify the capacity of an ecosystem to provide a service. Therefore, the quality of such data and their scales of acquisition drive ES assessment and mapping tasks and have a critical impact on the final results. Most of the services do not display the same spatial coverage and are limited only to few portions of the study area. If this is related, on one hand, to the bio-geo-physical heterogeneity of the landscape – meaning clearly that not all the ES are or can be supplied by the same spatial units and might have different patterns – on the other hand, it gives clues about data availability, accessibility and data acquisition methods (Le Tourneau and Noucher 2023).

For instance, the assessment of quarrying and mining materials supply does not account for data on ancient quarries which sometimes were located in the newly populated areas nor on illegal gold mining production rates and risks. Raw materials offered supply is here based on geological surveys and prospections carried by the French National Geological Survey over the last decades (Magnien et al. 1990; Billa et al. 2013) that specifically targeted the gold-hosting regions of the greenstone belts (Fig. 6a). Potential wetlands were identified at the scale of the whole region (Guitet and Brunaux 2017). Biodiversity-related data (e.g. Fig. 4e) are mapped at the regional scale also because most of the surveys over the years focused on biotic resource inventory (Gautreau 2020). Spatial patterns of geoheritage points can be explained by their identification through both remote-sensed regional data and local field surveys.

The areas with highest ES levels are located on the coastal and riverine areas of French Guiana, because they are the most explored, accessible and inhabited and data are needed for most of the past and current practical planning challenges. These areas are also the most threatened (Fig. 9a), since human occupation is mainly located here and, based on our assessment, that automatically leads to "very high" sensitivity levels (Fig. 9b, c, d).

This means also that applying land use-based metrics for threats identification – such as the Hemeroby index – could translate spatial bias and overlaps in the identification of geofunctionality hotspots since: i) land use is one of the drivers of used ES supply; ii) land use can proxy the accessibility and availability of data, which might be higher in anthropic areas.
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The relationship between the spatial distribution of data availability and inhabited areas might lead to underestimate the levels of offered services in more remote areas. Such underestimations should be considered as a loss of opportunity to develop potential services which are still not known, and consequently unused, or, on the contrary, as the best way to preserve them (i.e. since they are not known they might be also not degraded by human interventions).

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Unavailability of geoscientific data must be identified and assessed and such gaps represent an undeniable challenge to address for landscape-planning. Through indirect or direct measures, the landscape and its structures should be better acknowledged to identify and apply adequate indicators for ES assessment and management. In lack of adequate indicators, the assessment process risks to be performed with coarse available data since it is the only option, rather than the best one. This is particularly true for regulating services, which provide direct impacts that can be difficult to express through pertinent indicators (Villamagna et al. 2021b), unlike provisioning services which are usually more easily available. Therefore, the multi-service combination of ES of different natures could lead to bias, since it combines services assessed based on data that have different levels of availability.

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A Regional Commission for Geoheritage of French Guiana has been only recently established and geoinventories are still ongoing. Geodiversity features of French Guiana, such as the unique komatiitic-related *Dachine* diamonds (Smith et al. 2016), a great variety of inselbergs spread across the region (Aertgeerts 2020), such as the *Mamilihpann* inselberg and its still unknown cave paintings (Fuentes 2022) or else the remarkable *Grand Connétable* island could be integrated in the assessment of cultural GES. Wetland identification field surveys are still unaccomplished because of the lack of harmonized soil and vegetation data (Blum 2013). Also, water-related services could be proxied by the permeability of lithological formations (Perotti et al., 2019) for instance through hydraulic conductivity or rock porosity (Freeze and Cherry, 1979).

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The identification of management goals and land planning exercises require an important level of detail, especially at regional and local scales (Gomez-Zotano et al. 2018). As highlighted by Heink et al. (2016), indicator choice “should capture the meaning of the construct that is to be measured” and “the variance between the indicator and the *indicandum* should be low”, meaning that the conceptual model used should be as clear as possible and that indicator selection should stick to it.

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## 6.4 Abiotic services or abiotic indicators?

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In a theoretical way, the ES concept already includes abiotic and interfacial components in its definition. However, the current position of geodiversity within the ES framework still remains confused (Fox et al. 2020). This declination resulted sometimes in varying classification systems and terminologies (e.g. “subsurface services”: Van Ree and Van Beukening 2016; “abiotic ES”: Fox et al. 2020; “geosystem services”: Gray 2011). For instance, some authors suggest that geosystem services are all the services associated with geodiversity and that are “independent of interactions with biotic nature” (Fox et al. 2020). Nevertheless, if we consider the landscape as a unified, holistic and dynamic whole, most of the services are *per se* the result of both biotic and abiotic components of natural diversity. One might argue that in any case, attention should be given to identifying a given biotic or abiotic factor that plays a dominant role in the supply of a specific service. Nevertheless, this dominance should be rather expressed in how that specific service is assessed, and thus, in the choice of an adequate predictive – and dominant – variable to assess it. For instance, although the “offered” services selected in this study are classified as biotic (e.g. HAB, FC) and abiotic (MM, MnM, WS, WU, GC) (Table 1), their assessment was performed only according to abiotic variables. Thus, it is pivotal to distinguish between the services themselves (biotic and abiotic) and the underpinning variable(s) that can be selected as assessment indicators. In other words, a unified definition of geodiversity-based ES might be needed.

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# Conclusion

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To fully support land use planning and conservation objectives, it is fundamental to account for the contribution of geodiversity to socio-ecological functioning (i.e. geofunctionality). This study proposes an approach to assess geofunctionality in terms of geodiversity-based ecosystem services (GES), through a conceptual model – based on the revised ES cascade model which distinguishes between offered and used services – and a methodological framework that aims at identifying and comparing geodiversity and geofunctionality hotspots.
608 The application of these frameworks in French Guiana, an Overseas French territory presenting
609 planning and conservation challenges, highlights the feasibility of such approaches and the
610 heterogeneity of spatial patterns between geodiversity and geofunctionality which thus must
611 be both included within landscape planning.

612 When assessing typological and functional variability, it is pivotal to distinguish between levels
613 and spatial patterns. The choice between the types of results to consider strictly depends on
614 the objectives of the assessment. Spatialized approaches seem more adequate for planning,
615 seen as the process of allocating lands and integrating impacts and conflicts with other
616 potential land uses. Nevertheless, when assessing geofunctionality hotspots – since used
617 services rely here on human inputs – threats and used geofunctionality levels might overlap
618 and lead to spatial bias. According to this study, French Guiana can be divided in two main
619 areas: the littoral areas, more populated, with higher levels of geodiversity and used
620 geofunctionnality but also higher threats, and the less inhabited and less known inner regions,
621 a widest area with important assessed levels of offered geofunctionality.

622 Despite the revised ES cascade model includes human interventions in the supply of ES, further
623 improvements should focus on a clear-cut distinction of human inputs as ES co-producers,
624 managers but also demanders.

625 Indicator selection is a crucial step in the assessment process and it should satisfy multiple
626 criteria and fit precisely the conceptual model used. However, data unavailability is the main
627 issue in the achievement of such requirements and it must therefore be considered as a matter
628 of land planning that should be quantified. This is particularly true in French Guiana, where
629 geoscientific data production must be enhanced.

630 Landscape planning – rather than “land use planning” – implies a holistic and metabolic vision
631 of ecosystem diversity, in both its biotic and abiotic dimensions, and it allows the understanding
632 of the relationships between biodiversity, geodiversity and socio-ecological functioning and
633 needs. The ES concept, despite its anthropocentric nature, can be a useful tool to identify and
634 analyze human-nature relationships. However, many improvements must be achieved to clarify
635 the ES concept itself, the place of abiotic and interfacial components of natural diversity within
636 it, and how this concept can fully relate to human activities, uses, needs and priorities of action
637 to drive and support the implementation of policies in increasingly disturbed environments.

638
639
640 Acknowledgments

641 This study was carried within the post-doctoral grant of the CoSav « Géoressources et durabilité » proposed by the French National Research Institute for Sustainable Development (Institut de Recherche pour le Développement, IRD). A particular thank is addressed to Dr. Robin Cura (UMR Prodig – Université de Paris 1, Panthéon-Sorbonne) for the review of the maps used in this study. The readers can freely access the data from this paper by contacting the first author.

647

648 Compliance with Ethical Standards and ethical conduct

649 The authors declare that the presented research has been carried in compliance with the Ethical Standards proposed by the journal Geoheritage and Springer and they declare to comply with the ethical responsibilities of authors.

652

653 Conflict of Interest

654 The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

656

657 Funding

658 This study was possible thanks to the post-doctoral grant of the CoSav « Géoressources et durabilité » proposed by the French National Research Institute for Sustainable Development (Institut de Recherche pour le Développement, IRD) and the funding of the UMR Prodig.

661
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