# Model-based management of macrophytes in shallow lakes under warming

Rainer Ferdinand Wunderlich, Aurélien Jamoneau, Sébastien Boutry, Maha

Hosni, Vincent Bertrin

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Rainer Ferdinand Wunderlich, Aurélien Jamoneau, Sébastien Boutry, Maha Hosni, Vincent Bertrin. Model-based management of macrophytes in shallow lakes under warming. Journal of Environmental Management, 2025, 378, pp.124543. 10.1016/j.jenvman.2025.124543. hal-04973857

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Research article

# Model-based management of macrophytes in shallow lakes under warming

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Rainer Ferdinand Wunderlich<sup>a, ID</sup>, Aurélien Jamoneau<sup>a,b</sup>, Sébastien Boutry<sup>a,b</sup>, Maha Hosni<sup>a,c ID</sup>, Vincent Bertrin<sup>a,b,* ID</sup>

## ARTICLE INFO

### Keywords:

- Climate change

- Conservation

- France

- Isoetid

- Joint species distribution model

- Traits

## ABSTRACT

Macrophytes are a critical component of freshwater ecosystems, harboring significant biodiversity and providing essential resources and services. However, their habitat faces multifaceted challenges from climate change, local anthropogenic disturbance, and biological invasions. Here, we aim to provide local management suggestions under both current and future higher temperature regimes. Using joint species distribution modeling (JSDM), we integrate comprehensive presence-absence data with environmental variables and ecological traits to predict the distributions and diversity of 44 vascular aquatic plant and charophyte species in three shallow lakes (435 sites in total) in southwestern France. The environmental variables considered included physical properties (including current surface temperature and a 2 °C warming scenario), anthropogenic disturbance, shoreline curvature, underwater topography, and the occurrence rate (temporary or permanent) of water and wetness. Subsequently, we use percentile-threshold-based spatial prioritization to identify conservation management hotspots. Our results show that macrophyte habitat suitability is largely influenced by land-use and human accessibility. Moreover, macrophyte habitat suitability and native species diversity generally decrease across lakes under warming. However, the decrease in habitat suitability is greater for native isoetid species than for invasive species—suggesting a potential forthcoming cascade of changing community composition, higher lacustrine trophic states, and impaired provisioning of ecosystem services. Therefore, we suggest immediately adopting adaptive management principles at the identified conservation management hotspots, including the control of and targeted monitoring for invasives as well as conservation and restoration measures for native species, in particular isoetids.

## 1. Introduction

Macrophytes (in this article, both vascular aquatic plants and charophytes) play a key role in the sound functioning of aquatic ecosystems (such as lakes) by promoting habitat complexity, providing refuge or feeding grounds, contributing to nutrient cycling, stabilizing sediments, and contributing to water purification and human well-being (James and Barko, 1994; Barko and James, 1998; Millennium Ecosystem Assessment, 2005; Lawniczak et al., 2010; Bl window et al., 2014; Lv et al., 2016; Lu et al., 2018; Nohner et al., 2018; Murphy et al., 2019; Su et al., 2019; Sterner et al., 2020; Meerhoff and de los Ángeles González-Sagrario, 2021; Lobato-de Magalhaes et al., 2023; Thomaz, 2023). On one hand, their near-global distribution (Lobato-de Magalhães et al., 2023; Murphy et al., 2019) should make them less sensitive to both local and global change, on the other hand, macrophytes are

stationary in nature and most lack the traits required for long distance dispersal; and many species are limited to a single ecozone (Lobato-de Magalhaes et al., 2023; Murphy et al., 2019). In addition to dispersal constraints, aquatic plants are often subject to intense environmental filtering, e.g., by the availability of light and thus depth (Middelboe and Markager, 1997). Therefore, native macrophytes could be considered as highly susceptible to multifaceted threats, such as climate change, increased water level fluctuations, land-use change, recreational use, and biological invasions (Akasaka et al., 2010; Dudgeon et al., 2006; Han et al., 2024; Kim and Nishihiro, 2020; Mantyka-Pringle et al., 2014; Millennium Ecosystem Assessment, 2005; Råman Vinnå et al., 2021; Reid et al., 2019). Among those, species with conservation importance such as isoetids are threatened under increasingly warm and high-CO₂ conditions by both competition with faster-growing macrophytes (Spierenburg et al., 2010) and trampling (Liddle and Scorgie, 1980).

<footnote><sup>a</sup> UR EABX, INRAE, 50 Avenue de Verdun, 33612, Cestas-Gazinet, France</footnote>

<footnote><sup>b</sup> Pole R&D ECLA Ecosystèmes Lacustres, France</footnote>

<footnote><sup>c</sup> University of Toulouse III - Paul Sabatier, Faculty of Science and Engineering, 118 Route de Narbonne, 31062 Toulouse, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: vincent.bertrin@inrae.fr (V. Bertrin).</footnote>
since they are slow-growing and well-adapted to low nutrient and CO2 conditions in shallow water (Borowiak et al., 2017; Smolders et al., 2002). And any loss of isoetids could impair the maintenance of the oligotrophic conditions (Ribaudo et al., 2017; Smolders et al., 2002) and thus promote shifts toward more eutrophic states. In contrast, invasive species can often profit from such changes; for example, *Ludwigia gran-diflora* can adapt to prolonged terrestrial conditions by metabolic adjustment (Billet et al., 2018) and shifting depth distributions of the root system (Hussner, 2010). Furthermore, likely continuing increases in both temperature and CO2 alongside heightened availability of nutrients (e.g., due to agricultural run-off) might generally favor emergent and floating macrophyte species while adversely affecting submergent macrophyte species (Lind et al., 2022).

Without conservation and restoration efforts, climate change and both regional (land-use change) and local anthropogenic impacts (trampling by humans or livestock) could result in a loss of macrophyte habitat and diversity, changing nutrient uptake/release dynamics, and abrupt ecosystem changes that also affect the provisioning of ecosystem goods and services (Han et al., 2024; Harrow-Lyle and Kirkwood, 2020; Jeppesen et al., 2015; Lawniczak et al., 2010; Lu et al., 2018; Man-tyka-Pringle et al., 2014; Rowe et al., 2015; Woolway et al., 2022). Therefore, it would be paramount to conserve or restore healthy levels of native macrophytes and to anticipate biological invasions potentially impairing healthy ecosystem functioning.

A key requirement for effective conservation or restoration is knowing where the suitable habitat for individual species is or will be located (Gastón and García-Viñas, 2013; Lin et al., 2014; Margules and Pressey, 2000; Pearce and Ferrier, 2001; Pressey et al., 2007; Rondinini et al., 2005; Zellmer et al., 2019; Zhong et al., 2021). Future species distributions, however are—obviously—unknown, and sufficiently robust knowledge about them can only be obtained from well-calibrated models. Nowadays, a broad range of specialized tools is available under the umbrella term species distribution modeling (SDM), also often coined ‘ecological niche modeling’ (Thuiller, 2024), for the purpose of mapping the potential distribution of species, including both mecha-nistic (Evans et al., 2015; Kearney and Porter, 2009, 2017; Kearney et al., 2018) and statistical approaches (Elith et al., 2008; Phillips et al., 2017). More recently, joint species distribution modeling (JSDM) allows to expand statistical approaches beyond species-environment relation-ships, by incorporating species interactions and ecological traits (Clark et al., 2014; Clément and Vieilledent, 2023; Hui, 2016; Pichler and Hartig, 2021; Tikhonov et al., 2020; Warton et al., 2015; Wilkinson et al., 2019).

The quality of modeling predictions is subject to a range of factors, including sample size (Hernandez et al., 2006; Mitchell et al., 2017; Stockwell and Peterson, 2002) and precision (Engler et al., 2004; Feeley and Silman, 2010), as well as the scale of modeling (Ferrier and Watson, 1997; Guisan et al., 2007; Johnson et al., 2002; Wunderlich et al., 2022), and the choice of the environmental variables (Austin, 2002). Temper-ature, for example, directly interacts with the physiological functioning of organisms (Atkin and Tjoelker, 2003; Carriel et al., 2023); therefore, it is recognized as one of the most critical environmental variables in SDM for both terrestrial and aquatic realms (Bradie and Leung, 2017). Similarly, wind exposure and its associated effects on waves and sedi-ment resuspension are considered as potentially important determinants of macrophyte communities (Bertrin et al., 2017; Keddy, 1982; Ribaudo et al., 2021). Finally, even less obviously or indirectly related variables can be informative. Landscape metrics (LSMs), for example, have a long history of being used in ecology and biodiversity research (Uuemaa et al., 2009), to assess habitat suitability (Borges et al., 2017; Riitters et al., 1997), human modification of the landscape (Herzog et al., 2001; Jamoneau et al., 2021), or anthropogenic habitat fragmentation (Zhuo et al., 2022), and land-use change has been linked to deterioration of lake resilience (Han et al., 2024).

In this study, we aimed to model the distribution and diversity of forty-four macrophyte species in three shallow lakes in southwestern

France and predict their response to a hypothetical warming of 2 °C. Based on these predictions, we also aimed to identify and map hotspots that are critical for the conservation of protected native isoetid species, and native species diversity, under both current and increased temper-ature regimes, as well as for management and monitoring of invasive species' distributions. To achieve our study aims, we combined precise macrophyte presence-absence and trait data with a suite of high-resolution environmental variables (data available online: https://github.com/RWunderlich/Macrophytes-in-Aquitanian-shallow-lakes) to fit joint species distribution models and applied percentile-threshold prioritization to inform local scale macrophyte conservation and invasive species management under both a regional and a lake-by-lake perspective.

## 2. Methodology

### 2.1. Study area

In southwestern France, more than a dozen relatively shallow lakes have been formed along the seashore by coastal processes between the Gironde estuary in the north and the Pyrenees in the south; specifically, aeolian sand accumulation gradually began blocking the flow of coastal rivers approximately 3500–2000 years ago (Tastet et al., 2008). Our study area was embedded in a forest-dominated landscape mosaic (majorly pine plantations). Other common land-uses included other vegetation types and agriculture, while barren/built-up (both natural and artificial unvegetated surface) and wetlands covered a relatively small proportion. We studied in detail three of the largest (by surface area) coastal lakes in the wider region, (North to South) Lac de Carcans-Hourtin (HOU; ~62 km²), Étanç de Lacanau (LAC; ~20 km²), and Étanç de Cazaux et de Sanguinet (CAZ; ~58 km²) and their im-mediate terrestrial surroundings (Fig. 1). Other than featuring relatively small reported mean depths (HOU: ~3.4 m, LAC: ~2.6 m, and CAZ: ~8.6 m), these lakes share predominantly sandy substrate, near-neutral pH levels, as well as a tendency for steeper and gentler slopes in the west and east, respectively (Bertrin et al., 2018; Cellamare et al., 2012).

### 2.2. Macrophyte data

#### 2.2.1. Species occurrence, processing, and filtering

Occurrence data were collected along sectors (100 m long shoreline sections around the entire lakes, sampled visually by experts) in the years 2011, 2016, 2022, and 2023 (varying across lakes). We associated with each site (see section 2.4) the sector with the largest overlap with the respective site. In case of repeated site visits, presence was chosen over absence for representation. To limit the number of species (and thus model complexity), we combined (based on both close taxonomic relatedness and ecological similarity) some pairs or groups of species (e. g., *Chara* spp.) into species clusters (Table S1, Supplementary Informa-tion), that were modeled as a single species. In addition, we limited our analysis to those species and species clusters (N = 44) with a data prevalence between 5% and 95% (22 ≤ presences ≤413; across 435 sites). The data prevalence ranged from 5.06% (*Egeria densa*) to 74.02% (*Phragmites australis*), with 25th, 50th, and 75th percentiles of 8.62%, 19.43%, and 34.54%.

#### 2.2.2. Ecological traits

In total we considered four ecological traits, the growth form (elo-deid, isoetid, forbid, graminoid, herbid, myriophyllid, nympaeid, or peplid), aquaticity (1, 2, 3, 4, or 5; decreasing association with water), pollination vector (insect, water, or wind), and taxonomy (family and genus). Taxonomy, however, was dropped after an association screening between traits yielded high values (>0.8) for all pairs involving either of the two taxonomy levels according to Cramer's V (Cramér, 1999). Moreover, growth form and pollination were transformed into series of binary (0/1) dummy trait variables representing the individual factor
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Maps of the study area; coastlines and graticules are shown as thick continuous and thin dashed lines, respectively. All maps are shown in an equal-area projection (EPSG:3035). (a) The study area is situated in Nouvelle-Aquitaine, France (dark with red border, inset map), and includes three large lakes (HOU: Lac de Carcans-Hourtin, LAC: Étang de Lacanau, CAZ: Étang de Cazaux et de Sanguinet; blue) along the Atlantic coast; the department border (Gironde—Landes) is shown as a dotted line. HOU and LAC (b), and CAZ and PAR (c) are linked and connected to multiple streams and canals (blue lines); majority landcover at 250 m scale (forest: dark green, agriculture: gold, wetland: turquoise, other vegetation: light green, and barren/built-up: gray; derived from Malinowski et al., 2020). PAR (a&c) refers to Lac de Biscarrosse et de Parentis.</figcaption></figure>

levels. The initial trait data were obtained using the GIFT R package (Denelle et al., 2023). The trait data were then reviewed and substantially edited (alterations and filling in gaps) according to expert knowledge (Table S2, Supplementary Information).

## 2.3. Environmental data

We considered 28 environmental variables describing physicochemical properties, anthropogenic impacts, shoreline curvature, depth and underwater topography, and the water and wetness seasonality. A list of the retained variables is presented in Table 1.

### 2.3.1. Physical properties

**2.3.1.1. Temperature.** We acquired lake surface temperature for the four lakes ranging from April 1984 to December 2019 (Sharaf et al., 2023) and computed monthly means (I—XII) over the entire period. Based on those monthly means, we computed the annual mean temperature (TEMP). Finally, we prepared a warming scenario representing

a 2 °C increase in surface temperature, which roughly corresponds to the expected warming over ~50 years assuming current warming rate of ~0.38 °C decade⁻¹ in European lakes (Copernicus Climate Change Service, 2021) or over 100 years assuming the reported warming rate for Lac de Biscarrosse et de Parentis of ~0.2 °C since the late 1950s (Jamoneau et al., 2019). Relative to the temporal midpoint in our temperature data (approximately the year 2000), this is roughly equivalent to the year 2052 or 2100 depending on the warming rate.

**2.3.1.2. Wind exposure.** Similar to Keddy (1982), we computed monthly (I—XII) wind exposure (WEX) as the product of wind speeds and fetch, i.e., the unobstructed distance over water, based on monthly mean wind data (angle and speed). Monthly means of wind speed and angle were computed from a raster stack of mean monthly horizontal air movement speeds (u and v directions) obtained from the Copernicus Climate Change Data Store (Hersbach et al., 2023). Owing to variation of wind across the study area, however, horizontal air movement speeds were first recomputed as weighted means at each lake centroid based on the inverse great circle distances to the centroids of the wind raster stack
<caption>**Table 1**</caption>

A list of the retained environmental variables, including shorthand, full name (and explanation), and the contributions to variance explained by environmental variables in final models M1 and M2. Rows are sorted by the average variable contribution in final model M1.

<table><thead><tr><th rowspan="2">Shorthand</th><th rowspan="2">Full name and/or explanation</th><th colspan="2">Contribution to variance explained by environmental variables (%)</th></tr><tr><th>M1</th><th>M2</th></tr></thead><tbody><tr><td>LCO_PCT-Barren</td><td>Percentage of outside-of-lake area covered by unvegetated or barren/built-up cells</td><td>30.03</td><td>30.90</td></tr><tr><td>VRM</td><td>Vector ruggedness measure; a topographic metric accounting for variation of slope orientations in a neighborhood</td><td>14.87</td><td>16.65</td></tr><tr><td>MEE</td><td>Minimum energy expenditure required to move from an access hub to any location</td><td>10.95</td><td>12.08</td></tr><tr><td>LCO_PCT_Water</td><td>Percentage of outside-of-lake area covered by water</td><td>8.14</td><td>7.61</td></tr><tr><td>WEX</td><td>Accumulated wind exposure (wind speed × fetch) over the seasons in an average year</td><td>6.03</td><td>5.24</td></tr><tr><td>LCO_PCT_Cultivated</td><td>Percentage of outside-of-lake area covered by agriculture</td><td>5.05</td><td>4.61</td></tr><tr><td>LCO_PCT_Wetland</td><td>Percentage of outside-of-lake area covered by wetlands</td><td>4.79</td><td>3.24</td></tr><tr><td>BCIvar</td><td>Variance of the boundary convexity index</td><td>4.15</td><td>3.93</td></tr><tr><td>TPI</td><td>Topographic position index</td><td>3.77</td><td>5.58</td></tr><tr><td>TEMP</td><td>Annual mean temperature of the lake surface</td><td>3.56</td><td>2.41</td></tr><tr><td>HLI</td><td>Heat load index; a topographic metric estimating the solar warming effect</td><td>2.56</td><td>2.13</td></tr><tr><td>BCI</td><td>Boundary convexity index; the degree and orientation (inwards/outwards) of shoreline curvature</td><td>2.49</td><td>2.36</td></tr><tr><td>WAW_PCT_TEMP</td><td>Percentage of outside-of-lake area covered by non-permanent water/moisture</td><td>2.48</td><td>2.11</td></tr><tr><td>BCI:BCIvar</td><td>Interaction of the boundary convexity index with its variance</td><td>1.14</td><td>1.14</td></tr></tbody></table>

cells; this ensured that the underlying angles and speeds used to compute WEX were more representative for lakes situated in the vicinity of or on a cell boundary of the (relatively course scale) wind data. Each monthly fetch was then individually calculated as the median across all fetch values within a site using a custom R function. This R function encompassed several processing steps while iterating through the sites: 1) the shoreline is clipped based on the current circular site; 2) for each point comprising this line segment (typically >100), a long (25 km) line vector is generated from that point in the direction indicated by the wind angle and cut by the shoreline (including islands) before identifying and measuring the correct segment of that newly created line vector; 3) once all fetch values within a site have been measured, their median was calculated and assigned to the site. The resulting fetch data was then multiplied with the respective mean wind speeds to generate monthly mean WEX at all sites before aggregating them (using their sum) into annual WEX.

## 2.3.2. Anthropogenic impacts

### 2.3.2.1. Proportional land-use.
Proportional land-use (10 m resolution) was derived from Sentinel-2 imagery-based land-use data (Malinowski et al., 2020). The computation was performed in a quadratic moving window (s = 110 m) after 1) masking cells within the shorelines to keep the focus on terrestrial regions and 2) reclassifying land-use to forest (all types of tree-dominated vegetation, including pine plantations), agriculture (including vineyards), wetland, other vegetation (all types of vegetation not accounted for), barren/built-up, and water.

### 2.3.2.2. Landscape metrics.
We computed six landscape metrics (LSMs) from the same reclassified land-use data used for proportional land-use. The first three LSMs comprised edge density (LSM ED), patch density (LSM PD), and the landscape shape index (LSM LSI). LSM ED is an area and edge metric that relates the total length of edge to the area, scaled to provide values ≥ 0 in meters per hectare; LSM PD is an aggregation metric that relates the number of patches per 100 ha (scaled to return values > 0 and ≤ 1,000,000); LSM LSI (Patton, 1975) is another aggregation metric that relates the actual total length of edge to the hypothetical minimum edge length assuming only a single patch (McGarigal et al., 2023). The remaining three LSMs, marginal entropy or Shannon entropy (LSM ENT), conditional entropy (LSM CENT), and joint entropy (LSM JENT) all measure landscape complexity (McGarigal et al., 2023). LSM ENT measures the thematic diversity, while LSM CENT quantifies configurational complexity; finally, LSM JENT is a more encompassing complexity metric of landscape patterns (Nowosad and Stepinski, 2019).

### 2.3.2.3. Accessibility.
Organisms, including humans, limit their energy expenditure in pursuit of resources or, generally, when moving across the landscape (Hubel et al., 2016; Llobera and Sluckin, 2007; Shepard et al., 2013; Somveille et al., 2018; Weimerskirch et al., 2001). In turn, such energy-driven behavioral preferences of movement would also translate into energy-driven spatial distributions of some anthropogenic impacts (e.g., trampling of macrophytes by humans in shallow water). Therefore, we computed the energy expenditure required to reach by foot any location around and within the studied lakes using a least-cost-path (LCP) framework with the walking algorithm of Llobera and Sluckin (2007), effectively an inverse proxy of accessibility. Energy expenditure within the shoreline was assumed to be increased four-fold—primarily, to avoid over-estimation of accessibility due to highly unrealistic ‘short-cuts’ through water. The LCP analysis was based on the BD ALTI® elevation data (Institut national de l'information géographique et forestière, 2021) and BD TOPO® vector data of access points (e.g., parking lots, bus and train stops, taxi stands, etc.) obtained from the Institut national de l'information géographique et forestière (2023) and supplemented with data from OpenStreetMap contributors (2023). The analysis was performed on 50 m resolution data—after conversion of all non-point data (e.g., polygons of individual car parks) to points (centroids) and a subsequent cluster analysis (search radius 100 m) to reduce the number of access hubs and, thus, the memory use of least-cost-path analysis. The resulting raster map quantifies the minimum energy expenditure (MEE) in kilojoules to reach a given point from the nearest access point (or the centroid of a cluster of access points).

### 2.3.3. Shoreline curvature, depth, underwater topography, and water and seasonality

#### 2.3.3.1. Boundary convexity index.
The degree of shoreline sinuosity or curvature can influence processes, such as waves and sediment transport, including the accumulation of organic matter, and habitat suitability and/or diversity of aquatic species such as fish and macrophytes (Beck et al., 2010; Sabo and Kelso, 1991; Van Geest et al., 2003). To distinguish between maximally exposed and sheltered conditions, Albeke et al. (2010) introduced the boundary convexity index (BCI) as $BCI = \frac{BC}{BC_{max}}$, where the unscaled convexity (BC) is divided by half the window size ($BC_{max}$). BCI is close to zero for straight shorelines, and trends toward minus one or one in exposed and sheltered sections, respectively. Its calculation was performed in a moving window (100 m of scale, 1 m steps). The results were rasterized at the same scale as the other environmental variables for streamlined extraction of values later on. This variable was represented at site scale as both the mean (BCI) and the variance (BCI$_{var}$) to account for cases of low BCI but high variation within a site.
**2.3.3.2. Water and wetness.** Moreover, we also considered water level fluctuations by including information from the water and wetness (WAW) raster layer (European Environment Agency, 2020). These data categorize 10 m cells as dry, permanent water, temporary water, permanent wetness, and temporary wetness. From those data, we computed the proportions of both permanent and temporary water/wetness (WAW_PCT_PERM & WAW_PCT_TEMP) within the sites.

**2.3.3.3. Depth and underwater topography.** Depth or the height (H) of the water column is a potential key variable when modeling aquatic species (Bradie and Leung, 2017), in both marine and freshwater ecosystems (Charbonnel et al., 2023; Courrat et al., 2009; Hu et al., 2020; Yi et al., 2018). We received upon request point H data from the Adour-Garonne Water Agency (2017) for all lakes at resolutions of ~17 m (HOU & LAC) and ~10 m (CAZ). To obtain rasters, we followed two different approaches owing to the differential point spacing of the available H data among lakes. For HOU and LAC, we used anisotropic (major and minor directions: 0° and 90°) ordinary kriging (Gräler et al., 2016; Pebesma, 2004). For CAZ, the H data were available in a resolution equal to our target resolution, we iteratively applied a moving window computing the mean across a quadratic (s = 30 m) moving window (na.policy parameter equal to ‘only’ to limit recomputation to NA cells). Subsequently, the H rasters were combined into a single raster and clipped using the shoreline polygon data as the mask. Finally, the H raster was further processed in a two-step procedure based on the WAW raster. First, we set to 5 cm all the cells that satisfied two conditions, H < 5 cm and WAW indicating either permanent or temporary water) and, secondly, we set all remaining cells with H < 0 cm to 0 cm.

Similar to H, its spatial variation, i.e., underwater topography, is also known to be relevant for modeling aquatic species (Bertrin et al., 2017; Bradie and Leung, 2017; Hu et al., 2020). To compute underwater topography, we first multiplied H with -1, to ensure consistent interpretation of what is on top or at bottom. We computed six topographic variables: the slope in degrees (SLP), the topographic position index (TPI), the terrain ruggedness index (TRI), Riley's terrain ruggedness index (TRIriley), the square root mean squared differences terrain ruggedness index (TRIrmsd), and the roughness (ROUGH) following their common definitions (Riley et al., 1999; Wilson et al., 2007). In addition, we calculated the vector ruggedness measure (VRM) according to Sappington et al. (2007) and the heat load index (HLI) according to McCune (2007).

## 2.4. Data processing and coordinate reference system

We predominantly performed this study in R version 4.3.2 (R Core Team, 2023) and Rstudio (RStudio Team, 2020). The JSDM model was calibrated using the boral R package (Hui, 2016), which is based on a Bayesian framework and Gibbs sampling (Plummer, 2003; Warton et al., 2015). Raster and vector data were processed using the terra and sf R packages, respectively (Hijmans, 2023; Pebesma, 2018). We also used the spatialEco, rLFT, dbscan, ecmwf, leascostpath, usdm, gstat, and tidyverse R packages (Evans and Murphy, 2023; Fay et al., 2021; Hahsler et al., 2019; Hufkens et al., 2019; Lewis, 2023; Naimi et al., 2014; Pebesma, 2004; Wickham et al., 2019), and—mostly for visual validation—QGIS version 3.34 (QGIS Development Team, 2023). All data were extracted at site scale, i.e., as a summary statistic (the mean unless specified otherwise) computed within the sites. The sites represented non-overlapping and non-touching circles ($d = 200 m$; $area \approx 3.14 ha$) whose centroids were equally spaced along the lakes' shorelines. The primary objective for this approach was to limit the spatial autocorrelation. Our approach is conceptually related to spatial thinning (Aiello-Lammens et al., 2015) and similar to the gridSample function in the dismo R package (Hijmans et al., 2017), which both reduce spatial correlation among samples; however, our approach is performed on a non-regular 'grid'.

We used a broad range of data from various sources and differing in their coordinate reference system (CRS). Therefore, we created a template raster to be used as the target when resampling to a common CRS (EPSG:3035), extent (x-min: 3,410,000, x-max: 3,470,000, y-min: 2,400,000, and y-max: 2,530,000), and resolution (10 m). All vector data were also re-projected to the same CRS (EPSG:3035), where required after densifying (to ensure a more consistent representation of geometries). Using an equal area CRS for SDM applications was recommended by Elith et al. (2011) to ensure that equal weights of cells correspond to equal true area of cells during model calibration.

## 2.5. Variable selection

We used a multi-stage variable selection procedure based on correlation analysis, variance inflation factor (VIF; Marquaridt, 1970), and model diagnostics (Geweke, 1992), to limit multicollinearity and ensure successful calibration. In the first stage, we computed the Pearson correlation coefficient between variables across sites and eliminated from all pairs with a correlation coefficient >0.75 the variable with the higher VIF. In the second stage, we iteratively computed the VIF and kept removing the variable with the highest VIF (Borcard et al., 2011) until the highest observed VIF was <4 to make the model more parsimonious without losing too much information. While the specific choice of which variables to remove from sets of correlated variables can greatly impact predictions involving extrapolation under changing correlation patterns (Vorstenbosch et al., 2024), this was not a concern in our study since TEMP represented the only variable differing between current and future conditions.

## 2.6. Modeling and evaluation

JSDM was performed using the boral R package. The boral package is based on a Markov Chain Monte Carlo (MCMC) approach that produces results sampled from a probability distribution over a large number of iterations. We always included species responses (presence-absence data), environmental variables, ecological traits, and two or three latent variables (their number being determined as described further below). The exact meaning of latent variables in a JSDM context typically remains elusive as they represent combinations of multiple factors causing species-species correlations not explained by niche similarity. In this study, they are interpreted primarily as biotic interactions, but we acknowledge the fact that they could—to some degree—also represent missing covariates (Hui, 2016).

While the latent variable structure was assumed to be independent, the random effect (Lake: HOU, LAC, and CAZ) allowed to account for any distinct differences between the lakes not captured by the environmental variables used. In addition, we also added a random row effect (Site: 1 to 435) to the model. During initial attempts to calibrate the final model additional non-converging variables were iteratively removed. The model formula (argument formula.X in the boral function) included all the remaining environmental variables, including the interaction BCI:BCIvar, which was included since convexity can fluctuate within a site (e.g., a BCI of zero could result from bends in opposite directions).

We used two model structures M1 and M2, with M1 and M2 including and excluding the random effects, respectively. However, the predictive power of model M2 was substantially lesser than that of M1. For the results and downstream management purposes we therefore chose to only report model predictions for M1.

Initially, we fitted two preliminary models (n.burnin = 200; n.iteration = 1000; seed = 1) for each of M1 and M2; one with two and one with three latent variables. Importantly, we did not use thinning (i.e., n. thin = 1) for the preliminary models since this would reduce the number of MCMC samples to an overly small number—a thinning rate of ten, for example, would result in only 80 MCMC samples in our case. Despite some suggestions to favor marginal predictions (Merkle et al., 2019), we
chose to use conditional predictions whenever possible. This decision was primarily based on substantial losses of model performance observed in preliminary models when switching the prediction type from conditional to marginal.

The final number of latent variables used for M1 and M2 were chosen based on their overall predictive performance (mean R² and mean AUC across all species). The final models (M1 and M2) were then run with 30,000 iterations in total and a burnin of 20,000 (i.e., producing 10,000 posterior samples before thinning), and again a seed for replicability (seed = 1). In contrast to preliminary models, we used thinning for the final models (n.thin = 5; resulting in 2000 MCMC samples for computing means and assessing convergence) in order to reduce autocorrelation among MCMC samples, while limiting memory and storage use (Geyer, 1992; Jones and Qin, 2022; Link and Eaton, 2012). Predictions for current and future conditions were generated by predicting the fitted model (based on its posterior means) onto both the original data and the warming scenario (identical except TEMP being increased by 2 °C). Both final models were examined using diagnostic plots, Geweke's convergence diagnostic (Geweke, 1992) and variance partitioning, and evaluated using the mean R² and mean AUC (Fielding and Bell, 1997) across species. In addition, model performance was evaluated for each individual species by AUC. Furthermore, we converted continuous predictions of habitat suitability into binary presence-absence predictions by applying the threshold that maximizes the sum of sensitivity (SEN) and specificity (SPE) (Liu et al., 2005). These binary predictions were evaluated by SEN, SPE, the underprediction rate (UPR) and over-prediction rate (OPR) (Barbosa et al., 2013), as well as the symmetric extremal dependence index (SEDI) (Ferro and Stephenson, 2011; Wunderlich et al., 2019). The individual evaluation scores for individual species were classified in line with Swets (1988) and Wunderlich et al. (2022), adapted for those metrics with ranges other than zero to one and a scale inversion for UPR and OPR since their optimum values are equal to zero (Table S3, Supplementary Information).

## 2.7. Spatial management prioritization and additional statistics

Spatial prioritization and additional statistics were exclusively performed for predictions of model M1 since it outperformed model M2. Four protected native isoetids (Baldellia ranunculoides, Caropsis verticillatoinundata, Littorella uniflora, and Lobelia dortmanna) and five invasive/nuisance species (Egeria densa, Lagarosiphon major, Ludwigia grandiflora, Ludwigia peploides, and Sagittaria graminea) were selected as focal species and further analyzed, based on their relevance to ecosystem functioning and invasive species status, respectively. In addition, we computed the native species richness (i.e., excluding the five invasive/nuisance species) for all sites for further analysis. For the four native isoetids and five invasive/nuisance species, as well as for native species diversity, we identified their current and future (i.e., under warming) hotspots to aid conservation planning. The hotspots were identified by applying a percentile threshold (Gaston and David, 1994), here, the 90th percentile, to predicted habitat suitability (site maximum across natives or invasives, respectively) and species richness, respectively. We combined current and future hotspots of isoetids, invasives, and diversity into consensus maps of four management levels (Table 2) by adding one for each current hotspot and two for each future hotspot to zero, i.e., using the formula: Mangement Level = 0 + Current + 2*Future, where the weighting ('2') is solely applied for being able to differentiate between current and future hotspots. In addition, we used series of paired t-tests and barplots to compare the values of predicted habitat suitability under current conditions and warming and visualize the respective predicted changes, respectively, for individual native isoetid species and individual invasive species. T-tests were also used to assess the differences in predicted species richness between the current conditions and the warming scenario. Finally, we quantified both environmental and residual correlations between all modeled species in order to assess niche similarity and species interactions.

<caption>**Table 2**</caption>

Breakdown of management implications for factor levels resulting from consensus hotspot analysis, including (from left to right) the factor levels (L) and their according interpretations for isoetids, invasives, and diversity.

<table><thead><tr><th>L</th><th>Management implications</th><th></th><th></th></tr><tr><th></th><th>Isoetids</th><th>Invasives</th><th>Diversity</th></tr></thead><tbody><tr><td>0</td><td>Low priority</td><td>Low priority</td><td>Low priority</td></tr><tr><td>1</td><td>Conservation and restoration</td><td>Control &amp; monitoring</td><td>Conservation and restoration</td></tr><tr><td>2</td><td>Conservation of future habitat</td><td>Monitoring for early detection</td><td>Conservation of future hotspots</td></tr><tr><td>3</td><td>Conservation of climate refugia</td><td>Control &amp; intensive monitoring</td><td>Climate refugium</td></tr></tbody></table>

# 3. Results

## 3.1. Variable selection and removal

Our multi-stage variable selection approach (including removal of non-converging variables during calibration) resulted in the elimination of 15 environmental variables (H, LCO_PCT_Forest, LCO_PCT_OtherV, LSM_CENT, LSM_ED, LSMEnt, LSM_JENT, LSM_LSI, LSM_PD, ROUGH, SLP, TRI, TRIriley, TRIrmsd, WAW_PCT_PERM) and four of the five interactions (TEMP:HLI, WEX:BCI, WEX:MEE, and WEX:VRM). Accordingly, the final models were run with 13 environmental variables and 1 interaction, i.e., a total of 14 variables (not including random effects), resulting in a ratio of sites to environmental variables of ~31. The retained environmental variables are listed in Table 1.

## 3.2. Modeling and evaluation

### 3.2.1. Model structure and model performance across species

There was no significant difference in mean model performance between two and three latent variables in terms of both R² (R²<sub>2LV</sub> - R²<sub>3LV</sub> = -0.0399; t = -4.4999; df = 1; p = 0.1392) and AUC (AUC²<sub>2LV</sub> - AUC²<sub>3LV</sub> = -0.0100; t = -3.0999; df = 1; p = 0.1987) across species in the preliminary models M1 and M2. However, we still chose final models being calibrated with three latent variables in order to maximize the predictive skill. Notably, the R² and AUC values of preliminary models with three latent variables were equal to 0.6620 and 0.9691 (M1), and 0.6064 and 0.9564 (M2), respectively—suggesting at least sufficient model performance. The last round of calibration (i.e., after removal of the last non-converging variable) converged well, reaching stability for 95.95% and 93.45% of the parameters in final models M1 and M2, respectively. Diagnostic plots (Figs. S1 and S2, Supplementary Information) and satisfactory predictive performance computed across species (M1 : R² = 0.6627, AUC = 0.9691; M2 : R² = 0.6126; AUC = 0.9583) also support successful calibration.

#### 3.2.1.1. Model performance for individual species.
For the final model M1, the individual AUC scores ranged from 0.8467 ("good"; Schoenoplectus lacustris) to 1.0000 ("perfect"; Nuphar lutea, Osmunda regalis, and Sagittaria graminea), with a mean value of 0.9540 ("excellent"). The final model M2 performed slightly less well, with minimum, mean, and maximum AUC scores of 0.7962 ("fair"; Schoenoplectus lacustris), 0.9382 ("good"), and 0.9996 ("excellent"; Nuphar lutea). Overall, there was a trend toward overprediction, with minimum, mean, and maximum OPR scores of 0.0000 ("perfect"; Nuphar lutea and Osmunda regalis), 0.3165 ("strongly overpredictive"), and 0.7227 ("extremely overpredictive"; Elatine hexandra) for final model M1, and 0.0526 ("excellent", Chara fragifera), 0.3788 ("strongly overpredictive"), and 0.7891 ("extremely overpredictive"; Eleocharis palustris) for final model M2, respectively. To a lesser extent, this overprediction trend is also reflected in the specificity, with minimum, mean, and maximum scores of 0.7373 ("fair"; Schoenoplectus lacustris), 0.8765 ("good"), and 1.0000 ("perfect"; Nuphar
lutea, Osmunda regalis, and *Sagittaria graminea*) for final model M1, and 0.5416 ("useful"; *Schoenoplectus lacustris*), 0.8426 ("good"), and 0.9952 ("excellent"; *Nuphar lutea*) for final model M2, respectively. Good overall, performance across species is also supported by minimum and mean SEDI scores of 0.7195 ("good"; *Schoenoplectus lacustris*) and 0.9099 ("excellent") for final model M1, and 0.6762 ("good"; *Schoenoplectus lacustris*) and 0.8906 ("excellent") for final model M2. The complete scores for all metrics and all species for the final models M1 and M2 are provided in Tables S4 and S5 (Supplementary Information).

### 3.2.2. Variance partitioning and variable importance

In model M1 (M2 in brackets) and across all species, random factors (Lake), latent variables, environmental variables, and site effects explained 44.06% (n.a.), 25.38% (71.18%), 15.32% (28.82%), and 15.24% (n.a.). Traits were able to explain 64.88% (M1) and 64.51% (M2) of the environmental response. The three most influential environmental variables in final model M1, cumulatively accounting for >50% of the variation explained by the environment on average, were LCO_PCT-Barren (30.03%), VRM (14.87%), and MEE (10.95%) (Table 1). In final model M2, the same three environmental variables were identified as most influential and in the same order, albeit with slightly different contributions: LCO_PCT-Barren (30.90%), VRM (16.65%), and MEE (12.08%) (Table 1).

## 3.3. Spatial management prioritization and additional statistics

Hotspot analysis using percentile-thresholds revealed distinct patterns of hotspots of isoetid habitat suitability, native species diversity, and invasive species habitat suitability Fig. 2 (for a version using a lake-by-lake percentile-thresholding approach please see Fig. S3, Supplementary Information). Both visual appraisal and correlation analysis ($\rho = 0.6978; t = 20.2730; df = 433; p < 0.0001$) indicated a high agreement between native isoetid habitat suitability and native species diversity. In contrast, the weak positive correlation between native isoetid habitat suitability and invasive species' habitat suitability ($\rho = 0.0934; t = 1.9523; df = 433; p = 0.0516$) was not significant. When using lake-specific thresholds, these patterns were mostly consistent; however, there are some differences that may matter for local conservation planning. For example, more sites are suggested for immediate management action in HOU regarding invasive species—and they are spread over a much larger extent despite still being concentrated in the northwest (Fig. S3, Supplementary Information).

### 3.3.1. Local hotspot patterns

For HOU (Fig. 2a–d,g), both native isoetid species habitat suitability hotspots and native species diversity hotspots were predominantly found along the eastern shoreline and their majority might represent climate refugia under a warming scenario. Native isoetid species habitat suitability hotspots were concentrated north and south of Lachanau (ca.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Maps of Lac de Carcans-Hourtin (a,d,g), Étang de Lacanau (b,e,h), Étang de Cazaux et de Sanguinet (c,f,i) detailing management hotspots (region-wide percentile threshold) for native isoetid habitat suitability (a–c), invasive species habitat suitability (d–f), and native species diversity (g–i); individual sites are shown as circles—filled according to the legends provided in the fourth column; graticules are shown as dashed lines and all maps are shown in an equal-area projection (EPSG:3035).</figcaption></figure>
45° 9' N, 1° 4' W), as well as north-west of Le Poutch (ca. 45° 4' N, 1° 5' W), with only few hotspots along the west (e.g., ~1 km southwest of Pointe de Malignac). The distribution of native species diversity was similar but with less and more hotspots around Lachanau and northwest-to-west of Le Poutch, respectively. Invasive species habitat suitability hotspots were limited to three sites northwest (~800m) and southwest (~600m) of Piqueyrot (ca. 45° 12' N, 1° 7' W).

The patterns of hotspots in LAC (Fig. 2b-e,h) were generally similar to those in HOU. However, there were also some major differences. First, we identified only three native isoetid species habitat suitability hotspots along the north-eastern shore and those might vanish under a warming scenario. Second, all but two of twenty-three native species diversity hotspots (~28% of all sites in LAC and all situated along the eastern shore) were future—but not current—hotspots. Third, invasive species habitat suitability hotspots were not limited to the northwest but were scattered around the lake (except the northeastern shore) and their majority might represent climate refugia (i.e., identified as both current and future hotspots for invasives).

In CAZ (Fig. 2c-f,i), the hotspots of predicted native isoetid species habitat suitability were more scattered, with some clustering along the northeastern and southeastern shore. Similarly, native species diversity hotspots were also scattered, but three neighboring such hotspots were clustered in the northwest (approximately 44° 31' N, 1° 11' W), ~500 m southwest of the Plage de Cazaux-Laouga. Lastly, most hotspots of invasive species habitat suitability (19 of 25) were found along the eastern shore and all but one of the them were future—but not current—hotspots. The only current identified hotspot of invasive species habitat suitability was situated next to the Sailing Club of Sanguinet (ca. 44° 29' N, 1° 5' W).

### 3.3.2. Predicted changes under warming

For both native isoetid species and invasive species, the mean habitat suitability decreased across the lakes when models were extrapolated to increased temperature (Table 3). Generally, native isoetid species were impacted more severely than invasive species (min{$HS_{drop}^{native}$} > max{$HS_{drop}^{invasive}$}). These results, were also used to prepare boxplots (Fig. S4). The boxplots visualized the generally decreasing habitat suitability of native isoetids under a warming scenario, and—moreover—revealed that the interquartile range of habitat suitability no longer exceeds the presence-absence threshold for some species, when it still did for the baseline. The latter pattern was predicted for *Baldellia ranunculoides* (LAC & CAZ) and *Lobelia dortmanna* (in LAC). Similarly, the boxplots confirmed an overall—but less pronounced—decrease in habitat suitability for invasive species (Fig. S4). A paired t-test comparing mean species richness (excluding invasive species; S̄) under baseline conditions and warming also predicted a

<table><caption>**Table 3**
Mean drop of habitat suitability (in percent) under warming and t-statistic according to paired t-tests, for four native isoetid species and five invasive species.</caption><thead><tr><th>Group</th><th>Species Name</th><th>Mean Drop of Habitat Suitability Under Warming (%)</th><th>Value of the t-statistic<sup>a</sup></th></tr></thead><tbody><tr><td rowspan="4">Native Isoetids</td><td><em>Baldellia ranunculoides</em></td><td>36.25</td><td>17.6520</td></tr><tr><td><em>Caropsis verticillatoinundata</em></td><td>22.62</td><td>11.7880</td></tr><tr><td><em>Littorella uniflora</em></td><td>23.50</td><td>18.1170</td></tr><tr><td><em>Lobelia dortmanna</em></td><td>16.92</td><td>19.6000</td></tr><tr><td rowspan="5">Invasive species</td><td><em>Egeria densa</em></td><td>4.06</td><td>6.7758</td></tr><tr><td><em>Lagarosiphon major</em></td><td>13.27</td><td>26.2210</td></tr><tr><td><em>Ludwigia grandiflora</em></td><td>5.27</td><td>13.6400</td></tr><tr><td><em>Ludwigia peploides</em></td><td>1.23</td><td>4.4649</td></tr><tr><td><em>Sagittaria graminea</em></td><td>16.14</td><td>9.0488</td></tr></tbody></table>

significant drop under warming ($S_{drop} = 15.27\%$; $t = 20.2700$; $df = 434$; $p < 0.0001$). Finally, the correlation plot revealed that, despite primarily being positively or not significantly correlated (Fig. 3, lower left triangle), there were significant negative residual correlations between species (Fig. 3, upper right triangle), importantly, between *Egeria densa* and native isoetids (mean ± standard deviation: −0.6890 ± 0.0403). In addition, there were further negative residual correlations between *Egeria densa* and other species, including *Hydrocotyle vulgaris* (−0.6819), *Hypericum elodes* (−0.8058), *Juncus bulbosus* (−0.6057), *Juncus heterophyllus* (−0.7732), and *Schoenus nigricans* (−0.8578).

## 4. Discussion

Here, we have calibrated a multi species (n = 44) joint distribution model of macrophytes in a Bayesian framework to make predictions of habitat suitability and presence-absence of species—under current conditions and a hypothetical +2 °C-warming scenario. The model and its predictions could directly be used to provide local management suggestions (pertaining to isoetids, invasives, and native species diversity) at high spatial resolution (~3.14 ha) for both regional (Fig. 2) and lake-specific (Fig. S3, Supplementary Information) stakeholders. In addition, we assessed how habitat suitability of nine focal species (four native isoetids and five invasives) as well as native species diversity would likely change in three lakes under hypothetical warming.

Since the inception of SDM in the 1970s and 1980s (Box, 1981; Nix et al., 1977; Nix, 1986), its execution has become routine—largely owing to the release of software packages, such as maxent (Phillips, 2017; Phillips et al., 2004, 2006, 2017) or sdm (Naimi and Araújo, 2016). For macrophytes, however, SDM and related methods such as occupancy modeling seem to have been adopted comparatively reliably. We found only few examples up to the year 2015, including the works of Valle et al. (2012), and Auderset Joye and Rey-Boissezon (2015), who modeled habitat stability of eelgrass (*Zostera marina*) in the Ems estuary (Netherlands) and charophytes in Switzerland, respectively. Since then, SDM has become more readily adopted in the macrophyte modeling community with numerous publications investigating multiple aspects, such as niche conservatism (Alahuhta et al., 2017), the influence of specific variables or variable groups (Bertrin et al., 2018; Gillard et al., 2020) and the trajectories of native and invasive species (Gillard et al., 2017; Steen, 2019). However, SDM is not routinely applied to inform local management or conservation planning for macrophytes, especially in freshwater. One of the few examples we could find in the literature investigated the spatial overlap of an invasive macrophyte genus (*Elodea*, now commonly referred to as *Egeria*) with regions of Native Alaskan subsistence depending on fishing (Luizza et al., 2016). More recently, Mikulyuk et al. (2020) combined SDM predictions of both probability of presence and abundance of a non-native macrophyte (*Myriophyllum spicatum*) in Wisconsin lakes to categorize invasion risk and hence management priority. In addition, the suitability of macrophytes to act as surrogates in conservation planning was assessed (in terms of extent, connectedness, and representativeness) and compared to fishes and benthic macroinvertebrates in the Hungarian part of the Danube River catchment (Sály et al., 2020). Another recent example utilizes occupancy modeling to support early detection of an invasive (*Nitellopsis obtusa*) in Minnesota and Wisconsin and identified distance to access sites as a primary driver of invasive spread (Bajcz et al., 2024). Potentially owing to its more recent development, JSDM appears to have been used especially rarely in the context of freshwater macrophytes, but see notable exceptions (García-Girón et al., 2022; Kim and Nishihiro, 2020). This study might be the first harnessing JSDM to inform local management of or conservation planning for freshwater macrophytes. In addition, we have used an intuitive approach to quantitatively factor in direct anthropogenic disturbance (primarily, trampling), which can strongly affect macrophytes (Liddle

<footnote><sup>a</sup> Across all rows, the degrees of freedom and p-values are identical (df = 434; p < 0.0001) and thus omitted.</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Correlation plot comprising environmental similarity (correlation of the environmental parameters between species; lower left triangle) and residual correlation (correlation after factoring out the environment; upper right triangle); numbers along the axes indicate the species number (N°SP) as in Table S1 (for example, *Egeria densa* = 10). The size and color of filled circles indicate the strength and direction of correlations (small to large: weak to strong; red to blue: negative to positive); non-significant correlations and the diagonal are not shown.</figcaption></figure>

and Scorgie, 1980), by utilizing a least-cost-path framework calculating the minimum energy expenditure between all locations and access hubs—effectively an incorporation of the energy landscape (Shepard et al., 2013) for humans.

A wide variety of environmental variables is used for modeling the distribution of macrophytes. Our results demonstrate that, at the scale of our analysis (~3.14 ha per site), aquatic plant communities were majorly explained by LCO_PCT-Barren (percentage of outside-of-lake area covered by barren/built-up cells; ~30%), VRM (vector ruggedness measure; ~15%), and MEE (minimum energy expenditure to reach a location from the nearest access hub; ~11%). Further influential variables included, LCO_PCT_Water (percentage of outside-of-lake area covered by water; ~8%), WEX (accumulated wind exposure over the seasons in an average year; ~6%), LCO_PCT_Cultivated (percentage of outside-of-lake area covered by agriculture; ~5%), and LCO_PCT_Wet-land (percentage of outside-of-lake area covered by wetlands; ~5%). These results suggest a potentially strong anthropogenic influence on macrophyte communities, comprising both indirect (by land-use modification in the surrounding area) and direct impacts (e.g., by trampling), but also local variation in underwater terrain (depth, slope and aspect),

and natural disturbance in the form of wind-induced hydrodynamics. These results are in line with earlier studies highlighting the sensitivity of macrophyte communities to anthropogenic impacts, such as trampling (Liddle and Scorgie, 1980), as occurring in association with recreational swimming areas (Bertrin et al., 2018), or habitat alteration and land-use change (Akasaka et al., 2010; Kim and Nishihiro, 2020; McGoff et al., 2013). Our study thus also corroborates the findings of Kolada et al. (2024), who identified slope and wind exposure as minor determinants of the variation in hydrophyte communities for Polish lakes.

Overall, the calibration of models was successful (Figs. S1 and S2, Supplementary Information), resulting in fair to excellent model performance according to a large number of evaluation metrics (Tables S4 and S5, Supplementary Information). Specifically, model M1 (used for spatial predictions, prioritization, and additional statistics) exhibited high accuracy across all species ($R^2$ > 0.6; AUC > 0.9) with good predictive skill (AUC > 0.8) even for the worst-performing species (*Schoenoplectus lacustris*) in the dataset. Further, not only the threshold-independent AUC, which is also largely unaffected by data prevalence (although, its standard error seems to be: McPherson et al., 2004), SEN and SPE, which are among the most commonly chosen metrics to
quantify model performance in SDM studies, but also SEDI, which has been shown to be useful over a broad range of modeling conditions (Wunderlich et al., 2019) suggest at least fair, and mostly good to excellent, performance. Despite the SEN-SPE ratio being very close to 1 (SEN/SPE = 1.0611), our model (M1) appeared mildly overpredictive (SEN = 0.9301; SPÉ = 0.8765)—in line with a general trend in SDM (Velazco et al., 2020). However, the optimization approach taken to compute the thresholds (maximizing the sum of SEN and SPE) used to convert continuous predictions of habitat suitability into binary presence-absence naturally limits the observable difference between SEN and SPE (Barbosa et al., 2013). Therefore, we had also computed OPR (OPR = 0.3165), UPR (UPR = 0.0340), and their ratio (OPR/UPR = 9.3156). These metrics suggest a much stronger tendency toward overprediction than underprediction. However, both OPR and UPR are not measures of actual commission and omission but quantify the misclassification rate over the potential instead of the observed occurrence area. Thus, they might also be influenced by biotic interactions (or the lack as well as novel types thereof) and the (degree of) species-environment equilibrium (Barbosa et al., 2013), or—for invasive species—the proportion of the area still suitable for colonization (Muñoz and Real, 2006). Although overprediction could be an issue in spatial conservation planning and may (especially at large spatial extents) necessitate the application of constraining methods (Velazco et al., 2020), our relatively small study area, with sites exclusively along the lake shores and without spatial extrapolation, would suggest that the potential consequences of overprediction in this study are comparatively limited.

Spatial conservation prioritization or systematic conservation planning optimizes the design or expansion of protected areas by maximizing benefits and minimizing costs (Margules and Pressey, 2000), ideally with consideration of foreseeable change (Carroll et al., 2010). Considerations of current vs. future conditions are also crucial for viable conservation translocations, which might be integral to conservation management (Finn et al., 2024). Often the practice involves the use of specialized software packages, for example, zonation or marxan (Lehtomäki and Moilanen, 2013; Moilanen et al., 2009; Possingham et al., 2000; Watts et al., 2017). However, prioritization can also be based on classification or percentiles; recent examples in invasive species or roadkill management include the works of Mikulyuk et al. (2020) and Karanasios et al. (2021). In this study, we followed an approach similar to those two publications, and implemented spatial prioritization by applying percentile-based thresholds to both baseline and warming-scenario predictions and integrating them into four classes of management importance. In summary, we identify not only current and future hotspots—to identify sites where monitoring or restoration is or will be critical—but also their consensus which allows managing for the conservation of potential in situ climate refugia (sensu Ashcroft, 2010). This is important since anticipating climate change is crucial in systematic conservation planning (Carroll et al., 2010). However, we want to remind managers and other stakeholders that our regional and local management maps (Fig. 2; Fig. S3, Supplementary Information) did not incorporate costs such as land-value. Therefore, we would recommend to build on our maps by combining them with information on land-ownership and land-value in order to generate cost-effective conservation solutions (Naidoo et al., 2006). Ideally, alternative management scenarios—especially in relation to accessibility (for both humans and livestock), land-use-change, and water table fluctuation—could be developed using a similar workflow as described here but including participatory methods to involve all relevant stakeholders (Addison et al., 2015; Lees et al., 2021).

Both statistical analysis and boxplots (Fig. S4, Supplementary Information) revealed decreasing habitat suitability (~15%) across the focal species and the three lakes under a 2 °C warming scenario, despite the limited mean influence of TEMP (Table 1), potentially, suggesting, numerous near-threshold-temperature occurrences or indirect TEMP

influence via species interactions. Intriguingly, this trend of decreasing habitat suitability was exhibited by both native isoetids (~25%) and invasive species (~8%). This was unexpected since the origins of *E. densa*, *L. grandiflora*, and *L. peploides* represent (sub-)tropical regions (Yarrow et al., 2009), and earlier studies suggested (at least seasonally) increased rates of growth under warming in France, for example, in *E. densa* (Silveira and Thiébaut, 2017). However, this increase might be contingent on the availability of nutrients which are relatively sparse in the lakes studied. Together, the differential rates of decrease in habitat suitability of native isoetids and invasive point towards a potential turnover of species with a shift toward invasives—paired with an overall decline of macrophyte habitat, leading not only to potentially higher lacustrine trophic levels (Hilt et al., 2010; Ribaudo et al., 2017; Smolders et al., 2002), but also loss of structural diversity and shelter.

Finally, the potential of *Egeria densa* as an invasive species is critical—especially since its habitat suitability decreases less than that of native species (Fig. S4, Supplementary Information) and its negative residual correlations with native species (Fig. 3). While the species is considered as potential invasion risk and invasion risk in Belgium and France, respectively (Fried, 2010; Lafontaine et al., 2013), variation between geographic regions and individual watersheds would be expected in response to *E. densa*. Nevertheless, it is well known that this fast-growing macrophyte native to South America thriving under a broad range of light conditions, has spread across large parts of the globe (Matheson et al., 2005), often leading toward deteriorated ecosystems and lengthy and/or costly attempts for its control (Caudill et al., 2019; Champion, 2002). Specifically, *E. densa* can disrupt natural sedimentation, animal movement, and predator-prey systems, and can outcompete and replace native macrophytes in the community (partly, by shading other plants); moreover, it can cause impaired wind mixing, local depletion of oxygen, elevated water temperature (by light absorption), a sudden nutrient influx (in case of a die off), as well as create breeding areas for mosquitos (Bertrin et al., 2017; Caudill et al., 2019; Champion, 2002; Lafontaine et al., 2013; Ribaudo et al., 2021).

In the context of conservation management, these findings offer valuable insights for managers and stakeholders. At the community scale, the positive correlation between native isoetid habitat suitability and native species diversity highlights the need for preserving the whole community assemblages for better conservation of focal protected species. The significant role of barren land use near shorelines underscores the potential for developing land-use policies to limit such development in the lake's vicinity, following the most advanced policies in shoreline and marine coastal conservation (Abrahams, 2008; Meur-Férec, 1997). Also, the substantial influence of human accessibility would suggest to implement local restrictions on recreational use (and tentatively on grazing), especially, in isoetid hotspots since their shallow water habitat does not induce swimming but walking, and, thence, trampling (Bertrin et al., 2018). We identified and mapped hotspots of diversity where such conservation efforts should be focused. This will help prevent the loss of local communities in certain areas and ensure that other locations can serve as refugia for populations in the future. This spatial analysis will also be valuable for managers in identifying areas at risk of invasive species colonization, highlighting locations where monitoring efforts should be intensified for potential management actions. In practical terms, the conservation efforts undertaken by local managers (SIAEB-VELG, Géolandes, the Communauté de Communes des Grands Lacs, municipal authorities, hunting and fishing associations, water sports centers, etc.) primarily involve the regular manual removal of *Ludwigia* spp. and mechanical harvesting of *Lagarosiphon major* and *Egeria densa* in harbors and channels, alongside policies/action aimed at preventing and reducing new establishments of recreational areas (e.g., swimming and water skiing) or deforestation and vehicle/boating access in the coastal zone, respectively (Fig. S5). On a broader scale, these actions also contribute toward regional efforts combatting eutrophication. Consequently, our results can help selecting actual sites for the management of both protected and invasive species, while adding valuable data to
inform regional conservation management (land-use).

## 5. Conclusions

Macrophyte habitat suitability is generally predicted to decrease under warming in Aquitanian lakes. However, native isometids appear to be more strongly impacted than invasive species. This has implications not only for future macrophyte community structure and diversity but also the lakes' future trophic state and the provisioning of ecosystem services. This is since isometids play a central role of in maintaining lake sediment biochemistry. Moreover, we identified anthropogenic impacts (land-use and human accessibility) as the main driving force of species distributions, suggesting land-use change and accessibility (humans and, tentatively, livestock) should be restricted at management hotspots to potentially compensate warming impacts and further the conservation of native isometids and native species diversity, while limiting the potential spread of invasives to sites not colonized yet.

### CRediT authorship contribution statement

**Rainer Ferdinand Wunderlich:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Aurélien Jamouneau:** Writing – review & editing, Validation, Supervision, Funding acquisition, Data curation, Conceptualization. **Sébastien Boutry:** Writing – review & editing, Validation, Software, Data curation. **Maha Hosni:** Writing – review & editing, Visualization, **Vincent Bertrin:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Data curation, Conceptualization.

### Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) occasionally used ChatGPT in order to speed up coding. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

### Declaration of competing interest

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.

### Acknowledgements

We thank Benjamin Weigel for sound advice related to joint species distribution modeling. We are also grateful to Albin Meyer, Quentin Petitjean, and Margaux Herschel for helpful and often entertaining discussions. This study was part of the Vigie-Lac project and financially supported by the Adour-Garonne Water Agency (Nº 310 33 01917) and ESR Nouvelle-Aquitaine Region (Nº AAPR2022-2021-17081910). Copernicus Land Monitoring Service products and services used in this study were produced with funding by the European Union.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvman.2025.124543.

### Data availability

Data will be made available on request.

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