# Long-term changes of zooplankton in alpine lakes result from a combination of local and global threats

Rocco Tiberti, Flavia Dory, Florent Arthaud, Vincent Augé, Carole Birk, Laurent Cavalli, Diego Fontaneto, Raphaelle Napoleoni, Marie Elodie Perga, Ibor Sabás, et al.

► To cite this version:

Rocco Tiberti, Flavia Dory, Florent Arthaud, Vincent Augé, Carole Birk, et al. Long-term changes of zooplankton in alpine lakes result from a combination of local and global threats. Biological Conservation, 2025, 308, pp.111222. 10.1016/j.biocon.2025.111222. hal-05447198

HAL Id: hal-05447198

https://hal.inrae.fr/hal-05447198v1

Submitted on 7 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

# Long-term changes of zooplankton in alpine lakes result from a combination of local and global threats

Rocco Tiberti<sup>a, *</sup> <sup>●</sup>, Flavia Dory<sup>b</sup>, Florent Arthaud<sup>c</sup>, Vincent Augé<sup>d</sup>, Carole Birk<sup>e</sup>, Laurent Cavalli<sup>f</sup>, Diego Fontaneto<sup>g,h</sup>, Raphaelle Napoleoni<sup>d</sup>, Marie Elodie Perga<sup>i</sup>, Ibor Sabás<sup>j</sup>, Clotilde Sagot<sup>k</sup>, Ruben Sommaruga<sup>j</sup>

## ARTICLE INFO

Keywords:

Alps

Local impacts

Global warming

Alien fish

Glacial influence

Ecological-functional traits

## ABSTRACT

Lakes located above treeline or alpine ones offer valuable opportunities to assess the effects of natural and human-induced influences, ranging from local to global scales. Further, there is a growing concern about several local threats jeopardizing the ecological integrity of these ecosystems. Zooplankton communities, which essentially comprise the entire pelagic consumer compartment in naturally fishless alpine lakes, can indicate ecosystem changes. Here, we analysed 643 zooplankton samples from 101 lakes and ponds in the Western Alps, collected over 19 years (2006–2024). Community data -taxa and functional groups- were examined to evaluate zooplankton responses to local natural and anthropogenic factors and to detect long-term changes linked to broader regional or global trends. Long-term trends showed declines in cold stenothermal taxa and groups, which are consistent with the effects of climate warming. Despite most lakes being located within protected areas, about half were affected by local anthropogenic factors such as fish introduction and hydroelectric exploitation. Our findings highlight that a critical component of aquatic ecosystems underwent significant taxonomic and functional composition shifts over time, likely due to a combination of climate change and widespread fish introductions. The study emphasises the need for conservation measures and actions to safeguard still pristine alpine lakes and recover impacted ones to enhance mountain aquatic biota resilience to projected global changes.

## 1. Introduction

Zooplankton are key components in most aquatic ecosystems where they act as intermediate consumers and as a link to higher trophic levels, as well as regulators of primary production and nutrient cycling (Sterner, 2009). However, in alpine lakes, which are characterized by short pelagic trophic chains, the natural absence of usual predators, such as fish, places zooplankton at the top of pelagic food webs (Sánchez-Hernández et al., 2015). Without significant top-down predatory

pressure, the structure of zooplankton communities, including species composition and abundance, is primarily shaped by abiotic conditions and food availability (Loewen et al., 2019). As an extreme example of this control mechanism, highly selective conditions found at higher elevations or in lakes fed by glaciers can cause zooplankton and organisms from lower trophic levels to become virtually absent (Sommaruga, 2015).

The comparatively simpler food webs of alpine lakes and their zooplankton communities offer a valuable framework for ecological

<footnote><sup>a</sup> Dipartimento di Biologia Ecologia e Scienze della Terra – DiBEST, Università della Calabria, Ponte Pietro Bucci 6B, 87030 Rende, Italy</footnote>

<footnote><sup>b</sup> University of Milano-Bicocca, Department of Earth and Environmental Sciences, Piazza della Scienza 1, Milan, Italy</footnote>

<footnote><sup>c</sup> Université Savoie Mont Blanc, INRAE, CARRTEL, Thonon-les-Bains, France</footnote>

<footnote><sup>d</sup> Parc National Vanoise, Chambéry, Auvergne-Rhone-Alpes, France</footnote>

<footnote><sup>e</sup> Conservatoire d'espaces naturels de Haute-Savoie -ASTER, Annecy, France</footnote>

<footnote><sup>f</sup> Aix Marseille University, Avignon University, CNRS, IRD, IMBE, Marseille, France</footnote>

<footnote><sup>g</sup> National Research Council, Water Research Institute (CNR-IRSA), Largo Tonolli 50, 28922 Verbania Pallanza, Italy</footnote>

<footnote><sup>h</sup> NBFC (National Biodiversity Future Center), Piazza Marina 61, 90133 Palermo, Italy</footnote>

<footnote><sup>i</sup> Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland</footnote>

<footnote><sup>j</sup> Department of Ecology, Universität Innsbruck, Technikerstr. 25, 6020 Innsbruck, Austria</footnote>

<footnote><sup>k</sup> Parc National des Ecrins, Domaine de Charance, 05000 Gap, France</footnote>

<footnote>* Corresponding author.
E-mail address: rocco.tiberti@unical.it (R. Tiberti).</footnote>
studies, particularly on the effects of human activities on a regional or global scale. Indeed, the intrinsic complexity of ecological interactions often makes it challenging to interpret ecological data and isolate the effects of multiple human impacts from the natural dynamics. Using remote ecosystems such as alpine lakes can instead help test ecological hypotheses (Williams et al., 2002; Catalan et al., 2013). These relatively undisturbed environments can serve as reference ecosystems where human pressures act singularly or a few at a time (Loewen et al., 2019). In this regard, alpine lakes are often referred to as “sentinels” of global change (Williamson et al., 2009; Birck et al., 2013). Yet, there is a growing concern over the fragility of alpine lakes due to the recognition of various local threats that jeopardize their ecological integrity (Bradford et al., 1998; Knapp et al., 2001). Although they are typically low-biodiversity ecosystems, alpine lakes possess substantial and imperilled conservation significance because they serve as refuges for sensitive and specialized species that struggle to survive in anthropized areas or are adapted to cold climates (Ventura et al., 2017).

Among the local threats identified are hydroelectric exploitation (Spitale et al., 2015), ski infrastructures (Hatch et al., 2001), point and non-point sources of organic pollutants (e.g., from mountain huts and livestock; Derlet et al., 2012), and numerous emerging pollutants, which can reach the lakes with tourists and livestock (Machate et al., 2022). However, the most widespread local threat is fish introduction. Evidence that various fish species, especially salmonids, have been introduced in more than half of the lakes and ponds larger than half a hectare, and in almost all larger lakes, is commonly reported for many lake districts from mountain regions all over the world (Ventura et al., 2017). Fish introductions usually exert a strong predation pressure on zooplankton (Knapp et al., 2001), a phenomenon that would seldom occur in alpine lakes under natural conditions because native nektonic predators, such as aquatic invertebrates and carnivorous amphibians are scarce or absent (Schabetsberger et al., 2006).

Considering lakes as both reference ecosystems and conservation

targets, we conducted a study on the long-term dynamics (2006–2024) of zooplankton (namely, zooplanktonic organisms exceeding 50 µm in size) across >100 alpine lakes and ponds of the western European Alps. Hereafter, for simplicity, we will refer to both lakes and ponds as “lakes”. Our primary research objective was to assess how zooplankton change over the long-term and in response to local human disturbances, while considering the effects of natural seasonal and spatial gradients. By sampling a diverse array of water bodies—ranging from untouched to locally impacted by introduced fish and hydroelectric exploitation—in a region where the trend of air temperature rise is particularly pronounced (Harris et al., 2014), we sought to distinguish multi-annual trends stemming from regional and global factors from the effects of local environmental conditions and impacts. Our main hypothesis is that zooplankton communities and their long-term dynamics are significantly influenced by a combination of local conditions and both local and global human activities. We therefore tested how these communities respond to natural environmental gradients (e.g., elevation, seasonality, depth, and glacier cover) to understand -and account for- their ecological preferences. Furthermore, we quantified how communities, particularly climate-sensitive stenothermal taxa and groups, changed over the study period and how anthropogenic habitat alterations (alien fish and dams) affect the communities.

## 2. Methods

### 2.1. Study sites

All 101 lakes in the dataset are located in the Western Alps (Table S1). The region has been severely affected by climate warming in the last decades, with a 2 °C increase in annual average air temperatures in the last 50 years, and alone 1 °C in the latest 19 years covered by the study (Harris et al., 2014; Fig. 1). Lake area ranged from 0.1 to 147 ha (mean ± SD: 6.4 ± 16.6 ha) and maximum depth from 1.0 to 68.5 m

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Left: Location of the 101 alpine lakes sampled for zooplankton in the Western Alps, shown within watershed boundaries derived from HydroSHEDS (Lehner and Grill, 2013). Most lakes are located within protected areas: Parco Nazionale Gran Paradiso (N = 55; Italy), Parco Naturale Mont Avic (N = 20; Italy), Parc National de la Vanoise (N = 5; France), Réserve Naturelle Nationale de Sixt Passy (N = 2; France), Réserve Naturelle Nationale des Aiguilles Rouges (N = 2; France), Réserve Naturelle Nationale des Contamines Montjoie (N = 1; France; see Supplementary Table S1 for details). Top right: annual mean temperatures from 1900 to 2023 averaged over eleven 0.5° latitude/longitude grid cells containing all the study sites; vertical dotted line is 2006, the starting year; thick lines, and associated shaded areas, represent mean and 95 % confidence intervals of temperature obtained by a generalized additive model, with “year” as the independent variable; data from Harris et al., 2014). Down right: a typical alpine zooplankton community with highly pigmented cladocerans (*Daphnia* gr. *longispina*, large-size, dark brown), copepods (*Arctodiaptomus alpinus*, medium size, red), and rotifers (*Hexarthra bulgarica*, small size, orange). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
(14.0 ± 15.1 m). They have an ice cover duration of 5 to 10 months per year and are located close to or above the treeline, from 1556 to 3049 m (mean: 2450 ± 310 m). Twenty-seven lakes had glaciers in their catchments, with glacier coverage up to 72.4 % (Tiberti et al., 2020). While most lakes are natural, 11 are dammed and exploited for hydro-electric energy production. All lakes were originally fishless, but in about half (N = 48), fish was introduced, including 23 with trouts (family Salmonidae: brown trout *Salmo trutta*, brook trout *Salvelinus fontinalis*, arctic charr *Salvelinus alpinus*, lake trout *Salvelinus namaycush*, rainbow trout *Oncorhynchus mykiss*), 10 with Cypriniformes, mostly minnows *Phoxinus* sp., but also vairone *Telestes muticellus* (4 lakes) and stone loach *Barbatula barbatula* (1 lake), and 14 lakes with both trout and minnows.

## 2.2. Sampling and analytical methods

A total of 643 zooplankton samples was collected from the 101 lakes over 19 years (2006–2024; 1–52 samples per lake; 17–47 samples per year). The sampling frequency and duration were unequal among lakes; thus, some lakes (N = 10) were sampled annually during the 19 year-time period, but most lakes (N = 31) were sampled for shorter periods (2–18 years) or just for one year (N = 60). Sampling was primarily conducted in late summer (August–October) to capture the late stage of seasonal succession when most crustacean species reach their biomass peak (Tiberti et al., 2013). However, many lakes were sampled multiple times during the ice-free season (2–8 times per year), including early-summer, to account for the strong seasonal dynamics of zooplankton communities (Tiberti et al., 2013). Samples were collected using vertical net hauls (from the bottom to the surface) at the deepest point of each lake, with a conical plankton net (30 or 40 cm diameter, 50 µm mesh) (refer to Supplementary Table S2 for sampling metadata). Sample fixation (70 % ethanol or Lugol's iodine solution) was promptly conducted in the field.

All samples were consistently analysed by a single taxonomist, Rocco Tiberti. Standard methods (Edmondson and Winberg, 1971) were used for zooplankton analysis. Shortly, zooplankton samples were concentrated to a known volume, and 3–5 subsamples (volumes ranging from 0.05 to 1 mL) were extracted after homogenization for counting using a wide-bore pipette. Zooplankton taxa in each subsample were identified and counted using a closed counting chamber under a stereo microscope at 40× magnification, yielding density data (number of individuals per m⁻³). Given that certain taxa, such as some common rotifer species, were sometimes significantly more abundant than others, we counted high- and low-density taxa at different dilutions. While crustaceans and rotifers were often identified to the species level, coarse taxonomic levels denoting a group of species (gr.), the genus, or family were used when specific identification was uncertain or not possible due to morphological deformations caused by the fixative agent (e.g., *Synchaeta* sp., *Ascomorpha* sp.), unresolved taxonomy, or absence of individuals bearing the identification traits (e.g., adult crustaceans). Crustaceans and rotifers were identified using taxonomic references, including Braioni and Gelmini (1983), Stella (1984), Margaritora (1985), Alonso (1996), Einsle (1996).

Biomass (dry weight per m³) was estimated for each zooplankton taxon. Length-weight relationships were used for arthropods (mostly crustaceans, but also for *Chaoborus* sp. larvae), which exhibit substantial size variations. In contrast, standard Individual Weights (SIW) were used for rotifers, which typically have more homogeneous sizes (Supplementary Table S3 for equations and SIW and other traits associated with each taxon). Consequently, the length of a representative number of zooplanktonic crustaceans (mean ± SD: 42.0 ± 57.7 specimens per taxon per sample) was measured using a micrometric lens to estimate individual dry weights. The number of measured specimens was sometimes low, but only for taxa represented by a single or few individuals in the sample, thus with a low contribution to the total biomass. The biomass estimate per cubic meter was obtained by multiplying the

density of each taxon by their average individual biomass (for arthropods) or by their Standard Individual Weight (for rotifers).

## 2.3. Data analysis

### 2.3.1. Composition of zooplankton communities

Zooplankton taxa were categorized based on their feeding mode and thermal tolerance (Supplementary Table S3). Based on their feeding mode, taxonomic units were divided between filter feeders (FF) and selective predators (SP), which were further divided into five morphofunctional feeding groups considering their maximum size and feeding strategy (Barnett et al., 2007; Obertegger et al., 2011):

- Large Filter Feeders (LFF; max length ≥ 1 mm and with the presence of filtering apparatus, e.g., herbivores of the family Daphniidae),

- Large Selective predators (LSP; max length ≥ 1 mm and with the ability to select their food items, including the predation on other zooplankton species, e.g., large omnivorous or carnivorous copepods such as *Cyclops* gr. *abyssorum*, *Arctodiaptomus alpinus*),

- Small Filter Feeders (SFF; max length < 1 mm and with the presence of filtering apparatus, e.g., herbivores family Chydoridae and rotifers with malleate, malleoramate, or ramate trophi such as *Kellicottia*, *Keratella*, *Lecane*, *Notholca*);

- Small Selective Predators of type 1 (SSP1; max length < 1 mm, herbivores with the ability to select their food items, e.g., rotifers with cardate, incudate, uncinate, or virgate trophi such as *Polyarthra* sp., *Synchaeta* sp.);

- Small Selective Predators of type 2 (SSP2; max length < 1 mm, omnivores and carnivores with the ability to select their food items, e.g., small cyclopoids and rotifers with cardate, forcipate, incudate, uncinate, or virgate trophi such as *Asplanchna* sp. or *Ploesoma* sp.).

Based on their thermal tolerance and preferences (Stella, 1984; Margaritora, 1985; Braioni and Gelmini, 1983), each taxon was assigned to either cold stenothermal or eurythermal group.

While some lakes in our dataset were consistently sampled over time, others were sampled less frequently or only once. This unbalanced sampling design reflects the dual nature of our research objectives: on one hand, to explore long-term dynamics in zooplankton communities, and on the other, to assess how lake-specific characteristics shape community structure across a broad spatial gradient. To address these aims, we employed two complementary analytical frameworks—ordination methods (NMDS, Non-metric Multidimensional Scaling) and Generalized Additive Mixed Models (GAMMs)—each applied to different subsets of the data. Analyses were performed on the full dataset to capture spatial and seasonal gradients in community structure, and on a smaller subset of 13 lakes with long-term data (>15 years) to enable a focused investigation of temporal dynamics.

The selection of covariates in both NMDS and GAMMs represents a careful equilibrium between incorporating detailed information (e.g., fish presence/absence instead of their density or geological substrate instead of pH/alkalinity) and ensuring a representative sample size. We deliberately chose to strike this balance, leveraging the maximum available detail for all sampling sites. This approach aims to capture nuanced variations in the data, while maintaining statistical robustness across a diverse and representative range of observations. During variable selection, we also considered potential multicollinearity issues stemming from the use of intercorrelated variables (e.g., lake area and depth), particularly to avoid multicollinearity issues in univariate analyses.

### 2.3.2. Multivariate community analysis

To visualise and interpret patterns of zooplankton community composition, we used NMDS (Non-metric Multidimensional Scaling) in conjunction with PERMANOVA (Permutational Multivariate Analysis of Variance). The NMDS analysis was based on Bray & Curtis' dissimilarity
matrices (Legendre and Legendre, 1998) calculated from the biomasses of each taxonomic unit. We used the metaMDS procedure in R package vegan (Oksanen et al., 2013). This procedure runs NMDS repeatedly with random starting configurations, compares results, and stops after finding twice a similar minimal stress solution (Oksanen et al., 2013); explanatory factors were fitted to the configuration by vector fitting (envfit) and included the following covariates:

- Lake-ID: a unique identifier assigned to each lake;

- Day: the Julian date (numeric variable) accounting for seasonality;

- Year: year of sampling (numeric variable);

- Elevation: in m, serving as a proxy for climatic conditions;

- Maximum depth (in meters), serving as a proxy for lake size (e.g., area and volume) and accounting for depth-related gradients such as temperature and light attenuation;

- Catchment area (B): in ha;

- Catchment-lake area ratio (BA);

- Vegetation: percentage of vegetation cover in the catchment from the European Corine Land Cover 2012 database;

- Glacier: percentage of glacial cover in the catchment, derived from the European Corine Land Cover 2012 database and adjusted using satellite imagery to provide a more accurate quantification of glacier coverage around the time of sampling;

- Geology (binary): catchments were categorized as CR if they contained at least 10 % carbonate rocks (i.e., capable of buffering acid deposition) or as AR if dominated by acidic rocks (>90 %) and at risk of acidification (Tiberti et al., 2019a);

- Salmonids (binary): presence/absence of salmonids, no new introductions occurred within the sampling periods of each lake, but fish were eradicated from 4 lakes between 2013 and 2016;

- Cypriniformes (binary): presence/absence of Cypriniformes (mainly minnows), no new introductions occurred within the sampling periods of each lake;

- Dam (binary): presence/absence of a dam.

Then, we used PERMANOVA with 999 permutations based on Bray-Curtis distances to test the effects of the same explanatory variables on community composition.

To complement this main analysis, we performed NMDS and PERMANOVA on the subset of 13 lakes for which long-term monitoring data (spanning >15 years) are available. These analyses were conducted using the same methods and covariates described above, except Glacier, Dam and Cypriniformes as we do not had lakes with a dam or cypriniformes in this subset.

### 2.3.3. Time series and cross-lake analyses using generalized additive mixed models (GAMMs)

We applied two distinct series of Generalized Additive Mixed Models (GAMMs) implemented in the mgcv package (Wood and Wood, 2015) within the R statistical environment (R Core Team, 2024) respectively to explore long-term zooplankton dynamics and assess lake-specific characteristics' influence on zooplankton. The first set of models focused on the subset of 13 lakes (N = 13) with sampling spanning at least 15 years, allowing us to investigate long-term trends in zooplankton biomass dynamics. The second set of models included all available samples to maximize lake coverage. We checked all models for multicollinearity using the Variance Inflation Factor (VIF; using vif.gam function from the mgcv helper package (Clifford and Salmon, 2019), setting a threshold of five. Biomass data were log10 + 1 transformed to approximate normality and modelled using a Gaussian distribution.

In the first model set, we investigated biomass's long-term dynamics in functional feeding groups, thermal-tolerance groups, and total zooplankton biomass across 13 lakes. We fitted alternative models incorporating different residual autocorrelation structures—temporal, spatial (corExp(form = ~ Latitude + Longitude | Lake-ID)), and non-e—and compared model fit using Akaike Information Criterion AIC).

While all models yielded nearly identical fixed-effect estimates, the model incorporating temporal autocorrelation consistently provided a better balance between model fit and model complexity, with AIC values typically 3–29 points lower (mean ± SD: 15.7 ± 10.6) than the worst fitting model, i.e., the model including spatial autocorrelation with AIC values 1–2 points higher than the model without any autocorrelation structure. Given the minimal differences in fixed-effect estimates and the consistent improvement in model fit, we chose to present the results from the model with temporal autocorrelation. We therefore included an exponential correlation structure (correlation = corExp(form = ~ cyclic_time | Lake-ID)) to account for temporal autocorrelation. This structure was based on the cyclic_time variable, which was specifically created to represent continuous time across multiple years. The cyclic_time variable integrates both the day of the year (to capture seasonality) and the year difference (to reflect the passage of time over multiple years). By combining these two components, cyclic_time enables the model to account for the recurring annual cycle, while it also ensures that time is treated as a continuous, non-resetting variable across years. This structure accounts for the expectation that zooplankton communities in the same lake will be more similar in samples collected during the same season across years, compared with those collected in different seasons within the same year. This is because community composition and species abundances typically follow recurring seasonal patterns. In this first set of models, the year was added as a numeric covariate, and the number of covariates was reduced compared to those used in NMDS and PERMANOVA, as none of the lakes with long-term data had glaciers, Cypriniformes or dams and to streamline the model structure. Fixed effects included date of sampling, lake elevation and depth, geology, and presence/absence of salmonids, while Lake identity (Lake-ID) was fitted as a random term to avoid pseudoreplication. To clarify the main drivers of a particularly interesting decreasing trend in cold stenothermals, the same model was applied to the biomass of the dominant taxa of this group.

The second set of models aimed to provide a more detailed assessment of spatial and seasonal variation in both zooplankton groups and total zooplankton biomass. These models included all available lakes and samples, with both the year of sampling and lake identity (Lake-ID) specified as additive random effects to account for inter-annual and lake-level variability. The explanatory variables comprised all covariates used in the NMDS and PERMANOVA analyses, along with the interaction between salmonids and Cypriniformes. Since the year of sampling was already modelled as a random effect, incorporating a temporal autocorrelation structure was deemed unnecessary. Inclusion of a spatial autocorrelation structure (corExp(form = ~ Latitude + Longitude | Lake-ID)) produced nearly identical fixed-effect estimates but resulted in slightly higher AIC values (by 1–2 points) compared to the model without any autocorrelation. Accordingly, we present the results from the model without autocorrelation structure.

In all models, the date of sampling, elevation, and depth were initially modelled using smooth terms. Smooth terms were retained in the final model only if the non-linear relationship they captured provided a significantly better fit than linear terms. The goodness of fit was assessed using the marginal R² (mR²), which reflects the variance explained by fixed effects, and the conditional R² (cR²), which reflects the variance explained by both the fixed and random effects. We checked residuals to ensure the model adequately captured the data, with no patterns or trends remaining.

The first set of models is used to discuss long-term trends in zooplankton, considering potentially important variables. The second set of models is specifically used to discuss the effects of different covariates and threats on zooplankton. Due to the testing of multiple models, we conservatively set the significance threshold at 0.01.
# 3. Results

## 3.1. Patterns in community composition

The list of taxonomic units included 54 species and higher-ranked taxa (see Table S3 for a list of taxonomic units and their characteristics). Taxonomic richness (S) was typically low (mean ± SD: 5.5 ± 2.0 taxa per sample; range: 0–14), and evenness in taxa abundances (E) was moderate (0.46 ± 0.23), indicating the frequent presence of one or few dominant species in the samples, and resulting in very low to moderate values of the Shannon-Wiener index (H'; 0.75 ± 0.39). The most abundant taxa in the communities were rotifers, although copepods and cladocerans were abundant or dominant at certain sampling sites, in particular, in lakes without fish and towards the end of the summer season (see GBIF dataset; Tiberti, 2025). Planktonic insects were practically absent, with only a single observation of *Chaoborus* sp. (Diptera). Among the very common taxa were rotifers (e.g., *Keratella* gr. quadrata, *Keratella cochlearis*, *Polyarthra* sp.), copepods (*Cyclops* gr. abyssorum, *Arctodiaptomus alpinus*), and cladocerans (*Daphnia* gr. longispina). Some zooplankton species commonly found in alpine lakes (e.g., *Notholca* sp., *Alona* sp.) seldom attained appreciable densities, while some less frequent species (e.g., *Kellicottia longispina*, *Asplanchna priodonta*), when present, could reach considerable densities or become dominant in the community. Rotifers numerically dominate zooplankton communities, but crustaceans, due to their larger size, account for the majority of

zooplankton biomass.

The NMDS ordination plots illustrate how the taxonomic composition of the zooplankton community changed together with the ordination of explanatory factors (Fig. 2). Among the environmental variables, lake identity accounted for a substantial proportion of the variability in

<caption>**Table 1**</caption>

Results of PERMANOVA tests (ordinated by decreasing R² associated to each explanatory factor) with taxonomic composition of alpine lakes zooplankton communities as the response performed on Bray-Curtis dissimilarity matrix.

<table><thead><tr><th></th><th>Df</th><th>R<sup>2</sup></th><th>F</th><th>p</th></tr></thead><tbody><tr><td>Lake-ID</td><td>90</td><td>0.275</td><td>2.98</td><td>&lt;0.001</td></tr><tr><td>Day</td><td>1</td><td>0.039</td><td>38.16</td><td>&lt;0.001</td></tr><tr><td>Vegetation</td><td>1</td><td>0.027</td><td>26.65</td><td>&lt;0.001</td></tr><tr><td>Elevation</td><td>1</td><td>0.026</td><td>25.05</td><td>&lt;0.001</td></tr><tr><td>Salmonids</td><td>1</td><td>0.016</td><td>15.64</td><td>&lt;0.001</td></tr><tr><td>Year</td><td>1</td><td>0.013</td><td>12.25</td><td>&lt;0.001</td></tr><tr><td>Depth</td><td>1</td><td>0.013</td><td>12.19</td><td>&lt;0.001</td></tr><tr><td>Cypriniformes</td><td>1</td><td>0.009</td><td>9.18</td><td>&lt;0.001</td></tr><tr><td>Geology</td><td>1</td><td>0.008</td><td>7.87</td><td>&lt;0.001</td></tr><tr><td>Glaciers</td><td>1</td><td>0.006</td><td>6.03</td><td>&lt;0.001</td></tr><tr><td>Catchment/lake area ratio</td><td>1</td><td>0.005</td><td>4.57</td><td>&lt;0.001</td></tr><tr><td>Catchment area</td><td>1</td><td>0.004</td><td>4.27</td><td>&lt;0.001</td></tr><tr><td>Dam</td><td>1</td><td>0.004</td><td>3.99</td><td>&lt;0.001</td></tr><tr><td>Residuals</td><td>540</td><td>0.554</td><td></td><td></td></tr><tr><td>Total</td><td>642</td><td>1.000</td><td></td><td></td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** NMDS ordination plots based on count and biomass data of zooplankton communities across 101 alpinelakes in the Western Alps. Elevational gradients are visually emphasized through colour variations; “sal” and “cyp” indicate the presence of salmonids and Cypriniformes respectively; “b” and “ba” are the catchment area and catchment:lake area ratio; “veg” and “gla” indicate the percentage of vegetation and glacier cover in the catchment. The stress value of the NMDS was 0.172. An explanation of the codes used for each taxonomic unit is reported in Table S3.</figcaption></figure>
community data (see R² in Table 1; note that, because of the large sample size, even minor influences of explanatory factors on community composition attain statistical significance). Interestingly, year and elevation had an inverse correlation with NMDS axis 1, (Fig. 2), which anticipates the results of GAMMs (see next paragraph) and suggests the existence of a temporal trend where communities observed at higher elevations increasingly resemble those usually found at lower elevations. This is also evidenced by the clear alignment of samples along the elevation gradient within the multidimensional space (Fig. 2). Seasonality and the percentage of vegetation cover in the catchments were other important natural factors influencing community composition (Table 1). Among anthropogenic factors, the presence of fish, particularly salmonids, emerged as an important factor shaping community composition. While glaciers are known to have large negative effects on most zooplankton taxa and groups, their contribution to overall community variance appeared relatively modest (Table 1), but this is likely due to the relatively small number of lakes with glaciers in their catchment. The same could be true for the relatively few lakes with dams and the scarce contribution of dam presence on overall variability in community data. Geology, catchment area, and catchment:lake area ratio had limited effects on zooplankton communities, consistent with GAMM outcomes.

The NMDS ordinations based on the subset of 13 lakes with long-term data were highly consistent with those obtained from the full dataset. In particular, the inverse correlation between year and elevation with NMDS axis 1 was retained. Given this strong concordance, we included the results of the subset analysis as Supplementary Material S4 to avoid redundancy in the main text while still providing additional support for the robustness of our findings.

## 3.2. Long-term dynamics

Cold stenothermals exhibited a non-linear decline over the study period (estimated degrees of freedom - edf = 1.66, F = 10.27; *p* < 0.001; Fig. 3). This trend, which deviates slightly from a purely linear decrease, was predominantly driven by *Arctodiaptomus alpinus*, which accounted for 97.8 % of the cold stenothermal biomass and which showed a significant linear decline from 2006 to 2024 (β = -0.04; *t* = -5.23; *p* < 0.001). Although a single species primarily drove the decline in cold stenothermal species, rotifers of the genus *Notholca* and the large cladoceran *Daphnia pulicaria*—the second and third most common cold

stenothermal taxa—also declined (*Notholca*: edf = 1.08, F = 5.88, *p* = 0.017; *Daphnia pulicaria*: edf = 1.96, F = 17.2, *p* < 0.001; Fig. 3). Notably, three out of five known populations of *Daphnia pulicaria* disappeared during the study. In contrast, eurythermal zooplankton showed no significant relationship with year over the same period.

The year did not significantly influence filter feeders or large filter feeders, but it had significant complex non-linear effects on total zooplankton biomass (edf = 4.27; F = 4.82, *p* < 0.001), selective predators (edf = 5.24; F = 3.36, *p* < 0.01), large selective predators (edf = 4.52; F = 3.76, *p* < 0.01), and small selective predators of type 1 (edf = 5.68; F = 4.13, *p* < 0.001). However, these non-linear fluctuations did not exhibit a consistent long-term trend (Fig. 4). In contrast, small filter feeders showed a significant linear decline (β = -0.02, *t* = -2.98, *p* < 0.01), as did small selective predators of type 2 (β = -0.02, *t* = -3.45, *p* < 0.001). For further details on the models and the influence of covariates other than year, please refer to Table S5.

## 3.3. Cross-lake variability in zooplankton groups

According to the results from the second set of GAMMs (including all samples), the different zooplankton groups were influenced by a complex set of environmental variables (Fig. 5 and Table S5 for detailed results). Marginal R² values (0.051–0.424) highlight the contribution of fixed effects, while conditional R² values (0.53–0.75) show that random effects, particularly Lake-ID, capture a substantial portion of the variability. This underscores the importance of accounting for hierarchical structures in the data.

Elevation was a key factor influencing the biomass of thermal groups and filter feeders. It positively affected cold stenothermal species, while eurythermals and filter feeders showed a non-linear decline with elevation. Among catchment characteristics, vegetation cover positively influenced all filter feeders, whereas glacial cover had a strong negative impact on most groups. Depth, catchment area, catchment-to-lake area ratio, and geology did not have a significant effect on total zooplankton biomass or on the considered zooplankton groups. Regarding local human disturbances, both fish categories exerted adverse effects on large filter feeders: Cypriniformes impacted the entire filter feeder compartment, while salmonids showed positive associations with groups dominated by smaller-bodied taxa, such as SFF and SSP2. The Salmonids:Cypriniformes interaction did not have a significant effect. Damming had limited effects on zooplankton groups, except for small

<figure><img src="image_6.png" /></figure>

**Fig. 3.** GAMM results about the effects of year on the biomass of cold stenothermal zooplankton group and taxa, while accounting for other key variables. Left panel: long-term trends in average annual observed vs. predicted biomasses (±95 % CI) of cold stenothermal zooplankton (97.8 % of the group is dominated by *Arctodiaptomus alpinus*). Right panels: Smooth plots illustrating the relationship between year and the biomass of cold stenothermal zooplankton, as well as two common cold stenothermal taxa. Models based on 475 samples collected from 13 lakes.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Smooth plots illustrating the trends in the biomass of the total zooplankton biomass and different functional groups over time. Results from GAMMs based on 475 samples collected from 13 lakes.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Graphical summary of GAMM model outputs showing the factors influencing functional, thermal, and habitat preference groups, as well as total biomass in zooplankton communities from the Western Alps. Upward (↑) and downward (↓) arrows denote significant positive and negative linear relationships, respectively; the wave symbol (~) indicates significant non-linear relationships; 'NS' indicates non-significant relationships. s.Geology-CR: carbonated rocks (>10 % cover in the catchment); Geology-AR: acidic rocks (>90 % cover in the catchment). Goodness of fit is assessed using the marginal R² (mR²), which reflects the variance explained by fixed effects, and the conditional R² (cR²), which reflects the variance explained by both fixed and random effects. Models based on 643 samples from 101 lakes. See Table S5, Sheet 2 ("Cross-lake analysis") for detailed model outputs and plots of the smooth terms. Most zooplankton groups exhibited non-linear seasonal dynamics, with a summer growth phase and a fall decline. The total zooplankton community generally reached peak biomass at the end of August (day of the year ≈240; Fig. 6). While this overall trend was shared across several groups (Table S5), differences existed. For instance, alpine cold stenothermals tended to reach their peak earlier than montane eurythermals (Table S5). In contrast, groups dominated by smaller, short-lived taxa lacked distinct seasonal patterns.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Smooth plots illustrating the seasonal dynamics of total zooplankton biomass. Results from GAMMs based on 643 samples collected from 101 lakes. For detailed seasonal dynamics of zooplankton sub-groups, refer to Table S5, Sheet 2 ("Cross-lake analysis").</figcaption></figure>

filter feeders, which were negatively influenced.

# 4. Discussion

## 4.1. Long-term dynamics

The response of zooplankton communities and the observed multi-annual trends (Figs. 2 and 3) aligned with the effects expected from climate warming (MacLennan and Vinebrooke, 2016; Khaliq et al., 2024) and with predictions from several mesocosm experiments (Holzapfel and Vinebrooke, 2005; Loewen and Vinebrooke, 2016; Strecker et al., 2004), as well as field (Redmond et al., 2018) and paleolimnological (Catalan et al., 2013; Nevalainen et al., 2014) studies. Specifically, alpine, cold stenothermal taxa (e.g., *Arctodiaptomus alpinus*, *Notholca* sp., *Daphnia pulicaria*) are the most prone to decline, potentially being replaced by montane eurythermal species (*Holzapfel* and *Vinebrooke*, 2005). While our study aligns with these predictions, it is novel in providing long-term field evidence supporting these trends. Similar changes are known as thermophilisation and commonly associated with climate warming (Khaliq et al., 2024), which is particularly severe in the study region and in the last few decades (Fig. 1; Harris et al., 2014).

Collectively, cold stenothermal taxa declined, and overall communities progressively resembled those typically observed at lower elevations. Specialist alpine taxa may decrease due to a prolonged degradation of their thermal niche (e.g., increased water temperatures, or advanced thawing dates, and longer ice-free periods; Loria et al., 2020), replacement by montane eurythermal species, displacement by new colonisers from lower elevations (Holzapfel and Vinebrooke, 2005), or alteration of their trophic niche (Morales-Baquero et al., 2006). The species pool remained largely unchanged throughout our study, with few new colonisations (notably a few large populations of *Kellicottia longispina* established during the study period). Community changes were primarily due to shifts in the proportions of resident species or their disappearance, not the arrival of new species from lower altitudes. These dynamics may reflect colonization challenges, driven by strong founder effects or limited dispersal capacity in isolated alpine lakes (Donald et al., 2001; Ventura et al., 2014).

Despite the uncertainties associated with working in uncontrolled natural conditions, our results provide a relevant case study that could contribute significantly to the scientific debate on zooplankton's response to climate change. Indeed, a recent macroecological meta-analysis of long-term community responses to climate warming by Khaliq et al. (2024) suggests that zooplankton may deviate from the general trend of thermophilisation—that is, the increased prevalence of warm-affinity species in ecosystems—in response to climate warming.

The study indicates that cold-adapted zooplankton species may not decline as expected and, in some cases, could even benefit from changing climatic conditions. The reasons for this unexpected response are not understood at the moment, but the authors argued that this advantage could be temporary or seasonal (e.g., driven by increased primary productivity during winter in many lakes studied), constrained to brief periods of the year or the initial phases of zooplankton communities' adaptation to climate change (Khaliq et al., 2024). Compared to Khaliq et al. (2024), our study provides a contrasting, less unexpected perspective, which aligns zooplankton to the general thermophilisation trends observed for other taxa. The dissonance between available empirical data, and the absence of consolidated empirical evidence suggests that the response of freshwater zooplankton to recent climate change remains a poorly understood subject, despite the potential insights that can be gained through paleolimnological studies of lakes (Catalan et al., 2013). This is primarily due to the difficulty of distinguishing between global and local stressors and because of the scarcity of long-term data series from reference ecosystems, such as remote, relatively undisturbed lakes (MacLennan and Vinebrooke, 2016; Brancelj, 2021) used in the current study.

Although the dynamics of stenothermal species and a pronounced regional warming trend suggest climate warming is the most likely explanation for the observed thermophilisation trends, other concurrent factors cannot be ruled out. Additionally, we observe fluctuations and declining trends, particularly in small filter feeders and selective predators, for which a clear explanation remains elusive. These changes may be driven by complex internal community dynamics (e.g., interactions among zooplankton groups), unexplored interactive effects (e.g., between temperature increase and alien fish; Holzapfel and Vinebrooke, 2005), or other unexamined variables. The temperature increase is just one component of the broader, multifaceted effects of global and regional change, including the spread of long-range pollutants, emergent pollutants (Machate et al., 2022), historical and ongoing acid deposition, and changes in water chemistry (Sommaruga-Wögrath et al., 1997), all of which may collectively contribute to the observed trends in zooplankton communities.

## 4.2. Zooplankton community: Effects of seasonality and lake and catchment characteristics

Our taxonomic list included 54 taxonomic units, but several taxa were cautionary grouped into species groups or higher taxa. Compiling a taxonomic list requires caution and some inevitable simplifications, because the taxonomy of many taxa is yet to be resolved with unclear levels of hidden biodiversity (Fontaneto et al., 2009). The list encompassed most taxa that are commonly reported from alpine lakes located in the Alps (Giussani et al., 1986; Brancelj, 2021). The composition of sampled zooplankton communities was characterized by an admixture of species with diverse thermal tolerances and specialization levels. Alpine specialists—cold stenothermal taxa adapted to harsh high-elevation climates, such as *Arctodiaptomus alpinus* and *Daphnia pulicaria*—account for 22.1 % of total zooplankton biomass. However, this contribution may be underestimated, as some alpine specialists are likely included within broader species groups or higher taxonomic levels, for example, within the *Keratella* gr. quadrata, some morphotypes could have been assigned to the cold stenothermal *Keratella hiemalis* (Braioni and Gelmini, 1983). Grouping species into taxonomic and functional categories may have contributed to the lack of spatial autocorrelation observed in our analyses, as it reduces the likelihood of detecting species-specific biogeographic patterns. Additionally, the ecological characteristics of our study organisms—primarily small-bodied taxa with high dispersal capabilities and limited biogeographic structuring at the scale of our study area—may further explain this outcome.

The range of species richness in freshwater zooplankton communities can vary widely depending on environmental conditions; in high
mountain aquatic habitats, characterized by harsh climatic conditions and glacial influence, diversity values are typically low (Miracle, 1978; Giussani et al., 1986). With 54 identified taxonomic units and less than six species per sample on average, our study aligns with findings reported for other areas in the Alps (Tonolli and Tonolli, 1951; Giussani et al., 1986; Tolotti et al., 2006; Kernan et al., 2009; Brancelj, 2021), as well as for various European and non-European mountain regions (Miracle, 1978; Knapp et al., 2001). Communities characterized by low diversity values are recognized for their elevated sensitivity to environmental changes and potential vulnerability to disturbances from both natural and anthropogenic stressors (Redmond et al., 2018). This raises concerns regarding their stability and adaptive capacity in the face of anthropogenic impacts.

As expected, most zooplankton groups have strong seasonal patterns in response to thawing and the subsequent significant changes in environmental conditions during summer (Manca et al., 1999). Some short life-cycle species, especially rotifers and small crustaceans, can take advantage of resources made available when larger crustacean competitors have not yet developed and thrive at the beginning of the summer season or at intermediate periods (Tiberti et al., 2013). For the same reasons, groups dominated by smaller taxa often lack clear seasonal trends. This is likely due to their short life cycles, enabling them to exploit brief favourable periods, reducing reliance on longer seasonal changes. Unless fish-selective predation prevents their development (Knapp et al., 2001), large eurythermal crustaceans often dominate the late-summer community, especially *Daphnia gr. longispina*.

Elevation (as a proxy for local climatic conditions) and glaciers emerge as important forces shaping zooplankton communities. Most zooplankton groups sharply decrease as elevation exceeds certain levels, except for the cold stenothermal group, which is positively associated with elevation and was found up to the highest elevations (>3000 m). Colder temperatures and various degrees of glacial turbidity characterize glacier-fed lakes. Consistent with existing literature (Slemmons et al., 2013; Sommaruga, 2015; Tiberti et al., 2020), glacial runoff in our study area creates highly selective abiotic conditions that constrain the development of planktonic communities, excluding numerous taxa and entire functional groups, until it almost completely wipes out zooplankton communities in the most turbid lakes. Whether the impoverishment effect on zooplankton communities is due to low temperatures or suspended mineral particles is not entirely clear, but both factors contribute to taxa exclusion (Koenings et al., 1990; Tiberti et al., 2020). The retreat of alpine glaciers will significantly alter the number and characteristics of high-altitude lakes and their communities. As glaciers disappear, some new lakes may appear and many others may become clearer and warmer, potentially offering favourable conditions for zooplankton seeking refuge from global warming. However, this implies the loss of many unique turbid ecosystems. In addition, the role of new lakes as refuges will depend on protecting lakes from local impacts (Tiberti et al., 2020; see next paragraphs).

Other characteristics of lakes and catchments had uncleared and less generalizable effects on zooplankton. Vegetation cover, for example, is positively associated with all filter feeders, which could benefit from the relatively larger inputs of allochthonous organic matter provided by vegetated soils, rather than from barren alpine areas. However, these results, rather than standing out for significant influences, highlight the opposite outcome, that is a general weak effect of several variables that are often considered important for zooplankton (Fig. 5). This overall weak effect could be attributed to the fact that these variables are relatively crude proxies of other important factors. For example, we used catchment geology as the major driver of water chemistry in alpine lakes to differentiate between lakes at risk of acidification or not (with some carbonate rocks in their catchment); while its limited effects on zooplankton communities may seem surprising, this was a rather simplified approximation of acidification risk. The absence of substantial effect of geology on zooplankton could suggest that most lakes, even those on acidic geological setups, are sufficiently protected from acute

acidification, and the pH variation does not commonly reach levels that are known to impact zooplankton, as actually observed in our study area and reported by Tiberti et al. (2019a). In addition, catchment geology is essentially linked to variables associated with acidification processes (e. g., pH, alkalinity, ionic content), while it does not correlate with other chemical factors such as nutrients. While nutrient content is expected to have significant bottom-up effects on lakes' food webs and zooplankton, the measurement of nutrients was limited to a subset of sampling occasions, and we therefore chose to prioritize the number of samples and study sites rather than integrating nutrient availability.

### 4.3. Threats and implications for conservation

Our results highlight the impacts of prevalent local threats to alpine lake biodiversity, such as introduced fish and to a lesser extent damming. Additionally, the study quantifies the influence of glaciers on the zooplankton communities, with significant implications for conservation, particularly considering the rapid vanishing of alpine glaciers. Finally, considering the observed long-term trends, our results can broader conservation implications for other ecosystems.

The effects of lake damming appear limited and primarily impacted small filter feeders, dominated by small littoral cladoceran species associated with submerged vegetation (e.g., *Alona* sp.) and by pelagic rotifers (e.g., *Keratella*, *Kellicottia*). Both groups may be affected by water level fluctuations caused by dam operations, which can disrupt littoral habitats (Carmignani and Roy, 2017), dilute planktonic communities during rising water levels, or diminish them during drawdowns. Additionally, all dammed lakes in this study also contained introduced fish, and pelagic zooplankton may not be the most reliable indicator of the impacts of damming and associated abnormal water level fluctuations, which could be better assessed by focusing on littoral habitats and communities. Therefore, lakes with a dam do not host pristine zooplankton communities, but rather ones already influenced by fish, and should not be considered equivalent to pristine lakes. Importantly, our study compared dammed and natural lakes rather than monitoring changes following dam construction, which could yield different results; for instance, increasing the depth of small lakes could prolong water retention times and support greater zooplankton abundances (Paterson et al., 2019). Overall, the observed limited effects on pelagic zooplankton may underestimate the broader ecological consequences of lake damming, but we did not find studies specifically addressing the effects of lake damming on alpine zooplankton communities, to better support our findings and interpretations.

By contrast, the impact of introduced fish—especially salmonids—on zooplankton communities in alpine lakes is well documented in the literature. Our findings align with previous studies, which have shown that fish are present in a substantial proportion of alpine lakes (48% in the present study) and in almost all large lakes (76% of lakes >5 ha and 88% of lakes >10 ha). Fish presence is associated with decreasing or disappearing large-sized zooplankton (Bradford et al., 1998; Knapp et al., 2001). The fact that most large lakes have introduced fish highlights the potential of small lakes to serve as a refuge for fish-sensitive species (MacLennan and Vinebrooke, 2016). However, large lakes, unlike small lakes and ponds, offer unique depth microhabitats, high volume, and thermal stability, which should protect them from drought, heatwaves, and temperature increase, questioning the ability of small lakes and ponds to preserve zooplankton biodiversity under the combined pressure of these predicted effects of climate warming and fish spread.

Small fish species, such as minnows (*Phoxinus* sp.), have been either accidentally or deliberately released to serve as forage or live bait for game species across European mountain ranges (Miró and Ventura, 2015). While minnows have not yet emerged as a widespread threat in mountain ranges outside Europe, our findings suggest their impacts may be similar or more severe to those of salmonids as observed by Osorio et al. (2022) for littoral macroinvertebrates. In fact, the presence of
cyprinids affects not only large filter feeders but also the entire filter feeder compartment, likely due to the lack of compensatory mechanisms between small and large filter feeders, which can result in increased phytoplankton proliferation. Unlike salmonids, which trigger competitive release mechanisms favouring small-sized zooplankton (Knapp et al., 2001; Tiberti et al., 2019b; Fig. 5), such mechanisms are not evident with cyprinids. The spread of introduced minnows raises particular concerns due to illicit introductions by anglers, their increasing prevalence in Europe (Ventura et al., 2017), their strong impacts and adaptability (Osorio et al., 2022), and their challenging eradication (Schabetsberger et al., 2023). Our results thus highlight the potential risks associated with live baiting, urging increased awareness before this issue becomes more widespread in other mountain ranges.

The widespread introduction of fish in alpine lakes represents a challenging issue for conservation, which should be addressed through appropriate management and conservation measures and conservation actions (Knapp et al., 2001; Stuparyk and Vinebrooke, 2024). Preserving fishless lakes, particularly the few large lakes that are still fishless, should become a conservation priority. Such measures and actions have already been implemented in some protected areas in the present study. For example, the Gran Paradiso National Park, Mont Avic Natural Park, and Vanoise National Park have severe restrictions concerning fish introductions and fishing. In addition, the former two protected areas are promoting habitat restoration through fish eradication, which has been completed in four lakes (Tiberti et al., 2019b), while a further 12 lakes are being treated for the same purpose within the project LIFE RESQUE ALPYR (LIFE20 NAT/ES/000369; https://liferesquealpyr.eu/).

As mountain glaciers worldwide continue to retreat, and low-elevation glaciers are anticipated to vanish by the end of the century (Sommer et al., 2020), the biota within glacier-fed lakes will face unprecedented changes and pressures. Many formerly glacier-fed lakes will lose their hydrological connection with the glacier and transition to a clear water state typical of high mountain oligotrophic lakes unaffected by glacial runoff. This shift can significantly transform the abiotic conditions of alpine lakes, such as increased sunlight penetration and water temperature (Slemmons et al., 2013; Sommaruga, 2015). Over time, more intricate food webs may develop in these lakes, supporting a more diverse zooplankton community; initially, these communities may be dominated by stenothermal taxa, offering an example of how climate warming could temporarily benefit stenothermal taxa, similarly to what inferred by Khaliq et al. (2024). However, taxa adapted to turbid conditions might be adversely affected, as indicated by early signs of decreased prokaryotic diversity (Peter and Sommaruga, 2016) and this argues for continuing to study these lakes which are no longer being fed by glaciers. The simplicity of food webs and low zooplankton diversity in glacier-fed lakes highlight unique ecological conditions and should not be equated with low conservation value. The biodiversity of communities in turbid glacial lakes remains mainly unknown (Peter and Sommaruga, 2016), and their conservation value, marked by the presence of specialized taxa and communities, is yet to be fully understood. Nevertheless, the emergence of new proglacial and ice-contact lakes in depressions no longer covered by ice may offer a refuge for specialist taxa threatened by the disappearance of glaciers and increased temperatures. However, the potential of these new glacier-fed lakes to compensate for biodiversity loss resulting from the shift of turbid lakes to clear conditions remains uncertain, especially if all glaciers disappear from a particular region. Unless an effective global strategy halts and reverses climate warming in the coming decades, substantial glaciers could be lost over the long period (Sommer et al., 2020), leading to the complete loss of glacial aquatic habitats across large regions and decreasing many specialist taxa to extinction.

Alpine lakes exemplify a dual nature that makes them both ecologically significant and highly vulnerable. The relatively simple structure of their food webs, low functional redundancy, and specialized taxa render them uniquely sensitive to local and global pressures, such as climate change. This fragility is evident in the rapid decline of cold

stenothermal taxa, including rare species like the alpine pigmented *Daphnia pulicaria*, which has disappeared in several lakes. Such losses highlight their role as sentinels of environmental change, providing early warnings of biodiversity decline and ecosystem disruption. However, this same simplicity also distinguishes these ecosystems from more complex ones, limiting the extent to which findings from alpine lakes can be generalized. Unlike systems with higher trophic diversity and greater functional redundancy, alpine lakes may exhibit exaggerated responses to environmental pressures. Thus, conservation strategies should be cautious in extrapolating these dynamics to more complex ecosystems (e.g., lowland lakes or even salt waters), recognizing the unique ecological context of alpine lakes.

## 5. Conclusions

The broader relevance of our study is that long-term changes are already occurring in fundamental processes of aquatic habitats (i.e., secondary production), with possible effects on several vital ecosystem services provided by water resources. The zooplankton communities inhabiting the analysed alpine lakes and their long-term dynamics, were significantly influenced by a combination of local conditions and both local and global threats. In particular, the observed decline of cold stenothermals suggests a likely influence of rising regional temperatures.

Even if climate change necessitates broad global policies, conservation measures can be applied locally to protect residual biodiversity and increase its resilience in the face of local and global threats. Conservation efforts should prioritize the protection of still pristine lakes – those devoid of fish and free from dam interference. This emphasis should particularly extend to large pristine lakes and newly formed lakes after deglaciation, given their essential role in sustaining diverse zooplankton populations and providing unique cold habitats and refuges for aquatic biodiversity. Subsequently, it is necessary to extend the conservation measures that have proven effective in reducing local impacts on zooplankton, such as stricter regulations for fish introduction, fishing, and water exploitation. Protected areas will be key in this regard. Finally, impacted lakes can be recovered through conservation actions, for example, by alien fish eradication (Tiberti et al., 2019b). The recovery of mountain aquatic habitats can potentially increase the resilience of aquatic biodiversity against increased temperature, providing new pristine habitats for colonization.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.biocon.2025.111222.

## CRediT authorship contribution statement

**Rocco Tiberti:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Flavia Dory:** Writing – review & editing, Data curation.
**Florent Arthaud:** Writing – review & editing, Writing – original draft, Project administration, Investigation, Conceptualization.
**Vincent Augé:** Writing – review & editing, Writing – original draft, Investigation.
**Carole Birk:** Writing – review & editing, Writing – original draft, Project administration, Investigation.
**Laurent Cavalli:** Writing – review & editing, Writing – original draft, Investigation.
**Diego Fontaneto:** Writing – review & editing, Writing – original draft, Data curation, Conceptualization.
**Raphaelle Napoleoni:** Writing – review & editing, Writing – original draft, Project administration, Funding acquisition.
**Marie Elodie Perga:** Writing – review & editing, Writing – original draft, Investigation, Conceptualization.
**Ibor Sabás:** Writing – review & editing, Writing – original draft, Investigation.
**Clotilde Sagot:** Writing – review & editing, Writing – original draft, Investigation.
**Ruben Sommaruga:** Writing – review & editing, Writing – original draft, Project administration, Funding acquisition, Conceptualization.
## Author contributions

RT, RN, and CB developed the original idea for the study. The conceptualization of the paper was carried out by RT, FD, FA, DF, RN, CB, MEP, and RS. RT led the writing of the original draft of the manuscript. Data collection and curation were performed by RT, FA, VA, CB, LC, and CS. Statistical analyses were conducted by RT and FD. All authors contributed to reviewing and editing the manuscript, provided expert feedback, and approved the final version for submission.

## Declaration of competing interest

The authors declare no competing interests.

## Acknowledgements

We thank all the collaborators and students who helped to collect the samples and all protected areas and authorities that sustained our research over time. We are particularly grateful to Giuseppe Bogliani, Bruno Bassano, Achaz von Hardenberg, Massimo Bocca, Daniele Stellin. Funding for long term monitoring was provided by the projects PITEM Biodiv'ALP - PS 5217 "Geobiodiv" (2020-2022), LIFE BIOAQUAE (LIFE11 BIO/IT/000020; 2013-2017), LIFE RESQUE ALPYR (LIFE20 NAT/ES/000369; 2022), Biodiversa FISHME (BiodivRestor-280; 2022), FP7 ACQWA Project (Grant Agreement No. 212250; 2008-2010). Further funding and logistic support were provided by the Mont Avic Natural Park, Gran Paradiso National Park, University of Pavia, University of Calabria, Mercantour National Park, Ecrins National Park, Vanoise National Park, ASTER Conservatoire d'espaces naturels de Haute-Savoie, Reseau Lacs sentinelles www.lacs-sentinelles.org.

## Data availability

Zooplankton count and biomass data is available as a GBIF: Tiberti, R., 2025. Records of Zooplankton from 101 Alpine Lakes in the Western Alps. Occurrence dataset, Consiglio Nazionale delle Ricerche - Istituto di Ricerca sulle Acque. https://doi.org/10.15468/uesgz5 accessed via GBIF.org on 2025-04-16.

## References

* Alonso, M., 1996. Crustacea, branchiopoda, vol. 7. Editorial CSIC-CSIC Press.

* Barnett, A.J., Finlay, K., Beisner, B.E., 2007. Functional diversity of crustacean zooplankton communities: towards a trait-based classification. Freshw. Biol. 52, 796-813.

* Birck, C., Epaillard, I., Leccia, F., Crassous, C., Morand, A., Miaud, C., Bertrand, C., Cavalli, L., Jacquet, S., Moullec, P., Bonnet, R., 2013. Sentinel lakes: a network for the study and management of mountain lakes in the French Alps and in Corsica. Eco. Mont. Journal on Protected Mountain Areas Research 5, 63-69.

* Bradford, D.F., Cooper, S.D.C., Jenkins, T.M., Kratz, K., Sarnelle, O., Brown, A.D., 1998. Influences of natural acidity and introduced fish on faunal assemblages in California alpine lakes. Can. J. Fish. Aquat. Sci. 55, 2478-2491.

* Braioni, M.G., Gelmini, D., 1983. Rotiferi Monogononti (Rotatoria: Monogononta). Guide per il riconoscimento delle specie animali delle acque interne italiane. Consiglio Nazionale delle Ricerche, Italy, p. 180p.

* Brancelj, A., 2021. Shifts in zooplankton communities in high-mountain lakes induced by singular events (fish stocking, earthquakes): evidence from a 20-year survey in Slovenia (Central Europe). Aquat. Ecol. 55, 1253-1271.

* Carmignani, J.R., Roy, A.H., 2017. Ecological impacts of winter water level drawdowns on lake littoral zones: a review. Aquat. Sci. 79, 803-824.

* Catalan, J., Pla-Rabés, S., Wolfe, A.P., Smol, J.P., Rühland, K.M., Anderson, N.J., Kopáček, J., Stuchlík, E., Schmidt, R., Koinig, K.A., Camarero, L., 2013. Global change revealed by palaeoellimnological records from remote lakes: a review. J. Paleolimnol. 49, 513-535.

* Clifford, S., Salmon, M., 2019. Mgcv.Helper: helper functions for mgcv. R package version 0.1.9. Retrieved from. https://github.com/samclifford/mgcv.helper.

* Derlet, R.W., Richards, J.R., Tanaka, L.L., Hayden, C., Ger, K.A., Goldman, C.R., 2012. Impact of summer cattle grazing on the Sierra Nevada watershed: aquatic algae and bacteria. J. Environ. Public Health 2012, 760108.

* Donald, D.B., Vinebrooke, R.D., Anderson, R.S., Syrgiannis, J., Graham, M.D., 2001. Recovery of zooplankton assemblages in mountain lakes from the effects of introduced sport fish. Can. J. Fish. Aquat. Sci. 58 (9), 1822-1830.

* Edmondson, W.T., Winberg, G.G., 1971. A Manual on Methods for the Assessment of Secondary Productivity in Fresh Waters. Blackwell Scientific Publications, Oxford, UK.

* Einsle U. (1996). Copepoda: Cyclopoida: Genera Cyclops, Megacyclops, Acanthocyclops (No. 595.34 EIN). The Netherlands: SPB Academic Pub. 82p.

* Fontaneto, D., Kaya, M., Herniou, E.A., Barraclough, T.G., 2009. Extreme levels of hidden diversity in microscopic animals (Rotifera) revealed by DNA taxonomy. Mol. Phylogenet. Evol. 53, 182-189.

* Giussani, G., De Bernardi, R., Mosello, R., Origgi, I., Ruffoni, T., 1986. Indagine limnologia sui laghi alpini d'alta quota. Documenta dell'Istituto Italiano di Idrobiologia 9, 1-415.

* Harris, I., Jones, P.D., Osborn, T.J., Lister, D.H., 2014. Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 dataset. Int. J. Climatol. 34, 623-642.

* Hatch, L.K., Reuter, J.E., Goldman, C.R., 2001. Stream phosphorus transport in the Lake Tahoe basin, 1989-1996. Environ. Monit. Assess. 69, 63-83.

* Holzapfel, A.M., Vinebrooke, R.D., 2005. Environmental warming increases invasion potential of alpine lake communities by imported species. Glob. Chang. Biol. 11, 2009-2015.

* Kernan, M., Ventura, M., Bitusik, P., Brancelj, A., Clarke, G., Velle, G., Raddum, G.G., Stuchlik, E., Catalan, J., 2009. Regionalisation of remote European mountain lake ecosystems according to their biota: environmental versus geographical patterns. Freshw. Biol. 54, 2470-2493.

* Khaliq, I., Rixen, C., Zellweger, F., Graham, C.H., Gossner, M.M., McFadden, I.R., Antão, L., Brodersen, J., Ghosh, S., Pomati, F., Seehausen, O., Roth, T., Sattler, T., Supp, S.R., Riaz, M., Zimmermann, N.E., Matthews, B., Narwani, A., 2024. Warming underpins community turnover in temperate freshwater and terrestrial communities. Nat. Commun. 15, 1921.

* Knapp, R.A., Matthews, K.R., Sarnelle, O., 2001. Resistance and resilience of alpine lake fauna to fish introductions. Ecol. Monogr. 71, 401-421.

* Koenings, J.P., Burkett, R.D., Edmundson, J.M., 1990. The exclusion of limnetic cladocera from turbid glacier-meltwater lakes. Ecology 71, 57-67.

* Legendre, P., Legendre, L., 1998. Numerical ecology: developments in environmental modelling. Dev. Environ. Model. 20 (1).

* Lehner, B., Grill, G., 2013. Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrol. Process. 27, 2171-2186.

* Loewen, C.J., Vinebrooke, R.D., 2016. Regional diversity reverses the negative impacts of an alien predator on local species-poor communities. Ecology 97, 2740-2749.

* Loewen, C.J., Strecker, A.L., Larson, G.L., Vogel, A., Fischer, J.M., Vinebrooke, R.D., 2019. Macroecological drivers of zooplankton communities across the mountains of western North America. Ecography 42, 791-803.

* Loria, K.A., Christianson, K.R., Johnson, P.T., 2020. Phenology of alpine zooplankton populations and the importance of lake ice-out. J. Plankton Res. 42, 727-741.

* Machate, O., Schmeller, D.S., Loyau, A., Paschke, A., Krauss, M., Carmona, E., Schulze, T., Moyer, A., Lutz, K., Brack, W., 2022. Complex chemical cocktail, containing insecticides diazinon and permethrin, drives acute toxicity to crustaceans in mountain lakes. Sci. Total Environ. 828, 154456.

* MacLennan, M.M., Vinebrooke, R.D., 2016. Effects of non-native trout, higher temperatures and regional biodiversity on zooplankton communities of alpine lakes. Hydrobiologia 770, 193-208.

* Manca, M., Comoli, P., Lencioni, V., 1999. Population dynamics and production of crustacean zooplankton in two mountain lakes in the Italian Alps (Lake Paione superiore and Lake Malghette). J. Limnol. 58, 25-28.

* Margaritora, F., 1985. Fauna d'Italia. Edizioni Calderini Bologna, Cladocera. Italy, p. 400p.

* Miracle, M.R., 1978. Composición específica de las comunidades zooplanctonicales de 153 lagos de los Pireneos y su interes biogeográfico. Oecologia aquatica 3, 167-191.

* Miró, A., Ventura, M., 2015. Evidence of exotic trout mediated minnow invasion in Pyrenean high mountain lakes. Biol. Invasions 17, 791-803.

* Morales-Baquero, R., Carrillo, P., Barea-Arco, J., Pérez-Martínez, C., Villar-Argaiz, M., 2006. Climate-driven changes on phytoplankton-zooplankton coupling and nutrient availability in high mountain lakes of southern Europe. Freshw. Biol. 51, 989-998.

* Nevalainen, L., Ketola, M., Korosi, J.B., Manca, M., Kurmayer, R., Koinig, K.A., Psenner, R., Luoto, T.P., 2014. Zooplankton (Cladocera) species turnover and long-term decline of Daphnia in two high mountain lakes in the Austrian Alps. Hydrobiologia 722, 75-91.

* Obertegger, U., Smith, H.A., Flaim, G., Wallace, R.L., 2011. Using the guld ratio to characterize pelagic rotifer communities. Hydrobiologia 662, 157-162.

* Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'hara R.B., Simpson G.L., Solymos P., Stevens M.H.H., Wagner H., 2013. Package 'vegan'. Community ecology package, version 2 (9), 1-295.

* Osorio, V., Puig, M.A., Buchaca, T., Sabás, I., Miró, A., Lucati, F., Suh, J., Pou-Rovira, Q., Ventura, M., 2022. Non-native minnows cause much larger negative effects than trout on littoral macroinvertebrates of high mountain lakes. Biol. Conserv. 272, 109637.

* Paterson, M.J., Beaty, K.G., Findlay, D.L., Findlay, W.J., Schiff, S.L., St. Louis V.L., Venkiteswaran J.J., 2019. Long-term changes in nutrient dynamics and plankton communities following the creation of a new reservoir. Can. J. Fish. Aquat. Sci. 76, 1459-1469.

* Peter, H., Sommaruga, R., 2016. Shifts in diversity and function of lake bacterial communities upon glacier retreat. ISME J. 10, 1545.

R Core Team, 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/. (Accessed 10 September 2024).
Redmond, L.E., Loewen, C.J., Vinebrooke, R.D., 2018. A functional approach to zooplankton communities in mountain lakes stocked with non-native sportfish under a changing climate. Water Resour. Res. 54, 2362–2375.

Sánchez-Hernández, J., Cobo, F., Amundsen, P.A., 2015. Food web topology in high mountain lakes. PLoS One 10, e0143016.

Schabetsberger, R., Grill, S., Hauser, G., Wukits, P., 2006. Zooplankton successions in neighbouring lakes with contrasting impacts of amphibian and fish predators. Int. Rev. Hydrobiol. 91, 197–221.

Schabetsberger, R., Jersabek, C.D., Maringer, A., Kreiner, D., Kaltenbunner, M., Blažková, P., Pokorný, P., Denoël, M., Emmerstorfer, H., Lipovnik, C., Wölger, H., 2023. Pulling the plug - draining an alpine lake failed to eradicate alien minnows and impacted lower trophic levels. Water 15, 1332.

Slemmons, K.E., Saros, J.E., Simon, K., 2013. The influence of glacial meltwater on alpine aquatic ecosystems: a review. Environ Sci Process Impacts 15, 1794–1806.

Sommaruga, R., 2015. When glaciers and ice sheets melt: consequences for planktonic organisms. J. Plankton Res. 37, 509–518.

Sommaruga-Wögrath, S., Koinig, K., Schmidt, R., Tessadri, R., Sommaruga, R., Psenner, R., 1997. Temperature effects on the acidity of remote alpine lakes. Nature 387, 64–67.

Sommer, C., Malz, P., Seehaus, T.C., Liplpl, S., Zemp, M., Braun, M.H., 2020. Rapid glacier retreat and downwasting throughout the European Alps in the early 21st century. Nat. Commun. 11, 3209.

Spitale, D., Angeli, N., Lencioni, V., Tolotti, M., Cantonati, M., 2015. Comparison between natural and impacted alpine lakes six years after hydropower exploitation has ceased. Biologia 70, 1597–1605.

Stella, E., 1984. Fauna d'Italia. Copepoda: Calanoida (d'acqua dolce). Edizioni Calderini Bologna, Italy, p. 101p.

Sterner, R.W., 2009. Role of zooplankton in aquatic ecosystems. In: Likens, G.E. (Ed.), Encyclopedia of Inland Waters, 1st edition. Elsevier, Oxford, UK, pp. 678–688.

Strecker, A.L., Cobb, T.P., Vinebrooke, R.D., 2004. Effects of experimental greenhouse warming on phytoplankton and zooplankton communities in fishless alpine ponds. Limnol. Oceanogr. 49, 1182–1190.

Stuparyk, B.R., Vinebrooke, R.D., 2024. Contrasting resistance and resilience trajectories of zooplankton communities in restored fishless alpine lakes. Ecosphere 15, e4836.

Tiberti, R., 2025. Records of Zooplankton from 101 Alpine Lakes in the Western Alps. Occurrence dataset, Consiglio Nazionale delle Ricerche - Istituto di Ricerca sulle Acque. https://doi.org/10.15468/uesgz5 accessed via GBIF.org on 2025-04-16.

Tiberti, R., Metta, S., Austoni, M., Callieri, C., Morabito, G., Marchetto, A., Tartari, G., von Hardenberg, J., Provenzale, A., 2013. Ecological dynamics of two remote alpine lakes during ice-free season. J. Limnol. 72, 401–416.

Tiberti, R., Nelli, L., Tartari, G., Wienckowski, E., Marchetto, A., Rogora, M., 2019a. Multi-year trends and determinants of the hydrochemistry of high mountain lakes from the western Italian Alps. Aquat. Sci. 81, 54.

Tiberti, R., Bogliani, G., Brighenti, S., Iacobuzio, R., Liautaud, K., Rolla, M., von Hardenberg, A., Bassano, B., 2019b. Recovery of high mountain alpine lakes after the eradication of introduced brook trout *Salvelinus fontinalis* using non-chemical methods. Biol. Invasions 21, 875–894.

Tiberti, R., Buscaglia, F., Callieri, C., Rogora, M., Tartari, G., Sommaruga, R., 2020. Food web complexity of high-mountain lakes is largely affected by glacial retreat. Ecosystems 23, 1093–1106.

Tolotti, M., Manca, M., Angeli, N., Morabito, G., Thaler, B., Rott, E., Stuchlik, E., 2006. Phytoplankton and zooplankton associations in a set of alpine high altitude lakes: geographic distribution and ecology. Hydrobiologia 562, 99–122.

Tonolli, V., Tonolli, L., 1951. Osservazioni sulla biologia ed ecologia di 170 popolamenti zooplanktonici di laghi italiani d'alta quota. Memorie dell'Istituto Italiano di Idrobiologia 6, 53–136.

Ventura, M., Petrusek, A., Miró, A., Hamrová, E., Bunay, D., De Meester, L., Mergeay, J., 2014. Local and regional founder effects in lake zooplankton persist after thousands of years despite high dispersal potential. Mol. Ecol. 23, 1014–1027.

Ventura, M., Tiberti, R., Buchaca, T., Bunay, D., Sabás, I., Miró, A., 2017. Why should we preserve fishless high mountain lakes? High mountain conservation in a changing world 181–205.

Williams, M.W., Losleben, M.V., Hamann, H.B., 2002. Alpine areas in the Colorado front range as monitors of climate change and ecosystem response. Geogr. Rev. 92, 180–191.

Williamson, C.E., Saros, J.E., Vincent, W.F., Smol, J.P., 2009. Lakes and reservoirs as sentinels, integrators, and regulators of climate change. Limnol. Oceanogr. 54, 2273–2282.

Wood, S., Wood, M.S., 2015. Package 'mgcv'. R package version 1 (29), 729.