Increased drying threatens alpine pond biodiversity more than temperature increase in a changing climate

doi:10.21203/rs.3.rs-4703447/v1

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Increased drying threatens alpine pond biodiversity more than temperature increase in a changing climate

https://doi.org/10.21203/rs.3.rs-4703447/v1

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Climate change is one of the main drivers of species erosion. Rapidly changing climate in the form of warming, drying, and habitat isolation causes freshwater species to change their spatial extent, as most species have little capacity for in situresponses. However, the relative contribution of these three effects to freshwater species’ changing spatial distributions is largely debated. To shed light on this debate, we explored temperature, hydroperiod, and habitat connectivity effects on alpine pond species occupancy probabilities in the Northern French Alps. We studied alpine ponds as ideal test systems because they face climate change effects more rapidly, and in more concentrated areas, than any other freshwater ecosystem. We used multi-species occupancy models with three biological groups (amphibians, macrophytes and Odonata) to examine contrasted responses to climate change. Contrary to expectations, temperature was not the main driver of species occupancy probabilities. Instead, hydroperiod and connectivity were stronger predictors of species occupancy probabilities. Furthermore, temperature increase had the same effect on occupancy probabilities of generalist and cold-specialist species. Nonetheless, temperature disproportionately affected a greater number of specialist species compared to generalists. We conclude that climate change mitigation will primarily benefit a greater number of specialist species than generalists. Finally, we suggest that enhancing our understanding of freshwater hydroperiods will improve our predictions of climate change effects on freshwater species distributions.

conservation

freshwater

distribution

models

occupancy

connectivity

Climate change is one of the main drivers of species erosion (IPBES, 2019). Rising global mean temperature is accompanied by greater temperature extremes, as indicated by the increasing frequency and strength of heat waves in many regions (Masson-Delmotte et al., 2018; Mukherji et al., 2023). Precipitation patterns are also changing (Milly et al., 2005; Walther et al., 2002), with increasing frequency and intensity of droughts (Barnett et al., 2005; Woodward et al., 2010) and floods (Donat et al., 2013; Masson-Delmotte et al., 2018).

Species possess intrinsic adaptive capacities to climate change characterized by three key components (Bellard et al., 2012; Swaegers et al., 2024). (1) Adaptation: species with very rapid generation times can adapt via evolutionary mechanisms (Hughes, 2000; Swaegers et al., 2024). (2) Plasticity: certain species have the capacity to acclimate and modify their physiological traits, including growth, respiration, and tissue composition (Hughes, 2000; Lindholm et al., 2015). Additionally, species may adapt their behavior, such as day-night activity rhythm and eating locations (Dussault et al., 2004; Melin et al., 2014). They also can adapt their phenology, such as reproductive timing and dormancy cessation (Kannan et al., 2009; Roy & Sparks, 2000; Walther et al., 2002). (3) Dispersion: certain species have the capacity to disperse quickly within their current habitat, when it has become unsuitable, to more suitable habitat (Dawson et al., 2011; Edelsparre et al., 2024; Swaegers et al., 2024).

The modifications in species geographic distributions is one of the current effects of rapidly changing climate, because most species do not exhibit in situ adaptation (Baur & Baur, 2013). Indeed, species are moving to cooler climates (Freeman et al., 2018). The ranges of both flora and non-migratory fauna are shifting toward higher elevations or/and latitudes, with altitude gains of 20–30 m/decade for flora (Kelly & Goulden, 2008; Lenoir et al., 2008; Parolo & Rossi, 2008) and 10–60 m/decade for fauna. Likewise, the ranges of many high-latitude fauna are moving northwards at 30–70 km/decade (Baur & Baur, 2013; Hickling et al., 2006; Roth et al., 2014).

Species are constrained by their thermal tolerances and the connectivity of suitable habitats (Culp et al., 2022; De Frenne et al., 2021; Fazlioglu et al., 2020). Species exhibiting a limited distribution range (e.g., thermal specialists such as cold or hot stenotherms) are more susceptible to the impacts of climate change than those with a wide distribution range (e.g., thermal generalists such as eurytherms or ubiquist species) (Lindholm et al., 2015; Pallarés et al., 2020; Rosset & Oertli, 2011). Freshwater species additionally are adapted to specific hydroperiods (the frequency, duration, and magnitude of water (Convertino et al., 2013)) to successfully complete their life cycle (Ryan et al., 2014). The escalation in frequency of heatwaves and the decline in hydroperiod (Carlson et al., 2020; Diamond et al., 2023; Huang et al., 2022) can transform, for different durations, suitable habitat of some species into unsuitable habitat (Carlson et al., 2020; Galatowitsch & McIntosh, 2016). Such changes have already lead to mass mortality events with occasional species disappearance (e.g., amphibians, plants), and a shift in dominance to species that are tolerant to drier conditions (Carlson et al., 2020; He et al., 2016; Sandvik & Odland, 2014). Another consequence of drying is loss of habitat connectivity, as suitable patches become increasingly isolated (Malish et al., 2023). Decreased connectivity among patch habitats reduces species migration success (when possible) as it lengthens travel distances, and recolonization of previously occupied patches becomes dependent on rewetting frequencies (Bogan et al., 2015).

Efforts to understand changing species geographic distributions under drying and warming are abundant, and include empirical (Baur & Baur, 2013; Kang et al., 2016; Lynch et al., 2016) and model-based methods (Buisson & Grenouillet, 2009; Menéndez et al., 2014; Parmesan et al., 1999). However, for most of these efforts, the estimated shifts are based on global climate parameters, and they rarely integrate landscape structure (Opdam & Wascher, 2004). Ignoring landscape and habitat structure is likely a critical omission, as distribution shifts predicted by climatic models are often incorrect (Warren et al., 2001). To overcome these problems, some researchers now integrate dispersal abilities of species in predictive models (Keith et al., 2008; Vos et al., 2008). Still, the relative contribution of warming, drying and habitat isolation on species distributions is largely debated and remains unclear.

Mountain ecosystems exhibit elevated temperature responses to climate change when compared to global averages, particularly for warming (Gobiet et al., 2014; Thuiller et al., 2005) and heat waves (Huang et al., 2022). They thus provide a highly relevant study area because these climatic modifications are leading to rapid changes in habitat and species distribution (Dial et al., 2007; Gehrig-Fasel et al., 2007; Salerno et al., 2014). Due to their altitude gradients, mountains also provide optimal environments for observing past and future global changes in biodiversity (Peterson et al., 1997) in what is referred to as the "altitude-for-latitude disparity" (Jump et al., 2009). Indeed, the various vegetation types spanning hundreds of kilometers in longitude or latitude in plains are condensed into just a few vertical kilometers within mountains (Peterson et al., 1997). Likewise, mountainous freshwater ecosystems offer valuable opportunities for studying the effects of climate change on the critical drivers of biodiversity: water temperature, hydroperiod, and connectivity (Beniston, 2006; Lamouille-Hébert et al., 2024; Williamson et al., 2009).

Of the mountainous freshwater ecosystems, alpine ponds are especially susceptible to climatic changes (Beniston, 2006). They are warming rapidly (+ 0.72°C/decade (O’Reilly et al., 2015)) and drying more regularly (Carlson et al., 2020). At high altitudes, retreating glaciers increase the space for new ponds and glacier meltwater increases pond size (Bosson et al., 2023; Salerno et al., 2014). The rapid changes in the spatial distribution of alpine ponds (Salerno et al., 2014; Seimon et al., 2007) creates opportunities for the colonization of new species (Leibold et al., 2004; Macarthur & Wilson, 1967; Redmond, 2018). Simultaneously, at lower altitudes, south-facing ponds disappear or are reduced in size because of the increase of evaporative processes (Salerno et al., 2014). These combined changes to alpine pond extent may threaten freshwater stenotherm, endangered and endemic species (Khan & Baig, 2017; Yang et al., 2017).

In this study, we explored the relations between climate change effects (temperature, hydroperiod and connectivity) and Odonata, Amphibia and macrophytes occupancy probabilities in 73 ponds in the Northern French Alps. We specifically tested three predictions: 1) summer water warming leads to an increase of species occupancy probabilities for generalist species and to a decrease for cold specialist species, 2) increased drying, leads to a decrease of species occupancy probabilities for all species, and 3) the more geographically isolated the alpine pond is, the lower the probability of occupancy by alpine pond species.

2-1) Sampling design

The study area was in Aiguille Rouges and Mont Blanc mountains ranges, in Haute-Savoie alpine National Natural Reserve (Fig.1). Alpine ponds studied were situated above the treeline. With the reserve guards, we identified the accessible sectors where ponds were present. In each sector, we selected sampling sites in the field to represent alpine ponds biodiversity, and spatial and altitude gradients (white dots in Fig.1).

A single trained observer (M.L-H.) between 2021-07-15 and 2023-10-12 conducted the field survey. The survey comprised 73 alpine ponds, natural or human-made, ranging from 1,750 to 2,335 m (median= 2,064 m) above the sea level (Fig.1), whose maximum area varied between 0 and 3,500 m² (median: 28.5 m²) and whose maximum depth ranged between 0 and 1.10 m (median: 0.25 m). Four field visits were carried out to record the presence and absence of water in the ponds. Half of the ponds were visited two time in summer 2021 and one time per summer 2022 and 2023. When the others were visited one time in 2021, two time in 2022 and one time in 2023.

2.2) Species sampling methods

To describe species and to improve detectability of rare species, all sites were visited twice in the same year (2021 or 2022) for more than 30 minutes (46 minutes on average, minimum 30 minutes and maximum 2 hours and 35 minutes). Half of the sites were sampled in 2021 and half in 2022. We worked on three biological groups (amphibians, macrophytes and Odonata) to examine contrasted responses to climate change. All individuals were determined in situ. We counted the number of individuals of all detected species.

2-2-1) Odonata

To maximize the detection probability of adults of Odonata, the sampling was conducted between 15^th July and 15^th August. Sampling was conducted when the weather conditions were optimal for Odonata activity, i.e. no rain during two days before field work, low to moderate wind speed (< 5 in Beaufort scale), few clouds (cloud cover <75 %), and temperature above 17 °C. The maturation phase of the Odonata studied species lasts two to three weeks (Grand & Boudot, 2006; Wildermuth, 2013). Usually, during the maturation phase, subadults move away from the pond where they emerged before returning to their natal pond as adults. Thus, to detect subadults (including exuviae, i.e., the skin of emerging subadults) and adults we set the second sampling two or three weeks between after the first. The first visit was between 15^th July and 31th July and the second between 1^st August and 15^th August.

At each visit, we used different methods to detect adults and subadults of Odonata. In addition to visual species detection without collection, we used an Odonata-net for flying adults and a specific water net for Odonata aquatic subadults. Exuviae were collected in a minimum of a two meters buffer around the banks of the ponds.

2-2-2) Amphibians

We used two methods to detect adults and subadults of Amphibia: 1) visual detection without collection, and 2) Amphibia water net for adults and subadults.

2-2-3) Macrophytes

Plants present in free water were identified from different randomly located littoral quadrats (2 m*2 m). In case of low visibility, we used a grappling hook beyond each quadrat to explore the diversity at the bottom of the pond. We determined flora on 1 to 20 quadrats in function of the pond area : <20 m², 1 or 2 quadrats; <100 m², 2 to 9 quadrats; <1500 m², 10 quadrats; <3500 m², 15 quadrats and <5000 m², 20 quadrats. They were distributed at equal distance all around the free water of the pond. Each visit had different quadrat distributions: the first quadrat at each visit was placed where free liquid water was nearest to the observed site entry point.

2-2-4) Species classification.

To investigate the relation between the main drivers of climate change and thermal specialist or generalist species, we categorized each detected species as either a specialist or a generalist. Odonata specialist species occurred predominantly above 1500m, allowing differentiation between cold stenothermal species and eurythermal species (Oertli, 2010). Based on this altitudinal classification, we classified amphibians and flora specialist and generalist (SM.S1 and Table1).

2-3) Environmental covariates

2-3-1) Summer water temperature

We installed a temperature logger at the bottom of the pond at the first visit of each site to study the effect of water temperature on alpine pond biodiversity (HOBO, TidbiT-MXTemps400). Temperature was sampled every 30 minutes between 2021-09-24 and 2022-07-31. Ice-melt (T>0°C) occurred for all ponds by 2022-05-31. Thus, we defined the summer period as being between 2022-06-01 and 2022-07-31. Because some logger did not work during summer period, we calculated four temperature metrics (°C) in only 58 alpine ponds:

- Cumulative growth-degree-days (GDD), i.e. the sum of daily temperature mean.

- Minimum temperature: the 5th quantile of minimum daily temperature.

- Maximum temperature: the 95th quantile of maximum daily temperature.

- Median temperature range: the median of daily difference between the maximum and the minimum temperature.

2-3-2) Hydroperiod

Alpine ponds studied were shallow (depth: 0-1.10 m). Pond water can be completely iced in winter or dried in summer. We could defined the winter period for 59 ponds as being between 2021-11-01 and 2022-05-31. We calculated two hydroperiod metrics respectively in 73 and 59 alpine ponds:

- Summer drying gradient: the frequency of drying for each pond based on the four visits (from zero to four times). 59 % of the alpine pond sampled never dried (43) and 41 % dried almost one time: 19 % one time (14), 11 % two times (8), 5.5 % three times (4) and 5.5 % four times (4).

- Winter ice-stage: the number of days which maximum temperature was £ 0 °C.

2-3-3) Connectivity

We calculated several connectivity metrics among alpine ponds in the study area. We used a known distribution of wetlands because the distribution of alpine ponds is currently largely unknown. We compiled two datasets: 1) the departmental inventory of wetlands made in 2021 by Haute-Savoie territorial department directory (DDT74) and the local conservatory (Asters-CEN74) and 2) the alpine ponds detailed by M.L-H during field campaigns (2017–2019 and for this study 2021 and 2022). We made spatial analyses in a 10 km buffer around sampled ponds (Fig.1) to calculate four groups of connectivity metrics, with RStudio version 2023.5.0.335 (Posit team, 2023) and R version 4.2.0 (R Core Team, 2022):

- Topographical minimum distance between a pond and the nearest potential pond (m): in altitude, with reliefs and valleys, Euclidean distance is not sufficient to understand the distance between alpine ponds. We used a digital elevation model (DEM) information to integrate the elevation distance and calculate topographical distance between ponds. DEM raster was downloaded from the French national geographic institute (IGN). We utilized the RGE Alti® dataset at 5 meter sampling (https://geoservices.ign.fr/documentation/donnees/alti/rgealti) that we resampled to Sentinel-2 spatial sampling, at 10 meters. To calculate this metric we used the TopoDistance package with the function topoDist (Wang, 2020).

- Number and area of wetlands in different buffers around sampled ponds: we calculated these two metrics for each sampled pond in different buffers (100, 200, 300, 400, 500, 1,000, 2,000, 3,000, 4,000, 5,000, 7,500, and 10,000 m Euclidean distance).

- Land cover in different buffers around sampled pond: with the Python package beemap (Wu, 2020), we used Google Earth engine (Gorelick et al., 2017) with 38 pictures with less than 30% of clouds extracted from Sentinel-2 spatial sampling (10 meter) (Copernicus Sentinel data of Sentinel-2), to calculate the maximum of the median normalized difference vegetation index (NDVI) of summer 2022 (between 1st of June and 1st of September) in different buffers (100, 200, 300, 400, 500, and 1,000 m Euclidean distance) around each sampled pond.

- Number of tributaries in different buffers around sampled ponds and length of total tributaries: we calculated these two metrics for each sampled pond in different buffers (100, 200, 300, 400, 500, and 1,000 m Euclidean distance) based on the rivers cartography made in 2019 by DDT74.

2-4) Statistical methods

We tested the predicted relations between “summer water temperature”, "hydroperiod”, “connectivity” and alpine pond species occupancy probabilities. We modeled the detection-nondetection data of the replicates per sampling site (i.e. 2 temporal replicates × 2 sampling stages - subadults and adults) using multi-species occupancy models (MSOMs) (Devarajan et al., 2020; Dorazio & Royle, 2005; Zipkin et al., 2023). MSOMs rely on repeated sampling of a biological community at multiple spatial locations to estimate the number and composition of species in the community (Dorazio & Royle, 2005; MacKenzie et al., 2002). It allows for imperfect detection of species to be taken into account (Devarajan et al., 2020). They can simultaneously model the effects of covariates at the species and community levels, using information from the most frequent species to improve the accuracy of estimates, particularly for the rarest species (Dorazio & Royle, 2005; Mourguiart et al., 2021; Zipkin et al., 2023). We used the library spOccupancy (Doser et al., 2022) with RStudio version 2023.5.0.335 (Posit team, 2023) and R version 4.2.0 (R Core Team, 2022). We used the function sfMsPGOcc to account for spatial autocorrelation, and correlations between species. Imperfect detection was integrated in all our models considering the interaction between temporal replicates and sampling stages. We fit 104 models (SM.S2) to analyze the simple and quadratic relation between each scaled covariate and species occupancy probabilities. This relation was significant when 95 percent confidence intervals (CIs) of the intercept did not include zero. We fit a supplemental model without covariates and with sampling potential bias (stage*session)) to determine in what percent of sites species are present (occurrence) and estimated our sampling design probability to detect these species when they are present (detection probabilities). Script can be found in SM.S3 and with data in Data INRAE repository (https://doi.org/10.57745/YFE6IJ) to follow the FAIR (findability, accessibility, interoperability and reusability) guiding principles for managing scientific data (Wilkinson et al., 2016).

3 − 1) Biological communities

We detected 31 species: three species of amphibian, 20 of macrophytes and eight of Odonata (Table 1). Specialist species represent 0% of Amphibia, 50% of Odonata (4 species) and 50% of macrophytes (10 species). Species had occurrences ranging between 0.00 and 0.28 (Table 1). They were detected with a detection probability depending on the species, ranging between 0.62 (CI: 0.34–0.86) and 1.00 (CI: 0.98-1.00) (Table 1).

We detected 1,108 individuals of Odonata at 60% of the sampled alpine ponds (44 sites) (851 subadults and 235 adults of specialist species and 4 subadults and 18 adults of generalist species). Per site, we detected between 0 and 5 Odonata species from the eight present in our study. We identified 1,437 individuals of Amphibia at 78% of the sampled alpine ponds (57 sites) (1099 subadults and 338 adults). Per site, we counted 0 to 3 Amphibia species from the three present. Macrophytes were recorded at 84% of the sampled alpine ponds (61 sites). Per site, we counted 0 to 11 macrophytes species from the 20 present.

3 − 2) Relations between covariates and species occupancy probabilities

Sixty percent of species (71% of specialists and 50% of generalists) occupancy probabilities had a relation with summer temperature (GDD and/or summer minimal temperature). Thirty percent of species had a quadratic relation with GDD and/or 40% a positive relation with summer minimal temperature (Fig. 2). Odonata species occupancy probabilities had no relationship with GDD. Amphibia (Bufo bufo) and macrophytes (50% of specialist species and 30% of generalist) species occupancy probabilities had a relation with GDD (Fig. 2a). This relation had the same form with the same slope directions for Amphibia’ and macrophytes’ specialist and generalist species. The probability of all species occupancy is optimum for intermediate values of GDD, from 636 to 671°C (Fig. 2b). Amphibia (Ichtyosaura alpestris), macrophytes (30% of specialist species and 30% of generalist) and Odonata (75% of specialist species and 50% of generalist) species occupancy probabilities increased when summer minimal temperature increased (Fig. 2a and 2b). Alpine species occupancy probabilities were marginally related with the increase of summer maximal temperature and summer median temperature (SM.S4). For Bufo bufo, a generalist species, occupancy probability increased with the increase of maximum water temperature (when Temp max > 30°C). On the contrary, occupancy probability of Aeshna juncea, a specialist species, decreased when maximum water temperature exceeded 21°C.

Ninety-three percent of species (93% of specialists and 94% of generalists) occupancy probabilities decreased when hydroperiod decreased (Fig. 3). Only one amphibian species (Bufo bufo) and two macrophyte species (Eriophorum angustifolium and Juncus articus) were not correlated to the increase of summer drying gradient (Fig. 3a). Odonata, Amphibia and most macrophytes (except Carex nigra and Bryophytes sp.) probabilities of occupancy were almost null when alpine ponds had been observed dried at each visit (Fig. 3b). Amphibia species occupancy probabilities were not related with winter ice-stage. Macrophytes (90% of generalist and specialist species) and all Odonata species occupancy probabilities had a quadratic relation with this covariate (Fig. 3a). This relation had the same form with the same slope directions for macrophytes’ and Odonata’ specialist and generalist species. The probability of all species occupancy is optimum for intermediate values of winter ice-stage between 83 to 121 days (Fig. 3b). Macrophytes threshold was 83 to 121 days when for Odonata it was 110 to 118 days.

Eighty-three percent of species (93% of specialists and 75% of generalists) occupancy probabilities had a quadratic relation with topographical minimum distance between a pond and the nearest one (Fig. 4). Amphibians (Ichtyosaura alpestris and Rana temporaria), all macrophytes and 75% of Odonata specialist species and 25% of Odonata generalist occupancy probabilities were concerned by this relation (Fig. 4a). The relation had the same form with the same slope directions for specialist and generalist species from the three groups. The probability of all species occupancy is optimum for intermediate values of topographical minimum distance between a pond and the nearest pond between 117 to 208 meters (Fig. 4b). Amphibians threshold was 132 to 181 meters when it was 117 to 208 meters for macrophytes and 126 to 162 meters for Odonata.

Among the four generalist species whose occupancy probabilities were not correlated to topographical minimum distance between a pond and the nearest pond, Aeshna cyanea and Libellula quadrimaculata occupancy probabilities were not related with all connectivity covariate. Bufo bufo and Pyrrhosoma nymphula occupancy probabilities were negatively related with the number of wetlands and positively (after reaching a threshold) with the number of tributaries on the 100 meters buffer (SM.S5). Furthermore, occupancy probabilities of Aeshna juncea, the only specialist not influenced by topographical minimum distance between a pond and the nearest pond, decreased with the increase of the number and the area of wetlands in 3,000 and 4,000 meters buffers. Nevertheless, the increase of the area of wetlands on the 10,000 meters buffer increased its occupancy probability.

Our findings suggest that contrary to what we expected, the effect of increased water summer temperature was limited to a few species only and with the same relation for generalist and cold specialist species occupancy probabilities. We also found that the predicted decreasing hydroperiod and connectivity, especially considering topographic distance between ponds, resulted in reduced occupancy probabilities of species. Such relation influenced a greater number of studied species than the increased summer water temperatures. Current predictions of the effect of climate change on the distribution of species based principally on temperature variation minimized its effects on species occupancy probabilities.

4 − 1) Temperature was not the primary driver of biodiversity distribution

Temperature drives alpine species distribution. Its increase is influencing species densities (Alatalo et al., 2017) and increasing the risk of species local extinction. This last result has been demonstrated in all mountain ecosystems as terrestrial (plants) (Jump et al., 2009), streams (Stoneflies, Lednia) (Green et al., 2022) and ponds (Arctic fairy shrimp, Branchinecta paludosa) (Lindholm et al., 2015). Therefore, temperature increases are causing continued displacement of communities in mountain terrestrial ecosystems (Lenoir et al., 2008) as well as aquatic environments (Li et al., 2016; Sáinz-Bariáin et al., 2016). Consequently, temperature is the primary driver used to describe the effects of climate change on alpine species distribution (Adhikari et al., 2023; Engler et al., 2011; Tovar et al., 2022).

We observed that increasing the summer accumulated warm limited the occupancy probabilities of up to 30 percent of the 30 alpine pond species studied when minimum temperature had positive effects on species occupancy of 40 percent. Temperature increase had effects only in a part of studied species occupancy probabilities contrary to our expectations, but in line with the main results of a global meta-analysis including both terrestrial and marine fauna (Parmesan & Yohe, 2003).

When we showed an effect of temperature increase on species occupancy probabilities, summer temperature accumulated warm and minimum temperature effects were different. We found that summer accumulated warm increases decreased amphibian and macrophytes species occupancy probabilities when a threshold was reached but had no effects on Odonata’. Amphibian and macrophytes species occupancy probabilities began with an increase when water summer accumulated warm increased. This well studied phase (Cross & Zuber, 1972; Liu et al., 2022; McMaster & Wilhelm, 1997) corresponds to the triggering of their development and growth. As we showed, after a threshold occupancy probabilities of species decreased. This is because their development stops and if temperature accumulated continues to increase it can reach their lethal point (Abbasi et al., 2023; Wahid et al., 2007). The unexpected absence of effects of summer accumulated warm increases on Odonata species occupancy probabilities could be due to their tropical origin (Pritchard, 1989). Odonata exhibit thermoregulatory plasticity despite being ectothermic: color change, regulate hemolymph circulation in the thorax and abdomen, or employ "wing-whirring" behavior to thermoregulation (May, 1976; Polcyn, 1994; Sternberg, 1997). Our results suggest that cold specialist species of Odonata had the same capacity as other Odonata to colonize a broad spectrum of thermal habitats.

We also found that part of species occupancy probabilities from the three different studied groups were increased by summer minimum temperature increase. This is because in high mountains, low temperatures stress species, reducing their growth and survival (Cabrera, 1996; Larcher et al., 2010). Cold thermal specialist species are adapted to high mountain extreme colds. For example, cold adapted plants can express some particular forms/transcripts of proteins (e.g., rubisco) to increase “carbon assimilation rate supporting the photochemical mechanism of photosynthetic acclimation to cold” (Jurczyk et al., 2016). Unexpectedly and contrary to our predictions, cold specialists and thermal generalists studied species (40%) occupancy probabilities both increased with temperature increase. Low temperature increase reduces low temperatures stress for all species allowing them to colonize new alpine ponds. As we detected, temperature exerts a notable influence on species distribution, yet it is not the sole determinant of occupancy probabilities for all species in response to climate change.

4 − 2) Hydroperiod constrained more species occupancy probabilities than temperature

Hydroperiod reduction can be a major disturbance of aquatic biodiversity communities (Greig et al., 2013; Leigh & Datry, 2017; Stubbington et al., 2019). In lotic and lentic communities, absence of water leads to lower density and numbers of aquatic taxa at a given site than when water is present (Leigh & Datry, 2017; Rosset et al., 2017; Wissinger et al., 2009). The few studies conducted in alpine lentic and lotic freshwaters identified the reduction of density and diversity of Macroinvertebrates, bryophytes and high soil moisture vascular plant species with hydroperiod decrease (Doretto et al., 2020; He et al., 2016; Sandvik & Odland, 2014). Hydroperiod decrease can lead to changes in species distribution (Tolonen et al., 2019) and local extinction of species, for example amphibians, Bryophytes and aquatic vascular plants (Carlson et al., 2020; He et al., 2016; Sandvik & Odland, 2014).

Here, we found that most (90%) studied species occupancy probabilities decreased when summer drying increased, corroborating previous studies on fishes and amphibians (Ogston et al., 2016; Walls et al., 2013). This was notably the case for species from the three biological groups we studied. From Odonata, amphibians and macrophytes, some species are known to be summer drying-resistant. For example, larvae of Coenagrion hastulatum can resist desiccation and Somatochlora alpestris is able to grow without free water both by burrowing into the mud or damp peat (Heidemann & Seidenbusch, 2002; Kury & Wildermuth, 2013). It is also the case for amphiphytes, plants species which ability to produce terrestrial and aquatic growth forms, or aquatic and aerial leaves (heterophylly phenotypic plasticity) allow them to survive short-term drying (De Wilde et al., 2017; Wells & Pigliucci, 2000; Zelnik et al., 2021): Caltha palustris (Dorotovičová, 2013), Juncus sp. and Carex sp. (Casanova, 1997). However, like all studied species, drying-resistant species occupancy probabilities decreased with summer drying increased and this is probably because thresholds in drying-resistant exist, as found in rivers and streams for example (Stubbington & Datry, 2013).

We found here that when the number of water-freezed days increased, most (83%) studied species occupancy probabilities decreased. This was the case for species from two biological groups studied (Odonata and macrophytes). The low occupancy probabilities of these species associated to a low number of water-freezed days could be explain by the exposure to extreme air temperature, winter insufficient-feeding resources or low nutrients when photoperiod is sufficient to species breaking diapause and the energetic cost to adapt to frequent water state changes (Bale & Hayward, 2010). In fact, we showed for Odonata and macrophytes that species occupancy probabilities increased with freezing days up to a threshold, aligning with findings from ice enclosure stress tolerant aquatic invertebrates and vascular plants (Green et al., 2022; McAllen, 1997; Renman, 1989). In fact, alpine freshwater species are adapted to a water-freezing state and it helps them eliminate species coming from warmer habitats, potentially competitors or predators (Carbonell et al., 2024; Theissinger et al., 2013). Nevertheless, when the threshold was reached the occupancy probabilities of species decreased because long-term freezing still represents a possible lethal freezing risk for these species (Boudot et al., 2014; Hotaling et al., 2021; Rehm et al., 2021). Amphibians were the sole group examined whose species occupancy probabilities were unaffected by the potential long duration of these stresses and this is probably because they are adapted to survive to long-term freezing as found in different studies (Costanzo & Lee, 2013; Storey, 1999; Yokum et al., 2023).

Drying decreased the occupancy probabilities of a higher number of studied species (summer hydroperiod: 90%; winter drying duration: 83%) than summer water temperature increase (summer temperature warm: 30%; minimum temperature: 40%). To enhance our comprehension of climate change effects on freshwater species distribution, improving knowledge of hydroperiods is necessary. Because of the lack of long-term hydroperiod records, for example community compositions are used to try to predict the hydroperiod of wetlands or water-bodies (Gaiser et al., 1998; Lillie, 2003). It is urgent to set up long-term monitoring of hydroperiods in alpine ponds to lay the foundations to develop a model that predicts hydroperiods for known ones.

4 − 3) Isolation constrains species occupancy probabilities

Theoretical models suggest that limiting connectivity will reduce colonization or recolonization and increase local extinctions in source–sink systems (Macarthur & Wilson, 1967). Connectivity decrease should in turn affect metacommunity dynamics (sensu Leibold et al., 2004). Aquatic species are already concerned by these isolation threats altering their movement and survival (Serrano et al., 2020). In lotic and lentic ecosystems, dried hydrologic connections act as barriers to species displacement, for example for fish (Baber et al., 2002; Jaeger et al., 2014; Perkin & Gido, 2012) or macroinvertebrates (Bae & Park, 2016; Gauthier et al., 2021; Sarremejane et al., 2021). In lentic patchy environments, drying impacts suitable habitat (patch) surface and connectivity between them, for example for turtles (Kindlmann & Burel, 2008; Serrano et al., 2020). To recolonize suitable patches, species need to be able to migrate to connected ones (patches not dried) (Macarthur & Wilson, 1967). Decreases of connectivity affect persistence and turn-over of species and ultimately lead to changes in their occupancy probabilities (Serrano et al., 2020).

As predicted and in coherence with previous investigations we showed that decrease of connectivity decreased occupancy probabilities of most studied species. To analyze the effects of connectivity on species occupancy probabilities we used different structural metrics linked with the patch spatial distribution in the landscape (patch number, patch sizes, and inter-patch distances) (Tischendorf & Fahrig, 2001; With & Crist, 1995). We evidenced that number and patch sizes around a studied patch had effects on occupancy probabilities of less species (few species) than inter-patch topographic distances (83%). To face rapid climate change effects such as isolation caused by drying, we reinforced the previous result that corridors or chains of stepping-stones (short inter-patch topographic distances) are more crucial for sustaining most species populations than dense networks (Hodgson et al., 2012). Inter-patch topographic distances had the same effects on all species of all studied groups (Odonata, Amphibia, macrophytes). As for Fahrig (2017), one of our results was that the effects of inter-patch topographic distance on the occupancy probabilities were also the same for generalist and specialist species. We showed for these three groups' that species occupancy probabilities increased before decreasing when a threshold was reached. These results illustrate the importance of maintaining spatial heterogeneity of patches (inter-patch distance), like it was demonstrated by different authors (Gauze, 1934; Huffaker, 1958). It allows maintaining the persistence of prey and predator systems with separate prey refuges and dividing food resources in different habitats. It also can reduce the predator and parasitoid dispersal efficiency and decrease covariance of competing species (Roland, 1993). In addition, the distance between patches occupied by a matrix of terrestrial habitat is necessary for species (Duelli, 1997), for example for the maturation at different stages of studied species (Odonata and Amphibia). When the topographic inter-patch distances increased, a threshold was reached probably when it exceeded dispersal abilities of species (Macarthur & Wilson, 1967; Makoto & Wilson, 2019).

The effect of inter-patch topographic distance increase was neutral for the occupancy probabilities of few species. In fact, species with longer distance dispersal as Aeshna juncea, Aeshna cyanea and Libellula quadrimaculata are less sensitive to structural connectivity (Pearson & Dawson, 2005). Aeshna juncea was the only specialist species with a neutral effect of inter patch topographic distance on its occupancy probabilities. Nevertheless, we demonstrated that Aeshna juncea needs the presence of high areas of ponds in 10,000 meters buffers to increase its occupancy probabilities. This could be due to its necessity and flying ability to pass valleys to persist in a mosaic of alpine pond areas and valleys. Because we did not find similar studies, it will be particularly interesting to investigate genetic structuration of this species at a larger scale than we did, to better understand our results.

For Bufo bufo and Pyrrhosoma nymphula the densities of ponds and tributaries close (100 meters buffer) to the occupied pond were important to maintain their persistence. In order to increase their occupancy probabilities, they need to be isolated from the other ponds but be able to move to tributaries if conditions are unsuitable in their current living patch.

To improve knowledge about the effects of climate change on current and future distribution of species, inter-patch topographical distances need to be included in distribution models. Researches could begin with actual not widely known distribution of patch habitat. Nevertheless, one of the future challenges is to enhance their distribution and hydroperiod to limit biases. In fact, small decreases in hydroperiods lead to large decreases of connectivity between habitats in freshwater ecosystems (Baber et al., 2002; Malish et al., 2023; Stanley et al., 1997). Our results and future research will enable us to reinforce existing patch connectivity localizing where chains of patches require restoration or completion, thereby enhancing species resilience to ongoing climate change.

4–4) Mitigation of climate change benefit primary threatened specialist species

Consistent with prior research, we predicted that temperature increase would increase the occupancy probabilities of thermal generalist species, whereas cold specialist species occupancy probabilities would decrease (Lindholm et al., 2015; Pallarés et al., 2020; Rosset & Oertli, 2011). Contrary to what we expected, our results showed that temperature increase, hydroperiod decrease and connectivity decrease had the same effects on thermal specialist and generalist species occupancy probabilities. We found similar results in studies of the effect of climate change on presence-absence distribution of amphibians and Insects. In fact, Shadle and al. (2023) compared experimentally climate change effects on habitat specialist wood frogs (Lithobates sylvaticus) and more generalist spring peepers (Pseudacris crucifer) (Shadle et al., 2023). They demonstrated that warming accelerates the duration to metamorphosis, while drying leads to diminished body size at metamorphosis in both species. Other authors demonstrated that the Insects distribution trends over time were not significantly affected by species' range size across Europe (Engelhardt et al., 2022). Finally, we found that the main difference between specialist and generalist species is not on the effect of temperature increase, drying and isolation on their occupancy probabilities, but more on the proportion of species from each group concerned by these effects. Indeed, the impacts of temperature increase were observed to affect the occupancy probabilities of more specialist species compared to generalists (71% versus 50%). We found similar results in connectivity effects (93% versus 75%). Consequently, the mitigation of climate change effects will be beneficial to a greater number of specialist species than generalists.

Author Contribution

Conceptualisation: MLH, FA, AB, TD. Developing statistical methods: MLH, AB. Data analysis: MLH. Preparation of figures and tables: MLH, ML. Conducting the research, data interpretation, and writing: MLH, FA, AB, ML, TD.

Acknowledgement

We thank Jake Diamond for his friendly review and Rosalie Bruel and Adrien Guerou for their valuable assistance in formalizing thermal data and providing accessible satellite data, respectively. We also thank the Savoie Mont Blanc Foundation, INRAE, Pole R&D ECLA and Lafuma for providing field equipment. We extend our appreciation to all those who accompanied us in the field, with special thanks to the Reserve guards (Asters-CEN74) and the volunteers. We are also grateful for funding by Rhône-Méditerranée and Corse Water Agency (grant number: 2022-0784), Auvergne Rhône-Alpes Region (grant number: 2000811401-18296 and 2100830401-18296), Haute-Savoie Department (grant number: CP-2020-0616), Regional Directorate for Environment, Development and Housing (DREAL) (grant number: EJ2103112539) and a NGO called Sympetrum group (GRPLS).

Data Availability

All data and scripts necessary to reproduce this analysis are freely available for all purposes (and can be copied, modified and distributed) via data INRAE repository: https://doi.org/10.57745/YFE6IJ

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Table 1. Occupancy and design sampling detection probabilities of Amphibia, macrophytes and Odonata species in our study sites (31 species). To calculate these values based on the 73 sampling sites, we use the MSOM accounting imperfect detection (det.formula=factor(stade)*factor(session)) and any occurrence covariate. The asterisk “*” identify specialist species. Species have probabilities of detection between 0.617 (CI: 0.342-0.856) and 0.997 (CI: 0.981-1.000). Specialist species were rare but had a probability of detection superior at 0.6: between 0.664 (CI: 0.238-0.946) and 0.990 (CI: 0.882-1.000).

No competing interests reported.

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Increased drying threatens alpine pond biodiversity more than temperature increase in a changing climate

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Materials and methods

2-1) Sampling design

2.2) Species sampling methods

2-2-1) Odonata

2-2-2) Amphibians

2-2-3) Macrophytes

2-2-4) Species classification.

2-3) Environmental covariates

2-3-1) Summer water temperature

2-3-2) Hydroperiod

2-3-3) Connectivity

2-4) Statistical methods

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