Research Article |
Corresponding author: Emanuel Rocchia ( ema.rocchia@gmail.com ) Academic editor: Alessandro Campanaro
© 2019 Cristiana Cerrato, Emanuel Rocchia, Massimo Brunetti, Radames Bionda, Bruno Bassano, Antonello Provenzale, Simona Bonelli, Ramona Viterbi.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Cerrato C, Rocchia E, Brunetti M, Bionda R, Bassano B, Provenzale A, Bonelli S, Viterbi R (2019) Butterfly distribution along altitudinal gradients: temporal changes over a short time period. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 91-118. https://doi.org/10.3897/natureconservation.34.30728
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Mountain ecosystems are particularly sensitive to changes in climate and land cover, but at the same time, they can offer important refuges for species on the opposite of the more altered lowlands. To explore the potential role of mountain ecosystems in butterfly conservation and to assess the vulnerability of the alpine species, we analyzed the short-term changes (2006–2008 vs. 2012–2013) of butterflies’ distribution along altitudinal gradients in the NW Italian Alps. We sampled butterfly communities once a month (62 sampling stations, 3 seasonal replicates per year, from June to August) by semi-quantitative sampling techniques. The monitored gradient ranges from the montane to the alpine belt (600–2700 m a.s.l.) within three protected areas: Gran Paradiso National Park (LTER, Sitecode: LTER_EU_IT_109), Orsiera Rocciavrè Natural Park and Veglia Devero Natural Park. We investigated butterflies’ temporal changes in accordance with a hierarchical approach to assess potential relationships between species and community level. As a first step, we characterized each species in terms of habitat requirements, elevational range and temperature preferences and we compared plot occupancy and altitudinal range changes between time periods (2006–2008 vs. 2012–2013). Secondly, we focused on community level, analyzing species richness and community composition temporal changes. The species level analysis highlighted a general increase in mean occupancy level and significant changes at both altitudinal boundaries. Looking at the ecological groups, we observed an increase of generalist and highly mobile species at the expense of the specialist and less mobile ones. For the community level, we noticed a significant increase in species richness, in the community temperature index and a tendency towards homogenization within communities. Besides the short time period considered, butterflies species distribution and communities changed considerably. In light of these results, it is fundamental to continue monitoring activities to understand if we are facing transient changes or first signals of an imminent trend.
Butterfly, community composition, mountain ecosystem, LTER, protected area
Global warming and land use changes are considered among the main threats to biodiversity (
Mountain ranges are very sensitive to environmental changes and global warming (
The alpine biodiversity has already responded to these factors. Upward shifts of alpine plants (
Although some responses are evident, few investigations have focused on alpine biodiversity temporal changes. As mentioned above, alpine environments are under threat, therefore more information is needed to understand how the main adverse factors (global warming and loss of open habitats) have affected, or are affecting, alpine biodiversity over time. Exploring temporal patterns of biodiversity is of great significance because future warming and related environmental changes are expected to cause substantial changes, not only in spatial distribution of species, but also in species turnover over time (
Long-term monitoring programs are fundamental tools to assess and monitor temporal changes of biodiversity (
Protected areas are a key part of conservation strategies to mitigate and monitor losses of biological diversity due to changes in climate and land use (
In 2006, a multi-taxa monitoring project in the NW Italian Alps was started (Biodiversity Monitoring Project), with the goal of analyzing long-term biodiversity changes in space and time along altitudinal transects in three protected areas (
Following the protocol developed and tested by the LTER site Gran Paradiso National Park (GPNP), seven taxa were monitored (Lepidoptera Rhopalocera, Orthoptera, Aves, Coleoptera Carabidae, Coleoptera Staphylinidae, Araneae, Hymenoptera Formicidae), using standardized, easy-to-apply and cheap sampling techniques. In 2007, the protocol was adopted by two other protected areas (Orsiera Rocciavré Natural Park, ORNP and Veglia Devero Natural Park, VDNP). The protocol has been repeated every 5 years (2 yr monitoring – 4 yr stop), and as a result, data from two sampling periods are now available (1st season, 2006–2008; 2nd season, 2012–2013).
In this framework, we focused on butterfly data deriving from the two sampling periods (1st, 2006–2008; 2nd, 2012–2013) of the Biodiversity Monitoring Project. Butterflies are an ideal indicator taxon (
The main aim of our research consisted in assessing changes of butterflies’ species distribution and community composition along altitudinal gradients over a short time period. We investigated butterflies’ responses across multiple levels of biological organization following a hierarchical approach, in order to assess heterogeneity or consistency of changes among different levels. As a first step, we analyzed temporal changes of distribution for single species and for species classified into ecological groups (species level). Then, we explored temporal variation of species richness and of community composition (community level). We finally discussed the results in the light of the possible drivers of change within our study areas.
Our study was carried out in three protected areas in the NW Italian Alps: Gran Paradiso National Park (GPNP; 720 km2; 45°31'N, 7°19'E), Orsiera Rocciavrè Natural Park (ORNP; 110 km2; 45°3'N, 7°19'E) and Veglia Devero Natural Park (VDNP; 86.2 km2; 46°12'N, 8°14'E). GPNP is part of the Long-Term Ecological Research network (LTER, Sitecode: LTER_EU_IT_109).
All areas are characterized by mountain (alpine) conditions with vegetation ranging from mixed forest to alpine prairies and glaciers. The three parks have similar mean elevations and vegetation characteristics, but differ slightly in terms of climatic regime (the highest monthly precipitation and lowest annual mean temperature are found in VDNP).
A total of 11 altitudinal transects were set (one for each valley for each of the three parks) ranging from a minimum of 600 to a maximum of 2700 m a.s.l. The transects cover different altitudinal ranges and, overall, they involve three vegetation belts (montane, subalpine, alpine; Suppl. material
We sampled butterflies using linear transects along the diameter of the plot (200 m in length), walked along at uniform speed. According to the protocol by
In each plot, we also recorded the microclimatic conditions placing data-loggers (Thermochron iButton, DS1922L, Maxim, Sunnyvale, CA, U.S.) which recorded the air temperature every hour throughout the field season. Loggers were located in the center of each plot, at least 1 m above ground and were covered with a white shield.
By using land cover data derived from aerial photos and validated in the field (
Moreover, during each sampling session, we noted, for each plot, land management data (dominant land use) considering three categorical variables: grazing, mowing and no activities.
Species level
The first step of our analysis focused on distributional changes of butterflies’ species and groups of species. Following ecological requirements, species could change distribution along altitudinal gradient, either vertically or horizontally with the colonization or the loss of sites at similar altitudes.
To obtain a comprehensive framework on butterflies species distribution changes, we firstly assessed temporal changes in plot occupancy to detect an overall signal of expansion or retraction without considering an altitudinal directionality, then, we investigated altitudinal range changes between sampling periods.
Changes in plot occupancy
We defined occupancy as the number of plots occupied by each species in each sampling session (1st vs 2nd) and compared it by using a t-test for paired samples (significance level assessed after 999 randomizations, following
To identify which group of species changed the most over time, we analyzed if the occupancy increased/decreased equally between functional groups. We compared the changes in the number of plots per species (Δ plot, 2nd sampling session minus 1st sampling session), between the ecological groups of conservation interest by using non-parametric tests (Kruskal-Wallis or Mann-Whitney tests, depending on the number of ecological categories). Following the classification proposed by
To assess if individual species changed their occupancy of plots over time (if the frequency of presence/absence was statistically different between the two sampling periods), we applied the McNemar’s test for paired nominal data in a 2×2 table (
Altitudinal range changes
We described the altitudinal range of each species with the following parameters:
– altitudinal optimum (mean and median value);
– higher limit (absolute maximum, 90th percentile);
– lower limit (absolute minimum, 10th percentile).
To quantify the amount of change, we compared these parameters between sampling periods using the t-test for paired samples. To compare non-parametric data we assessed significance level after 999 randomizations (
To test for species responses over time along the altitudinal range, we used the indicator species analysis (IndVal method) proposed by
For this purpose, a species matrix (150 species) was created using the average abundance data between years for each plot and sampling period. A logarithmic transformation was applied to reduce the influence of extreme abundance values (
We grouped together sampling sites into four altitudinal bands (1: 550–1200 m; 2: 1250–1650 m; 3: 1700–2150 m; 4: 2200–2700 m) and two time periods (season I, 2006–2008; season II, 2012–2013).
This methodology allowed us to test the association of each species with the altitudinal bands over time, resulting in 255 possible groups, but we restricted our analyses to the 62 groups that could be interpreted ecologically in terms of altitudinal constancy or shift (Suppl. material
Community level
Species’ responses may affect communities’ structure and composition (
Species richness
To evaluate if sampling efficiency remained comparable throughout sampling periods, and if we consequently can carry out comparisons at the assemblage level, we performed a preliminary analysis of species richness estimators and sample completeness (expressed as the proportion of the observed species richness to the averaged estimated species richness). We calculated the averaged estimated species richness, through the mean value of four abundance-based estimators (ACE Abundance-based Coverage Estimator; Chao1; Jacknife1; Jacknife2), and correlated it with the observed species richness. Correlation was high and significant in both sampling periods (Spearman test; 1st sampling period, ρ = 0.937, p < 0.001; 2nd sampling period, ρ = 0.918, p < 0.001). Sample completeness was also high in both seasons (mean sample completeness among plots ± standard error; 1st = 67.59 ± 1.56; 2nd = 72.17 ± 1.31). Consequently, we considered the sampling efficiency between sampling periods as comparable, allowing us to carry out direct data comparisons.
To analyze how species richness per plot changed over time, we compared species richness between sampling periods using the t-test for paired samples (significance level assessed after 999 randomizations, following
To understand if changes in species richness were mainly related to characteristics of specific plots, we analyzed it as a function of the following variables: altitude, temperature, geographic location, dominant vegetation cover (habitat type), and dominant land use. We considered the rate-of-change as a dependent variable (hereinafter ROC), defined as the differences in species richness between sampling periods (1st, 2006–2008; 2nd, 2012–2013), divided by the species richness of the first sampling period (1st, 2006–2008). We analyzed the ROC through linear regression, and we compared variables in a multi-model context, according to two criteria: (i) avoiding the simultaneous use in the same model of highly correlated predictors (Spearman’s rs >0.5); (ii) choosing predictors to represent biologically meaningful combinations of predictive variables, and consequently avoiding data dredging. All models were compared with the null model (intercept only) and all continuous variables were standardized, to enable comparisons between variables. The multivariate model selection was performed using Akaike information criteria, in its form corrected for small samples (AICc). As a measure of goodness-of-fit, we calculated the adjusted R2. These analyses were performed by the R package ‘MuMIn’ (
Community composition
We analyzed community compositions by testing for changes both in location (significant changes in community composition per site over time) and dispersion over the years (significant changes in observed differences in community composition between sites, over time). Changes in location were tested by applying non-parametric MANOVA to Bray-Curtis distance matrixes, to test if the multivariate centroids of species composition were similar in the two groups, or not (
Community Temperature Index
The term “Species Temperature Index” (STI), refers to a quantitative description of the realized climatic niche of a species (
We used STI to calculate the “Community Temperature Index” (CTI), quantified as the mean STI of all the species present in a given community. We calculated the CTI for each community (plot) and sampling period, and we analyzed the changes in CTI over time.
We compared CTI values between sampling periods by using a t-test for paired samples. As for the case of ROC, we analyzed the temporal changes in CTI (Δ CTI) by a linear regression in a multi-model context regression, as a function of the same environmental variables and models.
Temperature analysis
To quantify changes in weather conditions between sampling periods, identified as a possible driving factor of changes in biodiversity, we compared monthly temperatures (from June to September), as a function of year, altitude and additive or interactive effects. Models were compared with the null one (intercept only) with Linear Mixed Effect Models (using the plot as random effect) and all continuous variables were standardized, to allow comparisons between variables. The multivariate model selection was performed using Akaike information criterion, in its form corrected for small samples (AICc). The analysis has been carried out using the R packages ‘lme4’ (
All the analyses were carried out with the R software, version 3.3.3 (
Considering all sampling sites and time periods, we recorded 150 butterfly species, of which 133 were common to both periods, 5 were exclusive to the first sampling session and 12 were exclusive to the second sampling session (Suppl. material
Changes in plot occupancy
We observed a general increase in mean occupancy levels (n = 150, t = -8.15, p = 0.001; plot/species 1st = 8.85 ± 0.74, 2nd = 12.50 ± 0.96, change = 3.65 ± 0.45).
The occupancy did not change equally between ecological groups. Concerning feeding specialization, strictly monophagous species differed from the other feeding groups (KW test, χ2 = 9.82, df = 3, p = 0.020), even showing a slight decrease in the number of plots per species (polyphagous = 3.26 ± 0.99, one family = 4.10 ± 0.59, one genus = 3.68 ± 1.05, monophagous = -1.17 ± 0.98). We also recorded significant differences regarding the relationship with altitude. Altitudinal specialists increased less than the generalists (KW test, χ2 = 13.13, df = 2, p = 0.001; generalists = 6.12 ± 1.03, medium = 3.04 ± 0.48, specialists = 2.32 ± 1.55) and also high altitude species showed a significantly less marked increase (MW test, W = 1070, p = 0.013, high altitude = 1.76 ± 1.32, others = 4.02 ± 0.46). “Shade loving” species showed the highest increase in mean occupancy levels (MW test, W = 2269, p = 0.041, “shade loving” = 4.53 ± 0.67, “sun loving” = 2.71 ± 0.57).
No significant differences have been recorded considering habitat (KW test, χ2 = 2.04, p = 0.564) preferences and dispersal capacity.
We identified 17 species which significantly increased their distribution within the study area, of which 7 were also significant after Bonferroni correction. No species significantly decreased its occupancy level (Table
Species which significantly changed their area of occupancy, expressed as the number of plots of presence during the first (Plot1) and the second (Plot2) sampling period. The number of plots gained or lost (Δ), the p-value of the McNemar’s test (p-value) and its significance level (* p < 0.05, ** p < 0.001) after Bonferroni correction (p-adj) are also expressed.
Species | Plot 1 | Plot 2 | Δ | p-value | p-adj |
---|---|---|---|---|---|
Cyaniris semiargus | 8 | 36 | 28 | <0.001 | ** |
Pieris bryoniae | 16 | 43 | 27 | <0.001 | ** |
Cupido minimus | 22 | 43 | 21 | <0.001 | ** |
Aglais urticae | 37 | 55 | 18 | 0.004 | |
Callophrys rubi | 9 | 27 | 18 | 0.001 | * |
Pyrgus malvoides | 20 | 38 | 18 | <0.001 | * |
Argynnis niobe | 20 | 36 | 16 | <0.001 | ** |
Maculinea arion | 10 | 24 | 14 | 0.001 | |
Colias alfacariensis | 2 | 14 | 12 | 0.001 | * |
Lasiommata petropolitana | 6 | 18 | 12 | 0.003 | |
Pieris rapae | 20 | 32 | 12 | 0.009 | |
Polyommatus coridon | 31 | 43 | 12 | 0.001 | |
Eumedonia eumedon | 3 | 14 | 11 | 0.001 | |
Pyrgus cacaliae | 2 | 13 | 11 | 0.010 | |
Erebia euryale | 34 | 44 | 10 | 0.009 | |
Pyrgus carthami | 1 | 11 | 10 | 0.003 | |
Argynnis aglaja | 33 | 42 | 9 | 0.009 |
Altitudinal range changes
We observed significant changes both at the minimum and at the maximum boundary of altitudinal range of the species. At the lower altitudinal limit, we observed a significant decrease in the absolute minimum value (t-test, n = 133, t = 3.03, p = 0.004, change = -96.62 m ± 31.85 m). At the higher altitudinal limit, we observed an increase in both the absolute maximum (t-test, n = 133, t = -3.08, p = 0.006, change = 75.19 m ± 24.01 m) and in the 90th percentile (t-test, n = 133, t = -2.63, p = 0.014, change = 55.15 m ± 20.97 m).
We also observed significant differences in the changes of the altitudinal range between ecological groups. In particular, high altitude species, compared to the others, showed a significant increase in the mean (MW test, W = 1657.5, p = 0.041; high altitude = 59.21 ± 21.24, others = -11.07 ± 18.09), median (MW test, W = 1657, p = 0.040; high altitude = 59.37 ± 19.90, others = -8.72 ± 17.58) and 10th percentile values (MW test, W = 1737, p = 0.012; high altitude = 68.12 ± 50.24, others = -72.11 ± 28.05).
“Shade loving” species lowered their minimum (MW test, W = 2738.5, p = 0.014; shade = -164.18 ± 45.34, sun = -28.03 ± 43.47) and 10th percentile values (MW test, W = 2702, p = 0.027; shade = -74.10 ± 36.66, sun = -19.09 ± 34.14) compared to the “sun loving” species.
Strongly vagile species increased their minimum boundary, while weakly and medium vagile species lowered their minimum boundary (KW test, χ2 = 8.34, df = 2, p = 0.015; high = 139.29 ± 110.22, medium = -103.49 ± 37.93, low = -178.79 ± 62.12).
We identified 87 species associated with one of the selected group combinations (Fig.
Changes along altitude. The number of significant IndVal species are shown for each combination of sampling periods (I = 1st sampling period, II = 2nd sampling period) and altitudinal bands (1, 550–1200 m; 2, 1250–1650 m; 3, 1700–2150 m; 4, 2200–2700 m). Different colours represent different statuses. Species that lost their indicator value with specific altitudinal bands during the second period (disappear; black, with continuous border); species stable over time (stable; white, with continuous border); species that showed, in the second sampling period, new significant associations for higher altitudinal bands, at the same time maintaining the association to the altitudinal bands of the first sampling period (altitudinal expansion; grey, with continuous border); species that lost their association to the lower altitudinal bands and gained new associations to higher ones during the second sampling period (altitudinal upward shift; grey with dotted border); species that became IndVal of specific altitudinal bands during the second sampling period (spread; white, with dotted border).
Species richness
Species richness significantly increased from the first to the second sampling period (t-test, n = 62, t = -9.76, p = 0.001, change = 8.82 ± 0.90).
The analysis of the ROC showed a significant effect of both land cover and land use: wooded habitats and managed plots increased the most (Table
Best linear regression model for Rate Of Change (ROC). Coefficients (± standard error) of the selected variables are indicated in the cells. Adjusted r squared is indicated as a measure of goodness of fit. alt = altitude; alt2 = altitude (second order); park = geographic location; rme = change in mean temperature; rmi = change in minimum temperature; vegetation = dominant cover type (land cover); use = land use; Tmin = mean seasonal minimum temperature during the first season. adj r2 = adjusted R2. ** p < 0.01; * p < 0.05; ° p = 0.06.
ROC | ||
---|---|---|
alt | ||
alt2 | ||
park | ||
rme | ||
rmi | ||
vegetation | ||
ecotone | -0.394 ± 0.141 ** | |
meadow | -0.277 ± 0.120 * | |
rock | -0.349 ± 0.185 ° | |
use | yes | 0.257 ± 0.102 * |
Tmin | ||
R2 adj | 20.64 |
Community composition
Species composition significantly differed between time periods, but the obtained R2 is extremely low, meaning a scarcely relevant change (non-parametric MANOVA, F-value = 5.87, r2 = 1.91, p = 0.001). More interestingly, we observed a significant change in dispersion between sampling periods, with a lower dispersion around the median during the second sampling period (distance to centroids 1st sampling period = 0.584, 2nd sampling period = 0.528, p = 0.001; indicating a tendency towards homogenization; Fig.
Homogenisation of community composition. Box-plot of the distances to the centroid of community composition, during each sampling period. A reduction in species heterogeneity at the community level from the first to the second period can be clearly seen.
Community Temperature Index
We observed that the CTI significantly increased from the first to the second sampling period (t = -3.59; p = 0.001), indicating a common trend toward thermophily in all the sampled areas. Moreover, interestingly, we observed that the change in CTI over time was mainly dependent on the geographic position of the sampling plots (Fig.
Changes in Community Temperature Index. We observed significant differences in ΔCTI between geographic locations (protected areas). The parks located in colder areas clearly show the highest increase in CTI.
Temperature
The analysis of field-recorded temperatures indicated significant differences between sampling periods, concerning the monthly mean and minimum temperatures observed during July, August and September (Table
Results from the best linear model indicating the role of altitude and year (and their interaction) for determining differences between sampling seasons for each month and temperature parameters (Temp).
Temp | Month | Intercept | Altitude | Year | Altitude*Year | R2 |
---|---|---|---|---|---|---|
Mean | June | 11.43 | -2.46 | 80.2 | ||
July | 13.22 | -2-72 | 0.11 | 91.9 | ||
August | 12.65 | -2.62 | 0.2 | 87.8 | ||
September | 8.39 | -2.45 | 0.28 | 89.2 | ||
Max | June | 17.08 | -1.76 | 46.8 | ||
July | 20.16 | -2.03 | -0.14 | 50.5 | ||
August | 19.35 | -1.92 | 53.8 | |||
September | 15.38 | -1.50 | 42.4 | |||
Min | June | 7.33 | -2.88 | 83.7 | ||
July | 8.3 | -3.08 | 0.25 | 96 | ||
August | 8.29 | -2.96 | 0.26 | 90.6 | ||
September | 4.22 | --2.88 | 0.37 | 95.9 |
Long-term monitoring, which is periodically and systematically repeated over time (usually decades), is crucial for correctly understanding the variables and mechanisms that determine species’ distribution responses and patterns of community composition as a consequence of climate and land use changes (
Although our research is based on a short time frame, it is important to consider the role of potential driving factors of change, by analyzing climatic and land cover changes (whenever occurring) as explicative predictors.
Concerning variations in temperature between sampling periods (2006–2008 vs 2012–2013), we observed that the second sampling period was warmer than the first one. These observed differences are coherent with the trend recorded by
Unfortunately, no punctual and detailed data on precipitations were available to introduce them into the analytical framework. However, analyzing data from the official weather stations of the Regional Meteorological Service located in the study areas, we observed an interesting pattern in snow cover, characterized by a reduction in the number of days with snow and by a seasonal shift in snow cover towards the spring, in particular since 2005. Such conditions are indicative of a warmer climate and they correspond to a common pattern for the entire Alpine chain (
Due to the short time frame of analysis, we considered the main habitat type (dominant land cover) as a constant variable because no huge structural changes have potentially occurred. However, because of the natural evolution of vegetation following climatic changes and land abandonment, minimal but effective land cover changes in terms of microhabitat scale (e.g. shrubs encroachment) can be taken into account (
Some sampling sites (plots) were characterized by low-intensity and sustainable grazing activities without any changes between sampling periods.
No other direct land use changes and habitat alterations occurred within our study sites because of the park status and LTER accredited site of our study area.
Plot occupancy
Despite the short time-frame under analysis, butterflies showed a general increase in mean occupancy levels but species-specific plot occupancy changes differed markedly among species. However, when grouping species according to homogeneous ecological traits, we observed consistent distributional responses, in accordance with several studies that have proved the effect of ecological and life-history traits in shaping species distribution (
Monophagous, altitudinal specialists and high-altitude species appeared to be more limited than other species. These categories comprise species with a high level of specialization, consequently less prone to colonize new environments, even if climatic or environmental constraints are relaxed. In particular, monophagous species are strictly limited by the presence and the quality of their single larval host plant, and are already observed and predicted to be highly vulnerable to climatic/environmental changes (
A significantly higher increase in plot occupancy by “shade-loving” species compared to other species can be associated with a tendency towards a higher coverage of shrubs in open areas at low and medium altitude. In the European Alps, the effect of climate change is regionally affected by human activities. Cattle grazing in the alpine pastures has been decreasing throughout the past century, allowing rapid recolonization by trees and shrubs, where the treeline had been artificially lowered (e.g.,
Interestingly, no species significantly decreased its area of occupancy, but 17 species increased their occupancy area, 7 of which in a particularly marked manner, still significant after Bonferroni correction (Table
Aglais urticae and Argynnis niobe showed pronounced increase in plot occupancy. Our results are in contrast with other researches that reported a decline of these two species in other parts of Europe (
Altitudinal range changes
As for plot occupancy analysis, we noticed a general process of altitudinal change, considering all butterflies species together.
Looking at the ecological groups, we observed that, even along altitude, generalist and highly mobile species were more prone than specialist and less mobile ones to change their distributional ranges. Particularly, high altitude species showed a significantly higher increase in their mean, median and 10th percentile altitudinal parameters, corresponding to a reduction in their lower altitudinal boundaries and in the surface available. Those species are already limited in their distribution. Their presence is, in many cases, limited by minimum temperatures (
Concerning altitudinal patterns, we used the IndVal test to identify species associated with specific altitudinal zones. Our analysis highlighted 52 species associated with one or more altitudinal zones at both time periods: these species are widely spread within their altitudinal zone, and stable over the investigated period, consequently representing species which – in the future – could be monitored to investigate important altitudinal changes. For 12 species, we observed new significant associations for higher altitudinal bands (altitudinal expansion). We recorded a change in association from a lower to a higher altitudinal band (upward shift) only for one species, Celastrina argiolus, which was associated with the first altitudinal zone (550–1000 m) during the first sampling period, while during the second sampling period, it resulted characteristic for the second zone (1250–1650 m) only (see statistical analyses – altitudinal range changes for the description of the relationships between IndVal association and altitudinal changes). It is a species that favors woodland and shade, and probably an increase in minimum temperature with an increase in the shrub land coverage had an important effect in the shift of this species.
In contrast, other studies in European countries observed on the opposite a higher amount of species shifting their altitudinal distribution, disappearing from low elevation sites and colonizing higher altitudes (e.g.,
Finally for 19 species, the changes in their distribution determined new associations for specific altitudinal bands during the second sampling period (spread). These species represent important taxa to be followed over time, to understand if this pattern represents the first signal of an upward expansion.
Species richness
We observed significant changes in species richness per plot within the analyzed period. Butterfly communities are known to quickly change their arrangement due to environmental changes (
We observed a clear and significant increase in species richness within our temporal frame, mainly related to land cover and land management. The highest rate of change was clearly evident in the wooded areas (woodland clearings), while ecotones (transitional areas dominated by shrubs, and mainly located inside the subalpine belts) showed the lowest rate of change. Other authors also observed similar results, although they were mainly related to individual species abundance. For example,
In grazed areas, we observed an increase in species richness that was twice as much as in the unmanaged area. This can be probably explained by the low-intensity, sustainable grazing of these sites. Although intense grazing activities can be detrimental for butterflies (
Community composition
Although we observed major differences in species distribution between our sampling periods, we did not observe significant differences in community composition. These results were probably due to a consequence of the short time-frame of analysis.
Studies that have demonstrated variation in butterfly community composition clearly take into account longer time-frames (e.g.,
For example, a similar change in community composition over time, accompanied by an increase in community similarity, has been observed in the data analysis from the UK Butterfly Monitoring Scheme over a period of 20 years (
Community Temperature Index
The observed increase in CTI tended to derive from the increase in mean occupancy levels recorded by a large number of generalist, more termophilous species in the second sampling period compared to the first one. This phenomenon was particularly marked in the coldest areas, representing a potential threat for high altitude and microthermic species. Indeed, the generalist species successfully colonizing a higher altitude from a lower one can outcompete the more specialized and vulnerable species of the alpine belt, challenging the survival of their local populations in the long term. Our results are consistent with an increase in CTI already observed in other geographic areas (e.g., in Greece by
In recent decades, changes in species distribution and composition of communities have already been analyzed in some parts of the Alps. Nevertheless, data usually concern plants (e.g.
Responses to climate and habitat changes vary widely between species of the same communities (
Sampling units placed in well-specified areas represent a more appropriate tool for investigative purposes (e.g.,
Interestingly, and somewhat alarmingly, our results suggest that, even if the time-frame under analysis is relatively short, we already observed considerable changes, in particular considering that the research was carried out in protected areas and LTER sites where habitat alteration by direct human effects is strongly reduced.
To summarize, in just 5 years, we observed:
– a general increase in mean occupancy level and in species richness of butterflies;
– no significant changes in the mean altitudinal optimum, but significant changes at both altitudinal limits.
Moreover, the observed changes differed across species, determining:
– an increase in shared species (a tendency to homogenization) within communities, even if the overall community composition did not change;
– a significant increase in Community Temperature Index (CTI).
Considering these results, it is now even more important to continue our monitoring in order to understand in the near future if our observed patterns represent only transient changes or are the first signals of an imminent trend.
We are grateful to the Park Directors for logistic support and data availability and to all Park Wardens, students and researchers for their essential help during the field work. The project leading to this research was coordinated by Gran Paradiso National Park and partially funded through the “Monitoraggio della biodiversità in ambiente alpino” grant (Progetti di Sistema ex cap.1551), provided by the Italian Ministry of the Environment (Ministero dell’Ambiente e della Tutela del Territorio e del Mare). This research also received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 641762 “Improving Future Ecosystem Benefits through Earth Observations” (Ecopotential) and the Project of Interest “NextData” of the Italian Ministry of Education, University and Research. We thank Sönke Hardersen, Pietro Brandmayr and Martin Konvička for their careful revisions on earlier versions of this paper.
Supplementary data