Spatial variation in early‐winter snow cover determines local dynamics in a network of alpine butterfly populations

Roland, Jens; Filazzola, Alessandro; Matter, Stephen F.

doi:10.1111/ecog.05407

Cited by 9 publications

(6 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Roland & Matter (2016) found, for the butterfly Parnassius smintheus Doubleday, 1847 in the Canadian Rocky Mts, negative effects of November temperature extremes, and positive effects of warm springs. November ( i.e ., early winter) snow cover was the best predictor of the following seasons’ abundances ( Roland, Filazzola & Matter, 2021 ). Climate effects differing across species or phenotypes were demonstrated by Buckley & Kingsolver (2012) , who modelled the interaction between the time available for flight and egg viability in two alpine Colias spp.…”

Section: Discussionmentioning

confidence: 99%

“…As a rule, climate variation is buffered by diverse microclimates in mountain environments ( Nieto-Sanchez, Gutierrez & Wilson, 2015 ; Roland, Filazzola & Matter, 2021 ; Turlure et al, 2010 ; Wilson et al, 2015 ) and the more abundant a population, the more individuals will likely locate microclimatically suitable sites. This provides grounds for moderate optimism regarding the future of the studied populations and other insects of temperate zone middle-high mountains.…”

Section: Discussionmentioning

confidence: 99%

“…It influences food intake ( Forrest & Thomson, 2011 ), development rate ( Ayers & Scriber, 1994 ), and pressure from natural enemies ( Corcos et al, 2018 ). All these factors ultimately affect adult abundance ( Roland, Filazzola & Matter, 2021 ) and flight period timing ( Gutiérrez & Wilson, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low winter precipitation, but not warm autumns and springs, threatens mountain butterflies in middle-high mountains

Konvička

Kuras

Lipárová

et al. 2021

PeerJ

View full text Add to dashboard Cite

Low-elevation mountains represent unique model systems to study species endangered by climate warming, such as subalpine and alpine species of butterflies. We aimed to test the effect of climate variables experienced by Erebia butterflies during their development on adult abundances and phenology, targeting the key climate factors determining the population dynamics of mountain insects. We analysed data from a long-term monitoring of adults of two subalpine and alpine butterfly species, Erebia epiphron and E. sudetica (Nymphalidae: Satyrinae) in the Jeseník Mts and Krkonoše Mts (Czech Republic). Our data revealed consistent patterns in their responses to climatic conditions. Lower precipitation (i.e., less snow cover) experienced by overwintering larvae decreases subsequent adult abundances. Conversely, warmer autumns and warmer and drier springs during the active larval phase increase adult abundances and lead to earlier onset and extended duration of the flight season. The population trends of these mountain butterflies are stable or even increasing. On the background of generally increasing temperatures within the mountain ranges, population stability indicates dynamic equilibrium of positive and detrimental consequences of climate warming among different life history stages. These contradictory effects warn against simplistic predictions of climate change consequences on mountain species based only on predicted increases in average temperature. Microclimate variability may facilitate the survival of mountain insect populations, however the availability of suitable habitats will strongly depend on the management of mountain grasslands.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Low winter precipitation, but not warm autumns and springs, threatens mountain butterflies in middle-high mountains

Konvička

Kuras

Lipárová

et al. 2021

PeerJ

View full text Add to dashboard Cite

show abstract

“…Moreover, climatological applications and studies usually focus either on the meteorological winter DJF (Scherrer and Appenzeller, 2006) or on the 6 months NDJFMA (Marcolini et al, 2017). However, winter in the Alps is not simply restricted to these 3 or 6 months.…”

mentioning

confidence: 99%

Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements

et al. 2021

View full text Add to dashboard Cite

Abstract. Daily measurements of snow depth and snowfall can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are usually located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall, and snow onset and disappearance dates, which are calculated for various seasons (December to February (DJF), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential variability. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA, which show median variations of less than 5 % for all indicators. Local-scale variability ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. The highest percentage (90 %) of station pairs with variability of less than 15 % is observed for days with snow on the ground. The lowest percentage (30 %) of station pairs with variability of less than 15 % is observed for average snow depth. Median differences of snow disappearance dates are rather small (3 d) and similar to the ones found for snow onset dates (2 d). An analysis of potential sunshine duration could not explain the higher variabilities in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3 d new snow sum, which are often used for avalanche prevention measures. The found return levels are within each other's 95 % confidence intervals for all (but three) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of variabilities of commonly used snow indicators and extremal indices. Knowledge about such variabilities in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

show abstract

“…In contrast, for many ecological and hydrological applications the melting spring snow cover is the main interest (e.g. (Brown and Robinson, 2011;Livensperger et al, 2016;Zampieri et al, 2015)) and for some applications even the onset of the snow cover (Roland et al, 2021). We therefore analyse the variations of important and commonly used snow climate indicators for seasonal effects.…”

Section: Introductionmentioning

confidence: 99%

Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements

Buchmann¹,

Begert²,

Brönnimann³

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Measurements of snow depth and snowfall on the daily scale can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are mostly located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall as well as snow onset and disappearance dates, which are calculated for various seasons (December to February (DFJ), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential uncertainty. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA which show median uncertainties of less than 5 % for all indicators. Local-scale uncertainty ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. Highest (lowest) percentage of station pairs with uncertainty less than 15 % is observed for days with snow on the ground with 90 % (average snow depth, 30 %). Median differences of snow disappearance dates are rather small (three days) and similar to the ones found for snow onset dates (two days). An analysis of potential sunshine duration could not explain the higher uncertainties in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3-day new snow sum, which are often used for prevention measures. The found return levels are within each other’s 95 % confidence intervals for all (but two) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and extremal indices. Knowledge about such uncertainties in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

show abstract

Spatial variation in early‐winter snow cover determines local dynamics in a network of alpine butterfly populations

Cited by 9 publications

References 73 publications

Low winter precipitation, but not warm autumns and springs, threatens mountain butterflies in middle-high mountains

Low winter precipitation, but not warm autumns and springs, threatens mountain butterflies in middle-high mountains

Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements

Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements

Contact Info

Product

Resources

About