Impacts of a millennium drought on butterfly faunal dynamics

Impacts of a millennium drought on butterfly faunal dynamics Background: Climate change is challenging plants and animals not only with increasing temperatures, but also with shortened intervals between extreme weather events. Relatively little is known about diverse assemblages of organisms responding to extreme weather, and even less is known about landscape and life history properties that might mitigate effects of extreme weather. Our aim was to address this knowledge gap using a multi-decadal dataset of 163 butterfly species that recently experienced a millennium-scale drought. To understand faunal dynamics in the context of the millennium drought, we investigated the behavior of phenology (including date of first flight), species richness and diversity indices through time at 10 study sites spanning an elevational gradient. Linear models were developed to understand the differential sensitivity of butterflies to climate at low and high elevations. Results: Dates of first flight advanced across the elevational gradient during the drought, leading to an overall expansion of the flight window at low elevations and a compression of the flight window in the mountains. The number of species observed per year increased at lower elevations but decreased at higher elevations, apparently as a consequence of extreme sensitivity to hot and dry conditions. Conclusion: Montane populations may be more sensitive to climatic extremes than expected based on availability of microclimates and spatial heterogeneity, while low-elevation populations (despite existing in degraded habitats) are buffered by life history plasticity. Keywords: Butterflies, Climate change, Drought, Extreme weather, Mediterranean climate, Phenology Background frequently, and across greater spatial scales, yet there are Extreme weather events are occurring with increasing few studies addressing the effects of extreme drought on severity and frequency in recent decades, a trend linked insects [9]. In general, warmer and drier conditions are to ongoing anthropogenic climate change and shifts in known to have both positive and negative effects on in- atmospheric circulation [1, 2]. While many studies of sect populations. Positive effects can be associated with species and ecosystems have looked at climate impacts more rapid development and less time spent in vulner- by using the average change in historical or projected able juvenile stages [10], while negative effects include climatic conditions [3, 4], we know far less about the reduced nectar availability and early senescence of host ecological impacts of extreme weather events on wild or- plants [11]. Here we take advantage of decades of data ganisms [5–7]. In part this is due to the regional geog- on 163 butterfly species across an elevational gradient in raphy in which extreme weather events occur, which Northern California (Fig. 1a) to investigate the effects of requires baseline biological data as well as appropriately- a recent, extreme drought on butterfly populations. The scaled climatological data for a particular region prior to years 2011 to 2015 were characterized by both record- an event [8]. Droughts have been occurring more setting high temperatures in California and low levels of precipitation, which combined to produce water deficits * Correspondence: forister@gmail.com that were without precedent in more than 1000 years of Department of Biology, Program in Ecology, Evolution and Conservation reconstructed climatic records [12]. Biology, University of Nevada, Reno, NV 89557, USA Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Forister et al. Climate Change Responses (2018) 5:3 Page 2 of 9 Fig. 1 a Elevational profile of Northern California (left) and map of the same area (on the right) with ten study sites indicated on both; site names as follows, from west to east: Suisun Marsh (SM), Gates Canyon (GC), West Sacramento (WS), North Sacramento (NS), Rancho Cordova (RC), Washington (WA), Lang Crossing (LC), Donner Pass (DP), Castle Peak (CP) and Sierra Valley (SV). b Average dates of first flight and c last flight across species at each location and year. d Average flight days, which are the average fraction of days individual species are observed per year. In panels b, c, and d, color coding for individual lines corresponds to sites as in panel a, and the data are shown as z-standardized values. Grey rectangles in panels b, c, and d, indicate the major drought years from late 2011 into 2015 Investigations of butterflies at our focal sites have re- source populations [15]. Previous analyses of abiotic ef- ported that a majority of populations at the lowest eleva- fects have noted responses to weather that were heteroge- tions have been in decline since at least the mid-1990s neous and idiosyncratic among sites and species [16, 17]. [13], which has been attributed to changes in land use For example, associations with climatic variables often dif- and warming temperatures [14]. Populations at higher fer in sign among congeneric species [18], and even elevations, in contrast, have shown relatively less direc- among populations of a single species [17]. tional change over time, with the exception of a decline The extremity of the 2011–2015 drought provides a in more dispersive, disturbance-associated species that unique opportunity to study abiotic effects on butterfly rely on demographic connections with lower-elevation populations that have already been well characterized in Forister et al. Climate Change Responses (2018) 5:3 Page 3 of 9 terms of climatic relations and long-term demographic For a subset of years and sites, absolute counts of indi- trends. Specifically, we asked the following: 1) does an vidual butterflies (by species) were taken in addition to extraordinary, multi-year drought elicit a faunal response the presence/absence data; this was done at the 5 lowest that is extreme relative to faunal behavior in previous elevation sites starting in 1999. These data are used here dry years? 2) Are impacts on the fauna consistent or di- to investigate the dynamics of the low elevation butter- vergent across elevations? A theoretical expectation is flies during the drought years, specifically the relation- that organisms living in more heterogeneous environ- ship between phenological shifts, changing abundance ments should be more resilient to extremes of temporal and dependence on irrigation. For the latter (dependence variation [19, 20]. We predicted that butterflies at mon- on irrigation), one of us (AMS) ranked species a priori tane sites would be robust relative to populations at (without knowing the results of analyses) based on nat- lower elevations in landscapes that are both less spatially ural history observations. Dependency on irrigated areas variable and already impacted by a history of human ac- was categorized as follows: 1), butterfly species that are tivity. 3) Finally, we asked if population-level responses essentially independent of irrigation; 2), species that use to drought are mediated by phenological shifts. Species irrigated, non-native hosts in some areas as well as na- that are able to begin activity earlier in the spring might tive, non-irrigated hosts in other areas; 3), species that reach higher population densities [21, 22], potentially use irrigated, non-native hosts in at least one of multiple offsetting detrimental drought effects. Another possibil- generations; and 4), species that are completely ity is that ectotherms exposed to longer growing seasons dependent on irrigated, non-native hosts. could fall into a developmental trap by which extra gen- erations fail because of insufficient time [23]. Weather variables Analyses included the following weather variables: max- imum and minimum daily temperatures, total precipita- Methods tion, and a sea surface temperature variable associated Butterfly data with regional conditions [17]. Following previous analyses Ten study sites (Fig. 1a) were visited biweekly (every [15], maximum and minimum temperatures were aver- 2 weeks) by a one of us (AMS) for between 45 and aged and precipitation was totaled from the start of 29 years, depending on the site, and only during good September of the previous year through August of the “butterfly weather” when conditions were suitable for in- current year. Previous fall through current summer is a sect flight (nearly year round at low elevations, and during useful climatological time period in a Mediterranean cli- a more narrow period at higher elevations). At each site, a mate and captures precipitation and overwintering condi- fixed route was walked and the presence and absence of tions that potentially affect butterflies through both direct all butterfly species noted. Maps of survey routes and site- effects on juvenile and adult stages, and indirect effects specific details, as well as publically-archived data can be through host and nectar plants. Data were generated as found at http://butterfly.ucdavis.edu/. monthly values using the PRISM system (Parameter-eleva- For most analyses, we restricted data to a common set tion Relationships on Independent Slopes Model, PRISM of years, from 1988 to 2016, for which we have data Climate Group; http://prism.oregonstate.edu) for latitude from all sites (the plots in Fig. 1 that go back to 1985 are and longitude coordinates at the center of each survey an exception, and do not include all sites in the first few route. years). Plots and analyses (described below) primarily in- As a complement to the site-specific, PRISM-derived volve species richness or phenological data, specifically weather variables, we used an index of sea-surface dates of first flight (DFF) or dates of last flight (DLF). temperature associated with the El Niño Southern Oscil- The latter two variables (DFF and DLF) involved filtering lation (ENSO). Specifically, we used the ONI (Oceanic to avoid biases associated with variation in the intensity Niño Index) values for December, January and February or timing of site visits. Specifically, DFF values were only (a single value is reported for those winter months; used if they were proceeded by an absence; in other http://cpc.ncep.noaa.gov) from the winter preceding words, there must be a negative observation before a butterfly observations for a given year. Higher values of positive observation is taken as a DFF record. Similarly, this index correspond to regionally warmer and wetter DLF values were not used if they were not followed by conditions. We also downloaded snowfall data from the an absence; so any species observed on the last visit to a Central Sierra Snow Lab located near our Donner Pass site in a particular year did not have a record of DLF for site (station number 428, https://wcc.sc.egov.usda.gov/ that year. If a species was only observed on a single day reportGenerator/), but preliminary investigations found in a particular year, then that date was used as a DFF that annual snowfall totals were highly correlated with (and only if proceeded by an absence) but not as a DLF, annual precipitation totals. Correlation coefficients be- in order to not use the same data point twice. tween snowfall and precipitation were between 0.80 and Forister et al. Climate Change Responses (2018) 5:3 Page 4 of 9 0.88, and the inclusion of snowfall caused variance infla- z-standardized within species and sites, and z-scores tion factors from linear models (described below) to were averaged across species. often exceed 10; thus snowfall was not included in final Following the visualization phase of investigation, we models. In contrast, correlations among other weather developed simple linear models that were focused on variables (maximum temperatures, minimum tempera- prediction of dates of first flight (DFF) and species rich- tures, precipitation, and ENSO values) were generally ness, summarized as described above (as z-scores aver- lower: across sites and weather variables, the mean of aged across species within each site and year). the absolute value of correlation coefficients was 0.31 Independent variables for both sets of models included (standard deviation = 0.23). average daily minimum temperatures, average daily max- Weather variables that were included in models were imum temperatures, total precipitation, ENSO (ONI sea z-standardized within sites to be in units of standard de- surface temperatures), sampling effort (number of visits), viations. This allows variables from sites with different and year. Models of species richness included date of average conditions (e.g., mountain and valley sites) to be first flight as an additional variable because we were in- readily compared and, more important, it allows for terested in the possibility that the timing of species slopes from multiple regression models to be compared emergence affects butterfly populations and conse- among weather variables that are themselves measured quently observed species richness. These models (for on different scales (as is the case with precipitation and both DFF and richness) were estimated for each site in- temperature). dividually and for high and low sites as groups of sites (5 sites in each model). Additional model complexities Analyses were explored that included interactions between wea- Statistical investigations involved two phases. First, we ther variables, time lags (effects of previous years on used plots of z-standardized data to visualize patterns in current year dynamics) and cumulative effects (sliding phenology (DFF and DLF) and flight days over time; the windows of averaged precipitation values). Interactions latter variable, the number of days flying, was expressed were rare, but we report interactions between weather as the fraction of days that a species is observed divided variables that were significant at P < 0.05. Lagged and by the number of visits to a site per year (this has been cumulative weather variables did not add to the explana- referred to as “day positives” in other publications using tory power of models and the individual lagged and cu- these data [17]). DFF, DLF, and flight days were z-stan- mulative effects were rarely significant and not discussed dardized within species at individual sites and then aver- further. aged across species to facilitate comparisons of patterns As we have found elsewhere for analyses of phenology across sites and years. We also used plots of species and richness with these data [13, 31], linear models sat- richness to explore change over time at each site. Plots isfied assumptions of normality and homogeneity of of richness were accompanied by splines (with 5 degrees variance, and autocorrelation plots were examined to of freedom) and predicted values from random forest confirm independence of residuals. To address potential analyses [24], which both allow for visualization of non- collinearity among predictor variables, variance inflation linear relationships. The spline analysis has the advan- factors were investigated and found generally to be be- tage of producing smoothed relationships (between tween 0 and 5, and in a few cases between 5 and 10. For richness and years), while the random forest analysis, instances where inflation factors approached 10, quality performed with the randomForest [25] package in R control was conducted by including and excluding cor- [26], has the complementary advantage of being able to related variables to verify that estimated β coefficients incorporate covariates (in this case the number of visits were not affected. Linear models were also used to test per year) as well as the advantage of making no assump- the hypothesis that phenological shifts at low elevations tions about the shape of the relationship (between rich- have demographic consequences for individual species. ness and years, while controlling for sampling effort). For each species at the lowest sites (SM, WS, NS, and Patterns in species richness over time were also explored RC; see Fig. 1 legend for site abbreviations), we separ- using diversity indices and Hill numbers that weight rare ately regressed dates of first flight against years, and an- and common species differently (at different levels of q, nual abundance against years. Slopes from those which determines the sensitivity of the analysis to rare regressions were then compared using correlation to ask species) [27–29], using the vegetarian (v1.2) package if species that shifted to an earlier flight (negative slopes [30]inR[26]. In addition, we used the combination of for DFF versus years) were also species that became spline and random forest analyses to investigate changes more abundant (positive slopes of abundance versus in abundance (numbers of individuals observed per year) years). This was done for the years 2008–2016 to cap- at the low elevation sites where abundance data were ture the transition into the millennium drought years, available. As with other variables, abundance values were and only included species that were present in at Forister et al. Climate Change Responses (2018) 5:3 Page 5 of 9 least 6 of those years. As with other analyses, linear are closer to the long-term average (Fig. 1c;also see models were performed and assumptions investigated Additional file 1: Figure S1 for the same results without using R [26]. sampling filters as described above). In other words, the total flight window expanded at lower elevations, while in the mountains the flight window shifted and compressed Results towards the start of the season, a change that is reflected A prolonged and consistent shift towards earlier spring in fewer overall flight days at higher sites (Fig. 1d). flight during the recent drought years can be seen in Fig. Along with the recent reduction in the average 1b. While the shift in phenology is evident across eleva- number of days that butterflies were observed flying tions, the dynamics of the flight window diverge later in at higher elevations during the drought years, there the season: at higher elevations, the date of last flight have been fewer butterfly species observed per year at shifted to an earlier time during the drought, while at thesamesites (Fig. 2a-e). In some cases, the millen- lower elevations the last flight dates from 2011 to 2015 niumdrought was associated with a discrete downturn High elevation sites Low elevation sites Species richness Species richness Abundance a 70 f k 0.4 0.2 0.0 -0.2 -0.4 -0.6 CP GC GC -0.8 b 90 l 0.6 0.4 0.2 0.0 -0.2 65 -0.4 DP 32 RC RC h m 0.6 80 40 0.4 0.2 70 0.0 -0.2 -0.4 LC WS -0.6 WS d i 46 0.6 0.4 0.2 0.0 -0.2 -0.4 SV NS NS e j o 0.4 0.2 70 36 0.0 -0.2 WA SM SM -0.4 1990 2000 2010 1990 2000 2010 2000 2005 2010 2015 Year Year Year Fig. 2 Species richness (a through j) for all sites, and abundance (k through o) for low elevation sites (abundance data were only collected at the low sites). Two letter site names and colors follow Fig. 1a. In all panels, patterns are visualized with both a spline fit with five degrees of freedom (thin black line) and predicted values from random forest analysis (thick gray line) incorporating variation in sampling effort. In panels k through o, values plotted are z-standardized values of total abundance (number of individuals) per year averaged across species Species Species Species Species Species Species Species Species Species Species Individuals Individuals Individuals Individuals Individuals Forister et al. Climate Change Responses (2018) 5:3 Page 6 of 9 (e.g., Fig. 2b and c), while at other montane sites the recent drought years contributed to ongoing, negative Association with irrigation trends (Fig. 2d and e). A downward trend in species Independent richness is less evident at the highest site (CP, Fig. Some areas 2a), which previous analyses have found to be receiv- Some generations ing immigrants from lower elevations as populations Dependent shift upslope in warmer years [15]. In a dramatic re- versal of fortunes, the lowest elevation sites during the millennium drought experienced some of their most productive years in nearly two decades, as reflected both in numbers of species (Fig. 2g-j)and numbers of individuals (Fig. 2l-o). Results shown in Fig. 2 are for simple richness (the number of species observed per year). We repeated analyses using alpha diversity Hill numbers that down weight the import- ance of rare species (Additional file 1:Figures S2, S3), -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 Change in date of first flight and found similar results for all sites except for GC, wherealong-term declineinthe number of species Fig. 3 Change in abundance as a function of change in date of first flight: species that shifted to an earlier date of first flight (negative becomes evident when rare, or transient species have values on the x axis) during the years 2008–2016 were species that less influence. increased their overall abundance (positive values on the y axis) (Pearson Why did the low elevation sites apparently rebound correlation = − 0.50, P < 0.001). Symbols represent dependency on during the drought years? Using the lowest sites (RC, irrigated areas, as follows: circles, essentially independent of irrigation; WS, NS and SM) and a span of years starting just before squares, use irrigated, non-native hosts in some areas as well as native, non-irrigated hosts; triangles, use irrigated, non-native hosts in at least the millennium drought, we discovered a potential effect one of multiple generations; diamond, completely dependent on of phenological plasticity. Specifically, species whose first irrigated, non-native hosts (there is only one species coded with flight shifted to an earlier day were the species that be- a diamond: Agraulis vanillae, not native to the area). Categories of came more abundant (r = − 0.50, P < 0.001; Fig. 3). irrigation dependence are not significant predictors of changing Butterflies at the lowest elevations are almost entirely abundance, either considered in a model with change in date of first flight or alone (F = 1.60, P = 0.21) multivoltine, and an earlier start for those species could 3,39 have allowed populations more time to build to greater densities which might have in turn translated to overall higher levels of observed abundance and species richness Models of species richness revealed even more pro- (Fig. 2). However, we note that causality in the other dir- nounced variation in weather effects across elevations, ection is also possible (higher abundance in favorable including an increased importance of minimum temper- years leading to earlier detection of first flight dates), atures (Fig. 4e), maximum temperatures (Fig. 4f), and and the two possibilities cannot be separated at this precipitation at higher elevations (Fig. 4g) (see time. Nevertheless, the association between change in Additional file 1: Table S2 for full details). The highest abundance and change in first flight date (Fig. 3) raises elevations are most negatively affected by dry years with the potential importance of phenological flexibility, warmer nighttime temperatures. While daily maximum which we sought to further understand by modeling the temperatures have risen everywhere (Fig. 4j) and pat- dates of first flight as a function of maximum and mini- terns of precipitation have fluctuated in concert across mum temperatures, precipitation, and El Niño (ENSO) sites (Fig. 4k), minimum temperatures have risen most conditions. Models explained 60% of the inter-annual steeply at the mountain sites (Fig. 4i). Models of species variation at low elevations (F = 35.17, P < 0.001), and richness also included phenology (date of first flight) as 6,138 72% at the higher elevation sites (F = 59.68, P <0. an explanatory variable, and we found an overall nega- 6,138 001) (Additional file 1: Table S1). Minimum and max- tive association (Fig. 4h), such that earlier emergence is imum temperatures had negative effects on first flight associated with elevated richness (consistent with the ef- dates (warmer temperatures lead to earlier flights), and fect of phenology on abundance at low elevations; Fig. 3). the effect of the former was most noticeable at higher el- However, the beneficial effects of earlier emergence at evations (Fig. 4a and b). Precipitation, as reflected by higher elevations might not be as consequential because local weather and ENSO conditions, had a delaying effect of a lack of multivoltine species, or they may simply be on phenology (positive β coefficients in Fig. 4c and d), outweighed by the negative effects of minimum tempera- which is expected as wetter conditions are associated with tures. Negative effects of minimum temperatures at the cooler, cloudy days and delayed spring emergence. higher elevation sites range from 0.48 fewer species seen Change in abundance -0.1 0.0 0.1 0.2 0.3 0.4 0.5 Forister et al. Climate Change Responses (2018) 5:3 Page 7 of 9 a e ** *** 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year b f ** 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year d h l ** 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year Fig. 4 Results from a model of phenology (a through d; DFF = date of first flight) and a model of species richness (e through h; SR = species richness), as well as plots of weather variables through time (i through l; ENSO = El Niño Southern Oscillation). In the model results (a through h), the values shown are β coefficients (with standard errors) that summarize the effect of a particular weather variable (while controlling for other variables) on either phenology (DFF) or species richnes (SR). Trend lines are only shown in plots where the effect of a particular weather variable changes with elevation (* P < 0.05; ** P < 0.01; *** P < 0.001; see Additional file 1: Table S4 for additional details). In panels i through k, weather patterns are visualized using splines with five degrees of freedom; panel l is the El Niño index (ONI) for each year. Weather variables are shown as z-standardized values in panels i through k, and site specific colors in all plots are the same as in Fig. 1a for every degree Celsius of warming at WA, to 6.46 fewer weather elicits species-specific responses [32], the mil- species seen for every degree at DP (Additional file 1: lennium drought in California produced a consistent re- Table S3). Effects of ENSO on species richness did not dif- sponse across many sites that included advanced dates fer across elevations (not shown in Fig. 4, see Additional of first flight with elevation-specific changes in flight file 1: Table S4), and were generally weak at individual windows and species richness. The resilience exhibited sites (Additional file 1: Table S2). by the lowest elevations appears to be associated with phenological flexibility combined with multivoltine life Discussion histories and climatic associations that are less detrimen- In contrast to both the previously documented hetero- tal (in the context of current climatic trends) than geneity in population response at our focal sites [16] and biotic-abiotic associations at higher elevations. These re- the observation from other systems that extreme sults are interesting in the light of the finding that a ENSO DFF Precip. DFF Max. temp. DFF Min. temp. DFF -0.4 0.0 0.4 -0.4 0.0 0.4 -0.4 0.0 0.4 -1.0 -0.5 0.0 0.5 DFF SR Precip. SR Max. temp. SR Min. temp. SR -2.0 -1.0 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0 -2.0 -1.0 0.0 1.0 ENSO (ONI) Precipitation Max. temperature Min. temperature -1.5 -0.5 0.5 1.5 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 3 Forister et al. Climate Change Responses (2018) 5:3 Page 8 of 9 major drought in 1995 in Great Britain shifted species between maximum and minimum temperatures) has assemblages towards more wide ranging, multivoltine been shrinking around the globe [43], but the ecological butterfly species with generalist host associations [33]. consequences of this thermal homogenization are poorly The drought response in the British butterflies also in- understood and not yet incorporated into theoretical ex- cluded an increase in abundance for a subset of sites, al- pectations of global change biology [44]. The results re- though (in contrast to our findings), the populations ported here suggest that we have much yet to learn that increased during the drought tended to be associ- about organismal responses to extreme weather, and ated with wetter sites, while we saw positive responses at the extent to which different habitat types might or the hotter and drier low elevations. might not buffer populations against climate change Many researchers have hypothesized an impending [45, 46]. However, there is hope for progress because mismatch between trophic levels as a result of climate powerful and simple statistical models predicting change [34]. The results from low elevation butterflies in faunal dynamics with annual climatic values are pos- California perhaps challenge that hypothesis, or at least sible for ectotherms even in the face of unprece- suggest that a shift in phenology at the consumer dented climatic variation. trophic level need not always have negative conse- quences. In addition to having multiple generations each Additional file year, populations at the lowest elevations have access to Additional file 1: Figure S1. Dates of first and last flight through time agricultural lands. Although association with irrigation with alternative data processing, for comparison with Fig. 1. Figures S2 does not appear to predict population dynamics during and S3. Species richness through time at ten study sites as α diversity the drought (Fig. 3), we cannot rule out the possibility using Hill numbers for q = 0, 1 and 2. Table S1. Results from linear models predicting dates of first flight as a function of weather variables that low elevation populations were buffered during the at each site separately and for low and high sites together. Table S2. drought by irrigated crops or agricultural margins. If Models similar to Table S1, but predicting species richness. Table S3. true, a reliance on agriculture would be interesting in effect sizes for models predicting species richness. Table S4. relationships between elevation and variation in beta coefficients summarizing weather the light of a recent hypothesis that long-term declines effects on dates of first flight and species richness. (DOC 2514 kb) in low elevations butterfly populations are associated with intensification of pesticide applications [35]. It is Acknowledgements possible that the rebound of the drought years could be Thanks to the Plant-Insect group at UNR for discussion and ideas that led to followed by a more severe decline following concen- the analyses reported here. trated agriculture dependency and toxin exposure. Funding Research was funded by the National Science Foundation (DEB-1638773 to Conclusions CCN, DEB-1638922 to JAF, and DEB-1638793 to MLF), and MLF was supported It has been known for some time that high latitude envi- by a Trevor James McMinn professorship. ronments are warming faster than the rest of the planet, Availability of data and materials with negative consequences for many high latitude spe- Data analyzed in this paper is available at http://butterfly.ucdavis.edu/. cies [36] but positive or neutral effects for others [37]. It is only recently that climatologists have become aware Authors’ contributions AMS designed and carried out data collection. JHT and DPW managed data that higher elevations may also be experiencing a dispro- entry and curation. MLF, JAF and CCN analyzed the data. All authors portionate share of warming [38], which raises the issue contributed to writing. All authors read and approved the final manuscript. of how cold-adapted, montane ecosystems will respond. Ethics approval and consent to participate Contrary to the expectation that mountains offer micro- Not applicable. climatic refugia and preadapt species for climatic vari- ation [39], we found high elevation butterfly Competing interests The authors declare they have no competing interests. communities to be declining and especially sensitive to dry years with warmer minimum temperatures. Warmer and drier years are associated with lower productivity of Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in mesic-adapted plant communities [40], and shorter win- published maps and institutional affiliations. dows during which montane plants are optimal for nec- tar and herbivory [41]. We did not model snowfall Author details Department of Biology, Program in Ecology, Evolution and Conservation because it is highly correlated with annual precipitation Biology, University of Nevada, Reno, NV 89557, USA. Department of Ecology at our sites (see Methods), but reduced snowpack in dry, and Evolutionary Biology, University of Tennessee, Knoxville, TN 37996, USA. warm years would have additional negative effects in- Department of Biology, Population and Conservation Biology Program, Texas State University, San Marcos, TX 78666, USA. Department of cluding higher overwinter mortality for life history stages Environmental Science and Policy, University of California, Davis, CA 95616, that typically spend the winter under a blanket of snow 5 USA. Center for Population Biology, University of California, Davis, CA 95616, [42]. The daily temperature range (the difference USA. Forister et al. Climate Change Responses (2018) 5:3 Page 9 of 9 Received: 26 January 2018 Accepted: 24 April 2018 26. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Compu; 2013. http://www.r-project.org/. 27. Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54:427–32. 28. Jost L. Entropy and diversity. Oikos. 2006;113:363–75. References 29. Jost L. Partitioning diversity into independent alpha and beta components. 1. Cai W, Borlace S, Lengaigne M, Van Rensch P, Collins M, Vecchi G, et al. Ecology. 2007;88:2427–39. Increasing frequency of extreme El Niño events due to greenhouse 30. Charney N, Record S. Jost diversity measures for community data. R Package warming. Nat Clim Chang. 2014;4:111–6. "Vegetarian" version. 2009;1(2). 2. Diffenbaugh NS, Swain DL, Touma D. Anthropogenic warming has 31. Forister ML, Shapiro AM. Climatic trends and advancing spring flight of increased drought risk in California. Proc Natl Acad Sci. 2015;112:3931–6. butterflies in lowland California. Glob Chang Biol. 2003;9:1130–5. 3. Parmesan C, Hanley ME. Plants and climate change: complexities and 32. Palmer G, Platts PJ, Brereton T, Chapman JW, Dytham C, Fox R, et al. Climate surprises. Ann Bot. 2015;116:849–64. change, climatic variation and extreme biological responses. Phil Trans R 4. Martay B, Brewer MJ, Elston DA, Bell JR, Harrington R, Brereton TM, et al. Soc B. 2017;372:20160144. Impacts of climate change on national biodiversity population trends. 33. Palma A, Dennis RLH, Brereton T, Leather SR, Oliver TH. Large Ecography (Cop). 2017;40:1139–51. reorganizations in butterfly communities during an extreme weather event. 5. Helmuth B, Russell BD, Connell SD, Dong Y, Harley CDG, Lima FP, et al. Ecography Wiley Online Library. 2017;40:577–85. Beyond long-term averages: making biological sense of a rapidly changing 34. Yang LH, Rudolf VHW. Phenology, ontogeny and the effects of climate world. Clim Chang Responses. 2014;1:6. change on the timing of species interactions. Ecol Lett. 2010;13:1–10. 6. Ewald JA, Wheatley CJ, Aebischer NJ, Moreby SJ, Duffield SJ, Crick HQP, 35. Forister ML, Cousens B, Harrison JG, Anderson K, Thorne JH, Waetjen D, et et al. Influences of extreme weather, climate and pesticide use on al. Increasing neonicotinoid use and the declining butterfly fauna of invertebrates in cereal fields over 42 years. Glob Chang Biol. 2015;21: lowland California. Biol Lett. 2016;12:20160475. 3931–50. 36. Parmesan C. Ecological and evolutionary responses to recent climate 7. Matter SF, Roland J. Climate and extreme weather independently affect change. Annu Rev Ecol Evol Syst. 2006;37:637–69. population growth, but neither is a consistently good predictor. Ecosphere. 37. Hunter MD, Kozlov MV, Itämies J, Pulliainen E, Bäck J, Kyrö EM, et al. Current 2017;8:e01816. temporal trends in moth abundance are counter to predicted effects of 8. Parmesan C, Root TL, Willig MR. Impacts of extreme weather and climate on climate change in an assemblage of subarctic forest moths. Glob Chang terrestrial biota. Bull Am Meteorol Soc. 2000;81:443–50. Biol. 2014;20:1723–37. 9. Jentsch A, Beierkuhnlein C. Research frontiers in climate change: effects of 38. Mountain Research Initiative EDW Working Group. Elevation-dependent extreme meteorological events on ecosystems. Comptes Rendus Geosci warming in mountain regions of the world. Nat Clim Chang. 2015;5:424–30. Elsevier. 2008;340:621–8. 39. Daly C, Conklin DR, Unsworth MH. Local atmospheric decoupling in 10. Rouault G, Candau J-N, Lieutier F, Nageleisen L-M, Martin J-C, Warzée N. complex topography alters climate change impacts. Int J Climatol. 2010;30: Effects of drought and heat on forest insect populations in relation to the 1857–64. 2003 drought in Western Europe. Ann For Sci EDP Sciences. 2006;63:613–24. 40. Gottfried M, Pauli H, Futschik A, Akhalkatsi M, Barançok P, Alonso JLB, et al. 11. Ehrlich PR, Murphy DD, Singer MC, Sherwood CB, White RR, Brown IL. Continent-wide response of mountain vegetation to climate change. Nat Extinction, reduction, stability and increase: the resposes of checkerspot Clim Chang. 2012;2:111–5. butterfly (Euphydryas) populations to the California drought. Oecologia. 41. Pettorelli N, Pelletier F, von Hardenberg A, Festa-Bianchet M, Côté SD. Early 1980;46:101–5. onset of vegetation growth vs. rapid green-up: impacts on juvenile 12. Griffin D, Anchukaitis KJ. How unusual is the 2012-2014 California drought? mountain ungulates. Ecology. 2007;88:381–90. Geophys Res Lett. 2014;41:9017–23. 42. Williams CM, Henry HAL, Sinclair BJ. Cold truths: how winter drives responses 13. Forister ML, Jahner JP, Casner KL, Wilson JS, Shapiro AM. The race is not to of terrestrial organisms to climate change. Biol Rev. 2015;90:214–35. the swift: long-term data reveal pervasive declines in California’s low- 43. Vose RS, Easterling DR, Gleason B. Maximum and minimum temperature elevation butterfly fauna. Ecology. 2011;92:2222–35. trends for the globe: an update through 2004. Geophys Res Lett. 2005;32 14. Casner KL, Forister ML, O’Brien JM, Thorne JH, Waetjen DP, Shapiro AM. Loss 44. Speights CJ, Harmon JP, Barton BT. Contrasting the potential effects of of agricultural land and a changing climate contribute to decline of an daytime versus nighttime warming on insects. Curr Opin Insect Sci. 2017;23: urban butterfly fauna. Conserv Biol. 2014;28:773–82. 1–6. 15. Forister ML, McCall AC, Sanders NJ, Fordyce JA, Thorne JH, O’Brien J, et al. 45. O’Connor MI, Selig ER, Pinsky ML, Altermatt F. Toward a conceptual Compounded effects of climate change and habitat alteration shift patterns synthesis for climate change responses. Glob Ecol Biogeogr Wiley Online of butterfly diversity. Proc Natl Acad Sci U S A. 2010;107:2088–92. Library. 2012;21:693–703. 16. Nice CC, Forister ML, Gompert Z, Fordyce JA, Shapiro AM. A hierarchical 46. Nieto-Sánchez S, Gutiérrez D, Wilson RJ. Long-term change and spatial perspective on the diversity of butterfly species’ responses to weather in variation in butterfly communities over an elevational gradient: driven by the sierra Nevada Mountains. Ecology. 2014;95:2155–68. climate, buffered by habitat. Divers Distrib. 2015;21:950–61. 17. Pardikes N, Shapiro AM, Dyer LA, Forister ML. Global weather and local butterflies: variable responses to a large-scale climate pattern along an elevational gradient. Ecology. 2015;96:2891–901. 18. Harrison JG, Shapiro AM, Espeset AE, Nice CC, Jahner JP, Forister ML. Species with more volatile population dynamics are differentially impacted by weather. Biol Lett. 2015;11:20140792. 19. Dobrowski SZ. A climatic basis for microrefugia: the influence of terrain on climate. Glob Chang Biol. 2011;17:1022–35. 20. Nadeau CP, Urban MC, Bridle JR. Climates past, present, and yet-to-come shape climate change vulnerabilities. Trends Ecol Evol. 2017;32:786–800. 21. Boggs CL. The fingerprints of global climate change on insect populations. Curr Opin Insect Sci. 2016;17:69–73. 22. JRK F. Complex responses of insect phenology to climate change. Curr Opin Insect Sci. 2016;17:49–54. 23. Van Dyck H, Bonte D, Puls R, Gotthard K, Maes D. The lost generation hypothesis: could climate change drive ectotherms into a developmental trap? Oikos. 2015;124:54–61. 24. Breiman L. Random forests. Mach Learn. 2001;45:5–32. 25. Liaw A, Wiener M. Classification and regression by randomForest. R news. 2002;2:18–22. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Climate Change Responses Springer Journals

Impacts of a millennium drought on butterfly faunal dynamics

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Life Sciences; Ecology; Applied Ecology; Landscape Ecology; Plant Ecology; Animal Physiology; Fish & Wildlife Biology & Management
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Abstract

Background: Climate change is challenging plants and animals not only with increasing temperatures, but also with shortened intervals between extreme weather events. Relatively little is known about diverse assemblages of organisms responding to extreme weather, and even less is known about landscape and life history properties that might mitigate effects of extreme weather. Our aim was to address this knowledge gap using a multi-decadal dataset of 163 butterfly species that recently experienced a millennium-scale drought. To understand faunal dynamics in the context of the millennium drought, we investigated the behavior of phenology (including date of first flight), species richness and diversity indices through time at 10 study sites spanning an elevational gradient. Linear models were developed to understand the differential sensitivity of butterflies to climate at low and high elevations. Results: Dates of first flight advanced across the elevational gradient during the drought, leading to an overall expansion of the flight window at low elevations and a compression of the flight window in the mountains. The number of species observed per year increased at lower elevations but decreased at higher elevations, apparently as a consequence of extreme sensitivity to hot and dry conditions. Conclusion: Montane populations may be more sensitive to climatic extremes than expected based on availability of microclimates and spatial heterogeneity, while low-elevation populations (despite existing in degraded habitats) are buffered by life history plasticity. Keywords: Butterflies, Climate change, Drought, Extreme weather, Mediterranean climate, Phenology Background frequently, and across greater spatial scales, yet there are Extreme weather events are occurring with increasing few studies addressing the effects of extreme drought on severity and frequency in recent decades, a trend linked insects [9]. In general, warmer and drier conditions are to ongoing anthropogenic climate change and shifts in known to have both positive and negative effects on in- atmospheric circulation [1, 2]. While many studies of sect populations. Positive effects can be associated with species and ecosystems have looked at climate impacts more rapid development and less time spent in vulner- by using the average change in historical or projected able juvenile stages [10], while negative effects include climatic conditions [3, 4], we know far less about the reduced nectar availability and early senescence of host ecological impacts of extreme weather events on wild or- plants [11]. Here we take advantage of decades of data ganisms [5–7]. In part this is due to the regional geog- on 163 butterfly species across an elevational gradient in raphy in which extreme weather events occur, which Northern California (Fig. 1a) to investigate the effects of requires baseline biological data as well as appropriately- a recent, extreme drought on butterfly populations. The scaled climatological data for a particular region prior to years 2011 to 2015 were characterized by both record- an event [8]. Droughts have been occurring more setting high temperatures in California and low levels of precipitation, which combined to produce water deficits * Correspondence: forister@gmail.com that were without precedent in more than 1000 years of Department of Biology, Program in Ecology, Evolution and Conservation reconstructed climatic records [12]. Biology, University of Nevada, Reno, NV 89557, USA Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Forister et al. Climate Change Responses (2018) 5:3 Page 2 of 9 Fig. 1 a Elevational profile of Northern California (left) and map of the same area (on the right) with ten study sites indicated on both; site names as follows, from west to east: Suisun Marsh (SM), Gates Canyon (GC), West Sacramento (WS), North Sacramento (NS), Rancho Cordova (RC), Washington (WA), Lang Crossing (LC), Donner Pass (DP), Castle Peak (CP) and Sierra Valley (SV). b Average dates of first flight and c last flight across species at each location and year. d Average flight days, which are the average fraction of days individual species are observed per year. In panels b, c, and d, color coding for individual lines corresponds to sites as in panel a, and the data are shown as z-standardized values. Grey rectangles in panels b, c, and d, indicate the major drought years from late 2011 into 2015 Investigations of butterflies at our focal sites have re- source populations [15]. Previous analyses of abiotic ef- ported that a majority of populations at the lowest eleva- fects have noted responses to weather that were heteroge- tions have been in decline since at least the mid-1990s neous and idiosyncratic among sites and species [16, 17]. [13], which has been attributed to changes in land use For example, associations with climatic variables often dif- and warming temperatures [14]. Populations at higher fer in sign among congeneric species [18], and even elevations, in contrast, have shown relatively less direc- among populations of a single species [17]. tional change over time, with the exception of a decline The extremity of the 2011–2015 drought provides a in more dispersive, disturbance-associated species that unique opportunity to study abiotic effects on butterfly rely on demographic connections with lower-elevation populations that have already been well characterized in Forister et al. Climate Change Responses (2018) 5:3 Page 3 of 9 terms of climatic relations and long-term demographic For a subset of years and sites, absolute counts of indi- trends. Specifically, we asked the following: 1) does an vidual butterflies (by species) were taken in addition to extraordinary, multi-year drought elicit a faunal response the presence/absence data; this was done at the 5 lowest that is extreme relative to faunal behavior in previous elevation sites starting in 1999. These data are used here dry years? 2) Are impacts on the fauna consistent or di- to investigate the dynamics of the low elevation butter- vergent across elevations? A theoretical expectation is flies during the drought years, specifically the relation- that organisms living in more heterogeneous environ- ship between phenological shifts, changing abundance ments should be more resilient to extremes of temporal and dependence on irrigation. For the latter (dependence variation [19, 20]. We predicted that butterflies at mon- on irrigation), one of us (AMS) ranked species a priori tane sites would be robust relative to populations at (without knowing the results of analyses) based on nat- lower elevations in landscapes that are both less spatially ural history observations. Dependency on irrigated areas variable and already impacted by a history of human ac- was categorized as follows: 1), butterfly species that are tivity. 3) Finally, we asked if population-level responses essentially independent of irrigation; 2), species that use to drought are mediated by phenological shifts. Species irrigated, non-native hosts in some areas as well as na- that are able to begin activity earlier in the spring might tive, non-irrigated hosts in other areas; 3), species that reach higher population densities [21, 22], potentially use irrigated, non-native hosts in at least one of multiple offsetting detrimental drought effects. Another possibil- generations; and 4), species that are completely ity is that ectotherms exposed to longer growing seasons dependent on irrigated, non-native hosts. could fall into a developmental trap by which extra gen- erations fail because of insufficient time [23]. Weather variables Analyses included the following weather variables: max- imum and minimum daily temperatures, total precipita- Methods tion, and a sea surface temperature variable associated Butterfly data with regional conditions [17]. Following previous analyses Ten study sites (Fig. 1a) were visited biweekly (every [15], maximum and minimum temperatures were aver- 2 weeks) by a one of us (AMS) for between 45 and aged and precipitation was totaled from the start of 29 years, depending on the site, and only during good September of the previous year through August of the “butterfly weather” when conditions were suitable for in- current year. Previous fall through current summer is a sect flight (nearly year round at low elevations, and during useful climatological time period in a Mediterranean cli- a more narrow period at higher elevations). At each site, a mate and captures precipitation and overwintering condi- fixed route was walked and the presence and absence of tions that potentially affect butterflies through both direct all butterfly species noted. Maps of survey routes and site- effects on juvenile and adult stages, and indirect effects specific details, as well as publically-archived data can be through host and nectar plants. Data were generated as found at http://butterfly.ucdavis.edu/. monthly values using the PRISM system (Parameter-eleva- For most analyses, we restricted data to a common set tion Relationships on Independent Slopes Model, PRISM of years, from 1988 to 2016, for which we have data Climate Group; http://prism.oregonstate.edu) for latitude from all sites (the plots in Fig. 1 that go back to 1985 are and longitude coordinates at the center of each survey an exception, and do not include all sites in the first few route. years). Plots and analyses (described below) primarily in- As a complement to the site-specific, PRISM-derived volve species richness or phenological data, specifically weather variables, we used an index of sea-surface dates of first flight (DFF) or dates of last flight (DLF). temperature associated with the El Niño Southern Oscil- The latter two variables (DFF and DLF) involved filtering lation (ENSO). Specifically, we used the ONI (Oceanic to avoid biases associated with variation in the intensity Niño Index) values for December, January and February or timing of site visits. Specifically, DFF values were only (a single value is reported for those winter months; used if they were proceeded by an absence; in other http://cpc.ncep.noaa.gov) from the winter preceding words, there must be a negative observation before a butterfly observations for a given year. Higher values of positive observation is taken as a DFF record. Similarly, this index correspond to regionally warmer and wetter DLF values were not used if they were not followed by conditions. We also downloaded snowfall data from the an absence; so any species observed on the last visit to a Central Sierra Snow Lab located near our Donner Pass site in a particular year did not have a record of DLF for site (station number 428, https://wcc.sc.egov.usda.gov/ that year. If a species was only observed on a single day reportGenerator/), but preliminary investigations found in a particular year, then that date was used as a DFF that annual snowfall totals were highly correlated with (and only if proceeded by an absence) but not as a DLF, annual precipitation totals. Correlation coefficients be- in order to not use the same data point twice. tween snowfall and precipitation were between 0.80 and Forister et al. Climate Change Responses (2018) 5:3 Page 4 of 9 0.88, and the inclusion of snowfall caused variance infla- z-standardized within species and sites, and z-scores tion factors from linear models (described below) to were averaged across species. often exceed 10; thus snowfall was not included in final Following the visualization phase of investigation, we models. In contrast, correlations among other weather developed simple linear models that were focused on variables (maximum temperatures, minimum tempera- prediction of dates of first flight (DFF) and species rich- tures, precipitation, and ENSO values) were generally ness, summarized as described above (as z-scores aver- lower: across sites and weather variables, the mean of aged across species within each site and year). the absolute value of correlation coefficients was 0.31 Independent variables for both sets of models included (standard deviation = 0.23). average daily minimum temperatures, average daily max- Weather variables that were included in models were imum temperatures, total precipitation, ENSO (ONI sea z-standardized within sites to be in units of standard de- surface temperatures), sampling effort (number of visits), viations. This allows variables from sites with different and year. Models of species richness included date of average conditions (e.g., mountain and valley sites) to be first flight as an additional variable because we were in- readily compared and, more important, it allows for terested in the possibility that the timing of species slopes from multiple regression models to be compared emergence affects butterfly populations and conse- among weather variables that are themselves measured quently observed species richness. These models (for on different scales (as is the case with precipitation and both DFF and richness) were estimated for each site in- temperature). dividually and for high and low sites as groups of sites (5 sites in each model). Additional model complexities Analyses were explored that included interactions between wea- Statistical investigations involved two phases. First, we ther variables, time lags (effects of previous years on used plots of z-standardized data to visualize patterns in current year dynamics) and cumulative effects (sliding phenology (DFF and DLF) and flight days over time; the windows of averaged precipitation values). Interactions latter variable, the number of days flying, was expressed were rare, but we report interactions between weather as the fraction of days that a species is observed divided variables that were significant at P < 0.05. Lagged and by the number of visits to a site per year (this has been cumulative weather variables did not add to the explana- referred to as “day positives” in other publications using tory power of models and the individual lagged and cu- these data [17]). DFF, DLF, and flight days were z-stan- mulative effects were rarely significant and not discussed dardized within species at individual sites and then aver- further. aged across species to facilitate comparisons of patterns As we have found elsewhere for analyses of phenology across sites and years. We also used plots of species and richness with these data [13, 31], linear models sat- richness to explore change over time at each site. Plots isfied assumptions of normality and homogeneity of of richness were accompanied by splines (with 5 degrees variance, and autocorrelation plots were examined to of freedom) and predicted values from random forest confirm independence of residuals. To address potential analyses [24], which both allow for visualization of non- collinearity among predictor variables, variance inflation linear relationships. The spline analysis has the advan- factors were investigated and found generally to be be- tage of producing smoothed relationships (between tween 0 and 5, and in a few cases between 5 and 10. For richness and years), while the random forest analysis, instances where inflation factors approached 10, quality performed with the randomForest [25] package in R control was conducted by including and excluding cor- [26], has the complementary advantage of being able to related variables to verify that estimated β coefficients incorporate covariates (in this case the number of visits were not affected. Linear models were also used to test per year) as well as the advantage of making no assump- the hypothesis that phenological shifts at low elevations tions about the shape of the relationship (between rich- have demographic consequences for individual species. ness and years, while controlling for sampling effort). For each species at the lowest sites (SM, WS, NS, and Patterns in species richness over time were also explored RC; see Fig. 1 legend for site abbreviations), we separ- using diversity indices and Hill numbers that weight rare ately regressed dates of first flight against years, and an- and common species differently (at different levels of q, nual abundance against years. Slopes from those which determines the sensitivity of the analysis to rare regressions were then compared using correlation to ask species) [27–29], using the vegetarian (v1.2) package if species that shifted to an earlier flight (negative slopes [30]inR[26]. In addition, we used the combination of for DFF versus years) were also species that became spline and random forest analyses to investigate changes more abundant (positive slopes of abundance versus in abundance (numbers of individuals observed per year) years). This was done for the years 2008–2016 to cap- at the low elevation sites where abundance data were ture the transition into the millennium drought years, available. As with other variables, abundance values were and only included species that were present in at Forister et al. Climate Change Responses (2018) 5:3 Page 5 of 9 least 6 of those years. As with other analyses, linear are closer to the long-term average (Fig. 1c;also see models were performed and assumptions investigated Additional file 1: Figure S1 for the same results without using R [26]. sampling filters as described above). In other words, the total flight window expanded at lower elevations, while in the mountains the flight window shifted and compressed Results towards the start of the season, a change that is reflected A prolonged and consistent shift towards earlier spring in fewer overall flight days at higher sites (Fig. 1d). flight during the recent drought years can be seen in Fig. Along with the recent reduction in the average 1b. While the shift in phenology is evident across eleva- number of days that butterflies were observed flying tions, the dynamics of the flight window diverge later in at higher elevations during the drought years, there the season: at higher elevations, the date of last flight have been fewer butterfly species observed per year at shifted to an earlier time during the drought, while at thesamesites (Fig. 2a-e). In some cases, the millen- lower elevations the last flight dates from 2011 to 2015 niumdrought was associated with a discrete downturn High elevation sites Low elevation sites Species richness Species richness Abundance a 70 f k 0.4 0.2 0.0 -0.2 -0.4 -0.6 CP GC GC -0.8 b 90 l 0.6 0.4 0.2 0.0 -0.2 65 -0.4 DP 32 RC RC h m 0.6 80 40 0.4 0.2 70 0.0 -0.2 -0.4 LC WS -0.6 WS d i 46 0.6 0.4 0.2 0.0 -0.2 -0.4 SV NS NS e j o 0.4 0.2 70 36 0.0 -0.2 WA SM SM -0.4 1990 2000 2010 1990 2000 2010 2000 2005 2010 2015 Year Year Year Fig. 2 Species richness (a through j) for all sites, and abundance (k through o) for low elevation sites (abundance data were only collected at the low sites). Two letter site names and colors follow Fig. 1a. In all panels, patterns are visualized with both a spline fit with five degrees of freedom (thin black line) and predicted values from random forest analysis (thick gray line) incorporating variation in sampling effort. In panels k through o, values plotted are z-standardized values of total abundance (number of individuals) per year averaged across species Species Species Species Species Species Species Species Species Species Species Individuals Individuals Individuals Individuals Individuals Forister et al. Climate Change Responses (2018) 5:3 Page 6 of 9 (e.g., Fig. 2b and c), while at other montane sites the recent drought years contributed to ongoing, negative Association with irrigation trends (Fig. 2d and e). A downward trend in species Independent richness is less evident at the highest site (CP, Fig. Some areas 2a), which previous analyses have found to be receiv- Some generations ing immigrants from lower elevations as populations Dependent shift upslope in warmer years [15]. In a dramatic re- versal of fortunes, the lowest elevation sites during the millennium drought experienced some of their most productive years in nearly two decades, as reflected both in numbers of species (Fig. 2g-j)and numbers of individuals (Fig. 2l-o). Results shown in Fig. 2 are for simple richness (the number of species observed per year). We repeated analyses using alpha diversity Hill numbers that down weight the import- ance of rare species (Additional file 1:Figures S2, S3), -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 Change in date of first flight and found similar results for all sites except for GC, wherealong-term declineinthe number of species Fig. 3 Change in abundance as a function of change in date of first flight: species that shifted to an earlier date of first flight (negative becomes evident when rare, or transient species have values on the x axis) during the years 2008–2016 were species that less influence. increased their overall abundance (positive values on the y axis) (Pearson Why did the low elevation sites apparently rebound correlation = − 0.50, P < 0.001). Symbols represent dependency on during the drought years? Using the lowest sites (RC, irrigated areas, as follows: circles, essentially independent of irrigation; WS, NS and SM) and a span of years starting just before squares, use irrigated, non-native hosts in some areas as well as native, non-irrigated hosts; triangles, use irrigated, non-native hosts in at least the millennium drought, we discovered a potential effect one of multiple generations; diamond, completely dependent on of phenological plasticity. Specifically, species whose first irrigated, non-native hosts (there is only one species coded with flight shifted to an earlier day were the species that be- a diamond: Agraulis vanillae, not native to the area). Categories of came more abundant (r = − 0.50, P < 0.001; Fig. 3). irrigation dependence are not significant predictors of changing Butterflies at the lowest elevations are almost entirely abundance, either considered in a model with change in date of first flight or alone (F = 1.60, P = 0.21) multivoltine, and an earlier start for those species could 3,39 have allowed populations more time to build to greater densities which might have in turn translated to overall higher levels of observed abundance and species richness Models of species richness revealed even more pro- (Fig. 2). However, we note that causality in the other dir- nounced variation in weather effects across elevations, ection is also possible (higher abundance in favorable including an increased importance of minimum temper- years leading to earlier detection of first flight dates), atures (Fig. 4e), maximum temperatures (Fig. 4f), and and the two possibilities cannot be separated at this precipitation at higher elevations (Fig. 4g) (see time. Nevertheless, the association between change in Additional file 1: Table S2 for full details). The highest abundance and change in first flight date (Fig. 3) raises elevations are most negatively affected by dry years with the potential importance of phenological flexibility, warmer nighttime temperatures. While daily maximum which we sought to further understand by modeling the temperatures have risen everywhere (Fig. 4j) and pat- dates of first flight as a function of maximum and mini- terns of precipitation have fluctuated in concert across mum temperatures, precipitation, and El Niño (ENSO) sites (Fig. 4k), minimum temperatures have risen most conditions. Models explained 60% of the inter-annual steeply at the mountain sites (Fig. 4i). Models of species variation at low elevations (F = 35.17, P < 0.001), and richness also included phenology (date of first flight) as 6,138 72% at the higher elevation sites (F = 59.68, P <0. an explanatory variable, and we found an overall nega- 6,138 001) (Additional file 1: Table S1). Minimum and max- tive association (Fig. 4h), such that earlier emergence is imum temperatures had negative effects on first flight associated with elevated richness (consistent with the ef- dates (warmer temperatures lead to earlier flights), and fect of phenology on abundance at low elevations; Fig. 3). the effect of the former was most noticeable at higher el- However, the beneficial effects of earlier emergence at evations (Fig. 4a and b). Precipitation, as reflected by higher elevations might not be as consequential because local weather and ENSO conditions, had a delaying effect of a lack of multivoltine species, or they may simply be on phenology (positive β coefficients in Fig. 4c and d), outweighed by the negative effects of minimum tempera- which is expected as wetter conditions are associated with tures. Negative effects of minimum temperatures at the cooler, cloudy days and delayed spring emergence. higher elevation sites range from 0.48 fewer species seen Change in abundance -0.1 0.0 0.1 0.2 0.3 0.4 0.5 Forister et al. Climate Change Responses (2018) 5:3 Page 7 of 9 a e ** *** 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year b f ** 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year d h l ** 0 500 1500 2500 0 500 1500 2500 1990 2000 2010 Elevation Elevation Year Fig. 4 Results from a model of phenology (a through d; DFF = date of first flight) and a model of species richness (e through h; SR = species richness), as well as plots of weather variables through time (i through l; ENSO = El Niño Southern Oscillation). In the model results (a through h), the values shown are β coefficients (with standard errors) that summarize the effect of a particular weather variable (while controlling for other variables) on either phenology (DFF) or species richnes (SR). Trend lines are only shown in plots where the effect of a particular weather variable changes with elevation (* P < 0.05; ** P < 0.01; *** P < 0.001; see Additional file 1: Table S4 for additional details). In panels i through k, weather patterns are visualized using splines with five degrees of freedom; panel l is the El Niño index (ONI) for each year. Weather variables are shown as z-standardized values in panels i through k, and site specific colors in all plots are the same as in Fig. 1a for every degree Celsius of warming at WA, to 6.46 fewer weather elicits species-specific responses [32], the mil- species seen for every degree at DP (Additional file 1: lennium drought in California produced a consistent re- Table S3). Effects of ENSO on species richness did not dif- sponse across many sites that included advanced dates fer across elevations (not shown in Fig. 4, see Additional of first flight with elevation-specific changes in flight file 1: Table S4), and were generally weak at individual windows and species richness. The resilience exhibited sites (Additional file 1: Table S2). by the lowest elevations appears to be associated with phenological flexibility combined with multivoltine life Discussion histories and climatic associations that are less detrimen- In contrast to both the previously documented hetero- tal (in the context of current climatic trends) than geneity in population response at our focal sites [16] and biotic-abiotic associations at higher elevations. These re- the observation from other systems that extreme sults are interesting in the light of the finding that a ENSO DFF Precip. DFF Max. temp. DFF Min. temp. DFF -0.4 0.0 0.4 -0.4 0.0 0.4 -0.4 0.0 0.4 -1.0 -0.5 0.0 0.5 DFF SR Precip. SR Max. temp. SR Min. temp. SR -2.0 -1.0 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0 -2.0 -1.0 0.0 1.0 ENSO (ONI) Precipitation Max. temperature Min. temperature -1.5 -0.5 0.5 1.5 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 3 Forister et al. Climate Change Responses (2018) 5:3 Page 8 of 9 major drought in 1995 in Great Britain shifted species between maximum and minimum temperatures) has assemblages towards more wide ranging, multivoltine been shrinking around the globe [43], but the ecological butterfly species with generalist host associations [33]. consequences of this thermal homogenization are poorly The drought response in the British butterflies also in- understood and not yet incorporated into theoretical ex- cluded an increase in abundance for a subset of sites, al- pectations of global change biology [44]. The results re- though (in contrast to our findings), the populations ported here suggest that we have much yet to learn that increased during the drought tended to be associ- about organismal responses to extreme weather, and ated with wetter sites, while we saw positive responses at the extent to which different habitat types might or the hotter and drier low elevations. might not buffer populations against climate change Many researchers have hypothesized an impending [45, 46]. However, there is hope for progress because mismatch between trophic levels as a result of climate powerful and simple statistical models predicting change [34]. The results from low elevation butterflies in faunal dynamics with annual climatic values are pos- California perhaps challenge that hypothesis, or at least sible for ectotherms even in the face of unprece- suggest that a shift in phenology at the consumer dented climatic variation. trophic level need not always have negative conse- quences. In addition to having multiple generations each Additional file year, populations at the lowest elevations have access to Additional file 1: Figure S1. Dates of first and last flight through time agricultural lands. Although association with irrigation with alternative data processing, for comparison with Fig. 1. Figures S2 does not appear to predict population dynamics during and S3. Species richness through time at ten study sites as α diversity the drought (Fig. 3), we cannot rule out the possibility using Hill numbers for q = 0, 1 and 2. Table S1. Results from linear models predicting dates of first flight as a function of weather variables that low elevation populations were buffered during the at each site separately and for low and high sites together. Table S2. drought by irrigated crops or agricultural margins. If Models similar to Table S1, but predicting species richness. Table S3. true, a reliance on agriculture would be interesting in effect sizes for models predicting species richness. Table S4. relationships between elevation and variation in beta coefficients summarizing weather the light of a recent hypothesis that long-term declines effects on dates of first flight and species richness. (DOC 2514 kb) in low elevations butterfly populations are associated with intensification of pesticide applications [35]. It is Acknowledgements possible that the rebound of the drought years could be Thanks to the Plant-Insect group at UNR for discussion and ideas that led to followed by a more severe decline following concen- the analyses reported here. trated agriculture dependency and toxin exposure. Funding Research was funded by the National Science Foundation (DEB-1638773 to Conclusions CCN, DEB-1638922 to JAF, and DEB-1638793 to MLF), and MLF was supported It has been known for some time that high latitude envi- by a Trevor James McMinn professorship. ronments are warming faster than the rest of the planet, Availability of data and materials with negative consequences for many high latitude spe- Data analyzed in this paper is available at http://butterfly.ucdavis.edu/. cies [36] but positive or neutral effects for others [37]. It is only recently that climatologists have become aware Authors’ contributions AMS designed and carried out data collection. JHT and DPW managed data that higher elevations may also be experiencing a dispro- entry and curation. MLF, JAF and CCN analyzed the data. All authors portionate share of warming [38], which raises the issue contributed to writing. All authors read and approved the final manuscript. of how cold-adapted, montane ecosystems will respond. Ethics approval and consent to participate Contrary to the expectation that mountains offer micro- Not applicable. climatic refugia and preadapt species for climatic vari- ation [39], we found high elevation butterfly Competing interests The authors declare they have no competing interests. communities to be declining and especially sensitive to dry years with warmer minimum temperatures. Warmer and drier years are associated with lower productivity of Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in mesic-adapted plant communities [40], and shorter win- published maps and institutional affiliations. dows during which montane plants are optimal for nec- tar and herbivory [41]. We did not model snowfall Author details Department of Biology, Program in Ecology, Evolution and Conservation because it is highly correlated with annual precipitation Biology, University of Nevada, Reno, NV 89557, USA. Department of Ecology at our sites (see Methods), but reduced snowpack in dry, and Evolutionary Biology, University of Tennessee, Knoxville, TN 37996, USA. warm years would have additional negative effects in- Department of Biology, Population and Conservation Biology Program, Texas State University, San Marcos, TX 78666, USA. Department of cluding higher overwinter mortality for life history stages Environmental Science and Policy, University of California, Davis, CA 95616, that typically spend the winter under a blanket of snow 5 USA. Center for Population Biology, University of California, Davis, CA 95616, [42]. The daily temperature range (the difference USA. Forister et al. Climate Change Responses (2018) 5:3 Page 9 of 9 Received: 26 January 2018 Accepted: 24 April 2018 26. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Compu; 2013. http://www.r-project.org/. 27. Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54:427–32. 28. Jost L. Entropy and diversity. Oikos. 2006;113:363–75. References 29. Jost L. Partitioning diversity into independent alpha and beta components. 1. Cai W, Borlace S, Lengaigne M, Van Rensch P, Collins M, Vecchi G, et al. Ecology. 2007;88:2427–39. Increasing frequency of extreme El Niño events due to greenhouse 30. Charney N, Record S. Jost diversity measures for community data. R Package warming. Nat Clim Chang. 2014;4:111–6. "Vegetarian" version. 2009;1(2). 2. Diffenbaugh NS, Swain DL, Touma D. Anthropogenic warming has 31. Forister ML, Shapiro AM. Climatic trends and advancing spring flight of increased drought risk in California. Proc Natl Acad Sci. 2015;112:3931–6. butterflies in lowland California. Glob Chang Biol. 2003;9:1130–5. 3. Parmesan C, Hanley ME. Plants and climate change: complexities and 32. Palmer G, Platts PJ, Brereton T, Chapman JW, Dytham C, Fox R, et al. Climate surprises. Ann Bot. 2015;116:849–64. change, climatic variation and extreme biological responses. Phil Trans R 4. Martay B, Brewer MJ, Elston DA, Bell JR, Harrington R, Brereton TM, et al. Soc B. 2017;372:20160144. Impacts of climate change on national biodiversity population trends. 33. Palma A, Dennis RLH, Brereton T, Leather SR, Oliver TH. Large Ecography (Cop). 2017;40:1139–51. reorganizations in butterfly communities during an extreme weather event. 5. Helmuth B, Russell BD, Connell SD, Dong Y, Harley CDG, Lima FP, et al. Ecography Wiley Online Library. 2017;40:577–85. Beyond long-term averages: making biological sense of a rapidly changing 34. Yang LH, Rudolf VHW. Phenology, ontogeny and the effects of climate world. Clim Chang Responses. 2014;1:6. change on the timing of species interactions. Ecol Lett. 2010;13:1–10. 6. Ewald JA, Wheatley CJ, Aebischer NJ, Moreby SJ, Duffield SJ, Crick HQP, 35. Forister ML, Cousens B, Harrison JG, Anderson K, Thorne JH, Waetjen D, et et al. Influences of extreme weather, climate and pesticide use on al. Increasing neonicotinoid use and the declining butterfly fauna of invertebrates in cereal fields over 42 years. Glob Chang Biol. 2015;21: lowland California. Biol Lett. 2016;12:20160475. 3931–50. 36. Parmesan C. Ecological and evolutionary responses to recent climate 7. Matter SF, Roland J. Climate and extreme weather independently affect change. Annu Rev Ecol Evol Syst. 2006;37:637–69. population growth, but neither is a consistently good predictor. Ecosphere. 37. Hunter MD, Kozlov MV, Itämies J, Pulliainen E, Bäck J, Kyrö EM, et al. Current 2017;8:e01816. temporal trends in moth abundance are counter to predicted effects of 8. Parmesan C, Root TL, Willig MR. Impacts of extreme weather and climate on climate change in an assemblage of subarctic forest moths. Glob Chang terrestrial biota. Bull Am Meteorol Soc. 2000;81:443–50. Biol. 2014;20:1723–37. 9. Jentsch A, Beierkuhnlein C. Research frontiers in climate change: effects of 38. Mountain Research Initiative EDW Working Group. Elevation-dependent extreme meteorological events on ecosystems. Comptes Rendus Geosci warming in mountain regions of the world. Nat Clim Chang. 2015;5:424–30. Elsevier. 2008;340:621–8. 39. Daly C, Conklin DR, Unsworth MH. Local atmospheric decoupling in 10. Rouault G, Candau J-N, Lieutier F, Nageleisen L-M, Martin J-C, Warzée N. complex topography alters climate change impacts. Int J Climatol. 2010;30: Effects of drought and heat on forest insect populations in relation to the 1857–64. 2003 drought in Western Europe. Ann For Sci EDP Sciences. 2006;63:613–24. 40. Gottfried M, Pauli H, Futschik A, Akhalkatsi M, Barançok P, Alonso JLB, et al. 11. Ehrlich PR, Murphy DD, Singer MC, Sherwood CB, White RR, Brown IL. Continent-wide response of mountain vegetation to climate change. Nat Extinction, reduction, stability and increase: the resposes of checkerspot Clim Chang. 2012;2:111–5. butterfly (Euphydryas) populations to the California drought. Oecologia. 41. Pettorelli N, Pelletier F, von Hardenberg A, Festa-Bianchet M, Côté SD. Early 1980;46:101–5. onset of vegetation growth vs. rapid green-up: impacts on juvenile 12. Griffin D, Anchukaitis KJ. How unusual is the 2012-2014 California drought? mountain ungulates. Ecology. 2007;88:381–90. Geophys Res Lett. 2014;41:9017–23. 42. Williams CM, Henry HAL, Sinclair BJ. Cold truths: how winter drives responses 13. Forister ML, Jahner JP, Casner KL, Wilson JS, Shapiro AM. The race is not to of terrestrial organisms to climate change. Biol Rev. 2015;90:214–35. the swift: long-term data reveal pervasive declines in California’s low- 43. Vose RS, Easterling DR, Gleason B. Maximum and minimum temperature elevation butterfly fauna. Ecology. 2011;92:2222–35. trends for the globe: an update through 2004. Geophys Res Lett. 2005;32 14. Casner KL, Forister ML, O’Brien JM, Thorne JH, Waetjen DP, Shapiro AM. Loss 44. Speights CJ, Harmon JP, Barton BT. Contrasting the potential effects of of agricultural land and a changing climate contribute to decline of an daytime versus nighttime warming on insects. Curr Opin Insect Sci. 2017;23: urban butterfly fauna. Conserv Biol. 2014;28:773–82. 1–6. 15. Forister ML, McCall AC, Sanders NJ, Fordyce JA, Thorne JH, O’Brien J, et al. 45. O’Connor MI, Selig ER, Pinsky ML, Altermatt F. Toward a conceptual Compounded effects of climate change and habitat alteration shift patterns synthesis for climate change responses. Glob Ecol Biogeogr Wiley Online of butterfly diversity. Proc Natl Acad Sci U S A. 2010;107:2088–92. Library. 2012;21:693–703. 16. Nice CC, Forister ML, Gompert Z, Fordyce JA, Shapiro AM. A hierarchical 46. Nieto-Sánchez S, Gutiérrez D, Wilson RJ. Long-term change and spatial perspective on the diversity of butterfly species’ responses to weather in variation in butterfly communities over an elevational gradient: driven by the sierra Nevada Mountains. Ecology. 2014;95:2155–68. climate, buffered by habitat. Divers Distrib. 2015;21:950–61. 17. Pardikes N, Shapiro AM, Dyer LA, Forister ML. Global weather and local butterflies: variable responses to a large-scale climate pattern along an elevational gradient. Ecology. 2015;96:2891–901. 18. Harrison JG, Shapiro AM, Espeset AE, Nice CC, Jahner JP, Forister ML. Species with more volatile population dynamics are differentially impacted by weather. Biol Lett. 2015;11:20140792. 19. Dobrowski SZ. A climatic basis for microrefugia: the influence of terrain on climate. Glob Chang Biol. 2011;17:1022–35. 20. Nadeau CP, Urban MC, Bridle JR. Climates past, present, and yet-to-come shape climate change vulnerabilities. Trends Ecol Evol. 2017;32:786–800. 21. Boggs CL. The fingerprints of global climate change on insect populations. Curr Opin Insect Sci. 2016;17:69–73. 22. JRK F. Complex responses of insect phenology to climate change. Curr Opin Insect Sci. 2016;17:49–54. 23. Van Dyck H, Bonte D, Puls R, Gotthard K, Maes D. The lost generation hypothesis: could climate change drive ectotherms into a developmental trap? Oikos. 2015;124:54–61. 24. Breiman L. Random forests. Mach Learn. 2001;45:5–32. 25. Liaw A, Wiener M. Classification and regression by randomForest. R news. 2002;2:18–22.

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Climate Change ResponsesSpringer Journals

Published: Jun 5, 2018

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