Nonlinear trends in abundance and diversity SPECIAL FEATURE and complex responses to climate change in Arctic

Toke T. Høyea,b,1, Sarah Lobodac, Amanda M. Koltzd,e, Mark A. K. Gillespief, Joseph J. Bowdeng, and Niels M. Schmidth,i

aArctic Research Centre, Aarhus University, DK-8410 Rønde, Denmark; bDepartment of Bioscience, Aarhus University, DK-8410 Rønde, Denmark; cDepartment of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada; dDepartment of Biology, Washington University in St. Louis, St. Louis, MO 63130; eThe Arctic Institute, Washington, DC 20009; fDepartment of Environmental Sciences, Western Norway University of Applied Sciences, 6851 Sogndal, Norway; gAtlantic Forestry Centre, Canadian Forest Service, Natural Resources Canada, Corner Brook, NL A2H 5G4, Canada; hArctic Research Centre, Aarhus University, DK-4000 Roskilde, Denmark; and iDepartment of Bioscience, Aarhus University, DK-4000 Roskilde, Denmark

Edited by David L. Wagner, University of Connecticut, Storrs, CT, and accepted by Editorial Board Member May R. Berenbaum November 13, 2020 (received for review April 10, 2020) Time series data on populations are critical for under- found a midsummer decline in flying insect biomass of 82% over standing the magnitude, direction, and drivers of change. How- 27 y in protected areas in Germany, this pattern could not be ever, most arthropod monitoring programs are short-lived and attributed to landscape or climate factors and, as few locations restricted in taxonomic resolution. Monitoring data from the Arctic were sampled repeatedly, an assessment of the importance of are especially underrepresented, yet critical to uncovering and un- biotic factors such as density dependence was not possible. derstanding some of the earliest biological responses to rapid en- Arctic regions provide an important opportunity to isolate the vironmental change. Clear imprints of climate on the behavior and ecological impacts of climate change because direct anthropo- life history of some Arctic arthropods have been demonstrated, genic disturbances are largely absent (17). The region is also but a synthesis of population-level abundance changes across taxa warming rapidly and snowmelt, which strongly affects inverte-

is lacking. We utilized 24 y of abundance data from Zackenberg in brate activity, is occurring earlier (18, 19). While long-term data ECOLOGY High-Arctic Greenland to assess trends in abundance and diversity are generally scarce in the Arctic (12), the arthropod monitoring and identify potential climatic drivers of abundance changes. Un- program at Zackenberg, northeast Greenland, has been col- like findings from temperate systems, we found a nonlinear pat- lecting standardized data since 1996, representing the longest- tern, with total arthropod abundance gradually declining during running terrestrial arthropod monitoring program in the Arctic 1996 to 2014, followed by a sharp increase. Family-level diversity (20). The climate trends for Zackenberg are in line with trends showed the opposite pattern, suggesting increasing dominance of for the rest of Greenland, with significant warming trends of 0.1 − a small number of taxa. Total abundance masked more compli- to 0.3 °C·y 1 (21). These arthropod data offer a rare opportunity cated trajectories of family-level abundance, which also frequently to detect population changes across a broad array of taxa from varied among habitats. Contrary to expectation in this extreme different habitats. Many long-term arthropod monitoring pro- polar environment, winter and fall conditions and positive den- grams report only overall abundance, biomass, or trends from a sity-dependent feedbacks were more common determinants of ar- thropod dynamics than summer temperature. Together, these Significance data highlight the complexity of characterizing climate change re- sponses even in relatively simple Arctic food webs. Our results underscore the need for data reporting beyond overall trends in Arthropods are excellent indicators for studying global change biomass or abundance and for including basic research on life his- in the rapidly changing climate of the Arctic. We used the most tory and ecology to achieve a more nuanced understanding of the comprehensive standardized dataset on Arctic arthropods to sensitivity of Arctic and other arthropods to global changes. quantify diversity and abundance variation over 24 y in an area that is warming rapidly. Overall arthropod abundance and di- versity showed opposing nonlinear trends, with a sharp in- insects | long-term monitoring | | temporal trend crease in overall abundance in recent years. However, trends varied substantially among taxa and habitats and several ong-term monitoring data, meta-analyses, and reviews indi- groups declined in abundance. We found strong evidence of Lcate that the abundance and diversity of terrestrial arthropods conditions outside the growing season and density-dependent – are declining across many sites (1 5) and several anthropogenic feedbacks affecting abundance. Our results emphasize the drivers are likely involved (6). However, due to data limitations, need for a more integrated approach to investigating arthro- each assessment has various strengths and weaknesses: For ex- pod responses to environmental stressors at finer taxonomic ample, some studies are based on biological records requiring resolution and by incorporating time-lagged effects. corrections for sampling effort (1, 4, 7), while others provide only a comparison of a few discrete points in time (7). These and Author contributions: T.T.H., S.L., A.M.K., M.A.K.G., J.J.B., and N.M.S. designed research; other shortfalls have strengthened calls for more standardized T.T.H., S.L., A.M.K., and M.A.K.G. analyzed data; and T.T.H., S.L., A.M.K., and M.A.K.G. long-term biological monitoring (8–11), especially from areas wrote the paper. with reduced levels of direct human impacts (12–14). Previous The authors declare no competing interest. work on arthropod communities points toward habitat frag- This article is a PNAS Direct Submission. D.L.W. is a guest editor invited by the mentation, habitat degradation and loss, and land-use intensifi- Editorial Board. cation as primary drivers of species declines over the last decades Published under the PNAS license. (2), although climate changes will likely increase in importance 1To whom correspondence may be addressed. Email: [email protected]. (14–16). Teasing apart the effects of land-use change, climate This article contains supporting information online at https://www.pnas.org/lookup/suppl/ change, biotic factors, and other stressors, however, remains a doi:10.1073/pnas.2002557117/-/DCSupplemental. major challenge (14). For example, while Hallmann et al. (3) Published January 11, 2021.

PNAS 2021 Vol. 118 No. 2 e2002557117 https://doi.org/10.1073/pnas.2002557117 | 1of8 Downloaded by guest on September 30, 2021 single habitat type (3, 5, 22). Such studies may fail to detect these time series. We fitted simple linear and segmented linear important lineage-specific, spatial, and temporal patterns (23–25). (hereafter referred to as “nonlinear”) models to all time series For example, while Crossley et al. (26) found no net trend in data to assess support for nonlinear dynamics. Based on previous abundance in their meta-analyses of insect populations across results and our understanding of Arctic arthropods in general long-term ecological research sites in the United States, the (12, 13, 38), we expected the 1996-to-2016 declines to continue authors acknowledged that focusing only on broad cross-taxon and to be apparent in trends of summed abundance. In addition, patterns likely masked important ecological effects at finer we expected family-level diversity to be relatively stable over the taxonomic levels. study period due to a lack of direct habitat disturbance and a lack Despite the simpler suite of drivers in the Arctic, predicting of documented species immigrations to this area. At the family arthropod responses to climate change remains a challenge in level, we anticipated a range of trends in abundance as shown in part due to the various environmental changes occurring. For other areas (14). Given previously documented weak links be- example, as elsewhere, Arctic organisms are limited by temper- tween abundance of some arthropod species and climate at ature and moisture (27). We might expect rapidly warming Zackenberg, we did not expect substantial evidence that overall conditions to lead to increases in arthropod abundances while, abundance trends would be climate-driven. However, as several on the other hand, melting permafrost and the associated de- groups are known to be especially sensitive to desiccation stress clines in soil moisture would impose important constraints (15). during the juvenile or larval stages, such as Collembola, Diptera, Our previous work in the region has shown considerable het- and some Hymenoptera (39), we did expect families within those erogeneity in long-term changes and links to climate across in- groups to experience negative trends in association with warmer vertebrate orders (28). For example, a subset of families exhibited summer temperatures. Finally, as other authors have suggested declines in total abundance between 1996 and 2016 for all hab- the importance of climate conditions in other seasons (40), we itats studied (13), with habitat type playing an important role included relevant climate variables across the full annual cycle to mediating the strength of species abundance trends for both quantify their importance. spiders (Araneae) and muscid (Diptera) (13, 25, 29), while links to climate variables were taxon-specific and thus did not Results show a strong, consistent pattern. Conversely, we documented Climatic Trends. There were strong indications of warming at strong effects of climate on body size (30, 31), phenology Zackenberg in the first decade (∼1996 to 2005) of the study (32–34), community composition (25, 28), and species interac- period, including warming summers, increasing frequency of tions (35, 36). Previous work in Alaska has also indicated that freeze–thaw events, and shorter winters (Fig. 1). This period was density-dependent factors may play an important role in some also characterized by decreasing fall and winter temperatures. Arctic arthropod populations (37), but rigorous statistical testing However, since about 2005, the magnitude of these climate of effects of past population density requires long time series. trends has reversed, albeit with increasing variability around Such complex responses are likely to be masked by overall trends trends in summer temperature and winter duration. We note in abundance or biomass and emphasize the urgent need for that while we used soil temperature in this study, previous studies improved understanding of variation in population trends across that have reported a strong warming trend at the site (e.g., ref. arthropod taxa to predict the likely winners and losers as global 25) were based on air temperatures, which are typically uncou- climate continues to change. pled from soil temperatures when the ground is snow-covered or In this study, we used the Zackenberg dataset consisting of waterlogged (41). Detailed climate results are contained in SI more than 1 million individual arthropods collected weekly Appendix, Table S1. throughout the growing season over the last 24 y to improve the understanding of long-term changes in a community of terrestrial Trends in Total Abundance and Diversity. Our analysis of trends in and semiaquatic arthropods affected by climate change. Our first abundance revealed only limited evidence for clear, linear specific objective was to provide a detailed assessment of the changes in abundances of insect, , and microarthropod temporal dynamics in overall site-level arthropod abundance and populations at Zackenberg, a dataset that includes a total of diversity, in abundance summed at the habitat level, and in 1,006,848 individuals collected across the entire 24-y time period abundance for all available family–habitat combinations. Second, (1996 to 2018). Instead, total arthropod abundance was best we made a preliminary assessment of the relationships between fitted by a segmented linear model showing a gradual decline arthropod abundance, climate variables, and abundance in the until 2014, followed by a sharp increase (Fig. 2). This pattern was previous year (hereafter referred to as “density dependence”) for consistent for arid and mesic heath habitats but not for the wet

Fig. 1. Interannual variation and trends of key climate variables of relevance to arthropod abundance. Solid lines indicate significance for linear or seg- mented linear trends (see detailed results in SI Appendix, Table S1).

2of8 | PNAS Høye et al. https://doi.org/10.1073/pnas.2002557117 Nonlinear trends in abundance and diversity and complex responses to climate change in Arctic arthropods Downloaded by guest on September 30, 2021 SPECIAL FEATURE ECOLOGY

Fig. 2. Interannual variation and trends in total relative standardized arthropod abundance (Top) and arthropod diversity (Hill 0D, 1D, and 2D) for three habitat types extrapolated with individual-based rarefaction curves. Hill 0D, richness; Hill 1D, the exponential of Shannon’s entropy index; Hill 2D, the inverse of Simpson’s concentration index. Solid lines indicate significance for linear or segmented linear trends (see detailed results in SI Appendix, Table S2).

fen, where no significant trend was found. We found an opposite period (∼2006 to 2012), while decomposers declined in abun- pattern in diversity. Specifically, the exponential of Shannon’s dance during the same period. Within the functionally diverse entropy (Hill 1D) and the inverse Simpson’s concentration index Diptera, we found contrasting trends and complex dynamics (Hill 2D) computed from family-level data showed a gradual across groups. Explanations of taxonomic affiliations and broad increase until 2015, followed by a sharp decrease. This pattern functional groups of included taxa are presented in SI Appendix, was consistent for separate analyses of arid and mesic heath Table S3, and detailed results from the segmented regression habitats individually, which showed distinct change points in analyses of individual taxa from each habitat are presented in SI 2015 and 2014, respectively (Fig. 2). Overall family richness Appendix, Table S4. (Hill 0D) followed a somewhat different pattern, with an initial increase until 2005, followed by a gradual decline. This pattern Abundance Trends in Association with Climate Variables. We used was also found for mesic heath, whereas no trend was found in LASSO (least absolute shrinkage and selection operator) re- arid heath and wet fen. Detailed diversity results are presented in gression models to identify the explanatory climate variables that SI Appendix, Table S2. were the best predictors for overall abundance trends, as well as for trends across the different families (Fig. 4). We considered Trends in Abundance at Finer Taxonomic Resolution. Despite the the importance of density dependence in these trends by in- similar patterns in total arthropod abundance across habitats and cluding a first-order lagged abundance variable in each model. for the arid and mesic heath sites individually, analyses of indi- For the overall data, higher abundances were associated with vidual taxa revealed great variation. Some taxa such as Lycosidae warmer fall and winter temperatures and with lower summer and Muscidae even exhibited distinctly different patterns be- temperatures in the year arthropods were sampled as well as the tween habitats (Fig. 3). We found evidence of general linear previous year. This pattern was relatively consistent for arid and declines in a few taxa, particularly in mesic heath (Linyphiidae, mesic heath, although bootstrapped CIs around the coefficients Ichneumonidae, and Nymphalidae), but nonlinear trends were suggest uncertainty around some of the predictors (SI Appendix, much more common. This analysis also revealed distinct com- Table S5). There was also a strong density-dependent effect in monalities in the dynamics of several taxa. For example, the the arid habitat, as indicated by the large coefficient for the highest abundances of herbivores, most parasitoids, and certain lagged abundance term. Conversely, total abundance in wet fen predators were detected during the central part of the study was negatively affected by higher spring temperatures. When the

Høye et al. PNAS | 3of8 Nonlinear trends in abundance and diversity and complex responses to climate change https://doi.org/10.1073/pnas.2002557117 in Arctic arthropods Downloaded by guest on September 30, 2021 Fig. 3. Interannual variation and trends in relative standardized arthropod abundance at the family and higher taxonomic levels. Arthropod taxa are grouped into broad functional groups as outlined in SI Appendix, Table S3. Solid lines indicate where linear or segmented regression models were the best fit to the data (see detailed results in SI Appendix, Table S4).

analyses were carried out separately by family and habitat, we various climate variables, and some families were even affected found that abundance was associated with summer temperatures by different variables in each habitat (Fig. 4). For example, in in only 30% of the time series analyzed (SI Appendix, Tables wet fen and arid heath, Muscidae were positively influenced by S6–S8); in only one of these cases was previous summer tem- previous fall temperatures. Yet, in mesic heath, winter freeze– perature the only important driver (Linyphiidae in wet fen). Our thaw events and summer temperatures were the most important results also identified consistent support for positive effects of predictors. density-dependent feedbacks across taxa. Abundance was associated with temperature outside the Discussion summer season for many taxa, particularly the arid heath fauna, Recent commentaries of insect declines have called for stan- where conditions during the previous fall appear to be linked to dardized methodology and well-designed syntheses of population abundances during the following summer. We found varying dynamics (42), balanced analysis (11), and realistic interpretation effects of other drivers, including precipitation in the previous of results given the limitations of available data (43). With this year, winter duration, and winter freeze–thaw events. Taxa in study, we have met these goals by presenting and analyzing data mesic heath and wet fen in particular showed little fidelity to from a long-term standardized monitoring program in the Arctic overall abundance patterns across habitats, with a complex array over 24 y. We observed a nonlinear pattern in overall arthropod of largely negative climate effects, hampering our ability to abundance that included an initial decline followed by an abrupt generalize across habitats and taxa. There was high variability in recent increase. We also found arthropod populations to be highly model fit across taxa, with percentage deviance explained by the dynamic. Many families show contrasting, and often nonlinear, climate variables and density dependence ranging from 0 to patterns of abundance in comparison with the overall trends, a 80.6% (SI Appendix, Tables S5–S8). Unsurprisingly, we found a result that highlights how overall arthropod abundance patterns are high degree of variation among families in the importance of the inadequate proxies for abundance trends of individual taxa. While

4of8 | PNAS Høye et al. https://doi.org/10.1073/pnas.2002557117 Nonlinear trends in abundance and diversity and complex responses to climate change in Arctic arthropods Downloaded by guest on September 30, 2021 Pooled data Arid heath Mesic heath Wet fen

% Deviance 59 5635 78 34 39 81 47 46 47 35 57 59 43 56 31 47 58 69 35 49 60 36 SPECIAL FEATURE Summer temperature

Summer precipitation Coefficient

Spring temperature 0.0

Winter temperature 0.1 Winter freeze−thaw events 0.2 Winter duration

Previous fall temperature 0.3

Summer temperaturet−1 0.4 Precipitationt−1

Year

Abundancet−1 Overall Wet fen* Wet Muscidae Muscidae Muscidae Lycosidae Arid heath Collembola Collembola Aphidoidea Sciaridae § Linyphiidae Thomisidae Thomisidae Lycosidae § Lycosidae Collembola* Linyphiidae* Mesic heath* Chalcidoidea § Ichneumonidae Chironomidae § Chironomidae § Ichneumonidae*

Fig. 4. LASSO regression coefficients for a subset of the models fitted at least 30% null deviance explained (1-residual deviance/null deviance). The families along the horizontal axis are response variables and metrics on the vertical axis formed the explanatory variable matrix. The size of circles indicates the size of standardized coefficients. Black outlines around circles indicate bootstrapped CIs that do not encompass 0. Blue circles, negative coefficient; red circles, positive coefficient. *, coefficients presented are from models with one or two outliers removed; §, coefficients are from models that violated assumptions of normally distributed residuals or homogeneous variance. These models are included for completeness. The full set of LASSO regression coefficients and % null deviance explained is shown in SI Appendix, Tables S5–S8.

we did find support for strong associations between climate vari- As expected, family-level abundance trends were highly vari- ables outside the growing season and density dependence (as able and bore little resemblance to the overall patterns (except measured by previous year abundances) in relation to overall for the families Sciaridae and Chironomidae in arid heath). ECOLOGY abundance within and across habitats, the wide range of relation- We observed concerning declines in abundance of some taxa, ships at the family level suggests that generalization is not advisable. such as nymphalid butterflies. Spiders in the family Linyphiidae Overall, our findings suggest that, even for a “simple” food web, in mesic heath and flies in the family Muscidae in wet fen also there are few straightforward relationships between arthropod suffered steady declines over our study period. In previous work abundance and climate variables. at the same site, we have shown a range of positive, negative, and We have previously reported linear declines in habitat-level essentially unchanged population trends among individual spe- arthropod abundance at Zackenberg for a subset of taxa between cies in these two families (12, 13), which further supports our 1996 and 2016 (13), and we expected the pattern to continue, in general finding that analyses at coarse taxonomic levels mask line with other high-profile studies (2, 3, 44). However, with our responses at finer taxonomic resolution. Declines in some spe- complete dataset, we demonstrated that segmented regression cies of Muscidae may be driven by reduced soil moisture in warm techniques may be more appropriate for describing the dynamics years that negatively affects their aquatic or semiaquatic larvae of many arthropod time series. Had we used only linear regres- (13). Given their functional importance as predators and polli- sion for these data, a nonsignificant trend would have resulted in nators (25) and numerical abundance at Zackenberg, further all but mesic heath (SI Appendix, Table S2). Increases in abun- study on these taxa is particularly urgent. dance in the last few years of the study period at our site could be Results from our penalized (LASSO) regression findings in- dicated that climate variables are especially important predictors part of natural variation cycles or a response to changes in cli- of overall arthropod abundance (59% null deviance explained) mate. Even though the climatic change points are not coincident and for summed abundance in wet fen (78%). Climate variables with those of abundance, there has been a period of consistent, were also important for some individual taxa such as Collembola relatively high fall and winter temperatures since 2014, so it is in arid heath (81%) and Thomisidae in mesic heath (69%) possible that less harsh conditions during these cold periods fa- (Fig. 4). Our expectation that taxa particularly sensitive to changes cilitated increases in abundances of some groups. in soil moisture, such as Collembola, would show declines linked Interestingly, the change point in abundance did coincide with 1D 2D to warming summers was not fully met. While Collembola were change points for the diversity indices and , which began to negatively associated with warmer summers in all habitats, both decrease dramatically around 2014 (Fig. 2). These results mirror the abundance and summer temperature time series showed findings from another recent long-term study showing opposing complex trends. Other variables such as fall temperature (arid abundance and diversity trends (45). Together, our results indi- and mesic heath), winter temperature (mesic heath), and sum- cate that recent abundance increases are driven by a small number mer precipitation (wet fen) were also important for this taxon. of dominant taxa. In contrast, although overall family richness An additional finding from our analysis is the potential impor- 0D (Hill ) peaked in the central part of the study period, it showed tance of density-dependent feedbacks on long-term patterns in little overall change (Fig. 2). This lack of overall change is con- abundance as suggested by lagged abundance variables. For most sistent with other studies that have found overall species richness groups, density dependence was a more important predictor of to be stable, even over extended periods, in the face of climate abundance than any of the climate variables (Fig. 4). However, change (46). We speculate that the warmer summers during the density-dependent feedbacks often cannot be fully understood central part of the study led to higher abundance of particularly through abundance data alone and warrant demographic and temperature-limited taxa, but further study at the species level is experimental study. necessary to understand which species benefited from warmer Our findings align with previous work at Zackenberg showing summer temperatures and why. limited changes in abundance (13) and community composition

Høye et al. PNAS | 5of8 Nonlinear trends in abundance and diversity and complex responses to climate change https://doi.org/10.1073/pnas.2002557117 in Arctic arthropods Downloaded by guest on September 30, 2021 (28) in the wet fen as compared with the heath sites, suggesting would also contribute to a better understanding of which lineages that communities in sites with high soil moisture are buffered from and guilds are likely to exhibit correlated responses to climate the effects of climate change. Specifically, we observed no overall factors (12, 13, 60) and allow for generalization beyond single abundance or diversity patterns in the wet fen habitat at Zack- study systems. For example, some environmental pressures im- enberg, and there were few family-level trends. This habitat also pact soil-dwelling and semiaquatic larvae differently to flying exhibited a rather different set of relationships between abun- adults that are more influenced by air temperatures and wind dance and climate variables. However, the negative impact of speed. Similarly, arthropod responses vary across life stages spring temperatures on overall abundance in this habitat warrants (e.g., juvenile vs. adult, feeding vs. nonfeeding, diapausing vs. close monitoring, particularly as spring snowmelt timing is con- reproductive) with variable long-term impacts on population sidered to be a key driver of population dynamics (47). dynamics. Our study provides a starting point for formulating Another major finding from our results is that climate factors hypotheses about how taxa with particular functional traits may outside the growing season may be key drivers of arthropod respond to specific climate variables, which can be further abundance even in the harsh Arctic environment. The strong tested in experiments and through modeling. links between our climate variables and overall abundance Even with all of the aforementioned improvements in place, (Fig. 4), combined with the opposing patterns of overall abun- taxonomic identification will remain a hurdle: Too often it dance and diversity (Fig. 2), suggest that conditions character- proves time-consuming, expensive, and, for many groups, unre- ized by cool summers but warm “off-seasons” favor some taxa liable. Luckily, current technological developments offer some over others. For example, many of the taxa that have increased in promising outlooks for expanding basic monitoring work. Mo- recent years (e.g., Lycosidae, Collembola, Chironomidae, Mus- lecular identification tools can provide high taxonomic resolu- cidae, and Sciaridae in arid heath) are positively linked to fall tion, and new analytical approaches are emerging for deriving temperature, while some declining groups (e.g., Thomisidae) are both identity and abundance data (e.g., ref. 61). Image-based negatively associated with winter temperatures. Seasons outside solutions employing deep-learning models are an increasingly of the summer are often neglected in Arctic climate change re- powerful tool for automated insect identification and biomass search (40, 48), and thus the reasons for these patterns are un- estimation, as well as for monitoring of species and species in- clear. Warmer falls may provide fitness benefits to some groups teractions in their natural environment (62, 63). Relevant mod- via a delay in the onset of diapause or by allowing for additional eling tools can be further developed to scale up paired arthropod generations (49); warmer winters may improve growth rates for and microclimate data. Such future advances to improve un- some taxa (50) but could also increase mortality by reducing derstanding of arthropod population dynamics will also require insulating properties of snow (40). Warmer winter temperatures interdisciplinary efforts such as those stimulated by the Network can also differentially impact parasitoid species, with potential for Arthropods of the Tundra (64). knock-on effects for prey species (51). Although we found few family-level effects of the number of winter freeze–thaw events, Conclusions these are also thought to be an important factor in arthropod Unlike findings on insect trends from parts of Europe and North survival (40). Further studies are necessary to identify the com- America in the last decade, we have not found evidence of bination of climate conditions outside the growing season, which alarming overall population declines, although some taxa do have the strongest effects on Arctic arthropods, and the variation exhibit strong negative trends in abundance at our site. Opposing in critical climate conditions across taxa. trends in abundance and diversity also suggest a concerning in- crease in dominance of a smaller number of taxa. Importantly, Recommendations for Future Work. Our efforts at Zackenberg we demonstrate that overall measures of abundance can give highlight that a number of challenges remain in linking temporal misleading information about long-term dynamics of individual variation in environmental conditions to arthropod population taxa and the associated impacts of environmental factors. Our dynamics. It is clear that the general assumption that arthropods family-level trends depict widely differing and nonlinear pat- will benefit from increasing summer temperatures is too sim- terns. Moreover, we found that associations between abundances plified, and that identifying winners and losers of future Arctic and climatic variability were rarely generalizable across habitats. climate conditions will instead require both whole-year and Finally, we found that abundances were more commonly asso- multiyear approaches to modeling climate impacts on arthropod ciated with winter and fall conditions and possible within-pop- abundance (28, 41). The first and arguably most important goal ulation processes than with summer temperatures. We stress the is to develop a better understanding of the combination of cli- need for biome-wide coordination of targeted monitoring efforts, mate metrics most relevant for predicting responses to change in high taxonomic resolution of abundance data, trait data im- different arthropod lineages and guilds. For instance, desiccation stress is a key factor for arthropods inhabiting cold environments provements, continued development of efficient monitoring tech- (52) and may interact with temperature stress (53). Soil moisture nologies, and collection of relevant climate (and microclimate) variation can influence arthropod communities (54, 55) even variables as equally important in the pursuit of a fuller under- over small spatial scales (56). Data on snowmelt timing, soil standing of biotic and abiotic impacts on arthropod population temperature, and soil moisture should, therefore, be collected trends. The empirical findings we present here could have routinely at arthropod monitoring sites and at the localized scale emerged only from long-term, family-level, standardized data. of arthropod sampling and over all seasons (41). New efforts to We argue that, by integrating such monitoring with hypothesis- compile and model such microclimate data will help in our un- driven experiments and modeling, the research community will derstanding of relevant scales (57, 58). be better positioned to advance mechanistic understanding of Additionally, we suggest that future studies consider arthro- arthropod population dynamics (65). pod responses for different habitat types and at higher taxonomic Materials and Methods resolution, because it is clear that responses often cannot be generalized at order and family levels (Figs. 3 and 4). In the Sampling and Specimens. The Greenland Ecosystem Monitoring Program has been in operation at Zackenberg in northeast Greenland (74°28′N, 20°34′W) Arctic, coordination and collaboration, such as provided by the since 1996, and includes standardized pitfall trapping in five plots: one wet fen, Circumpolar Biodiversity Monitoring Program, could align plans two mesic heaths, and two arid heath plots (see ref. 20 for further details). Each for new monitoring programs with established programs to en- plot consisted of eight yellow pitfall traps (1997 to 2006) or four pitfall traps sure intersite comparability (12, 42, 59). Arthropod trait data- (1996 and 2007 to 2018) (each trap 5 m from its nearest neighbor). The traps bases that go beyond estimates of overall abundance or biomass were in operation during the growing season starting at snowmelt in late May

6of8 | PNAS Høye et al. https://doi.org/10.1073/pnas.2002557117 Nonlinear trends in abundance and diversity and complex responses to climate change in Arctic arthropods Downloaded by guest on September 30, 2021 to early June and ending by late August to early September, and were emptied in each habitat was set to the maximum annual number of individuals col- once a week. To compare across the years of sampling (1996 to 2018), we fo- lected within a family. We compared models of monotonic temporal trends in

cused on arthropod data from only June, July, and August. Data from 2010 climate, diversity, and abundance with segmented models [when breakpoints SPECIAL FEATURE were lost during transportation between Greenland and Denmark. Arthropods were detected (69)] built with the forecast package (70). Comparisons of were sorted to the family level for spiders and most insects, superfamily level models were based on Akaike information criterion corrected for small sam- for Aphidoidea, Chalcidoidea, and Coccoidea, and subclass for other arthro- ple sizes and Bayesian information criterion values. Normality of residuals and pods, and counted. We note that juveniles were included in the family-level other assumptions have been checked using the gvlma package (71). Detailed abundances for spiders. For some early years of the program, certain families of results are presented in SI Appendix, Tables S1, S2, and S4. Diptera were not sorted, but one family strongly dominated samples from later Relationships between family abundances and local climate variables were years (32), so we pooled the Chironomidae and Ceratopogonidae (hereafter assessed with a LASSO regression model. This procedure is useful in high- called “Chironomidae”), Anthomyiidae and Muscidae (hereafter called “Muscidae”), dimension situations with a large number of collinear predictors relative to and Mycetophilidae and Sciaridae (hereafter called “Sciaridae”). the sample size, as the coefficients of less important predictors are reduced The wet fen plot is composed primarily of mosses, grasses (e.g., Erio- to 0. This helps to avoid overfitting and issues related to multicollinearity. A phorum sp.), and sedges with scattered Arctic willow (Salix arctica). The regularization parameter, λ (72), is estimated to control the amount of mesic heath plots consist primarily of Arctic bell heather (Cassiope tetra- shrinkage and is optimized via cross-validation (73). The LASSO procedure is gona) and Arctic willow, grasses/sedges, and berry plants (Vaccinium), while essentially a method designed for prediction rather than inference, and the the arid heath plots have a greater dominance of mountain avens (Dryas estimation of statistical significance is problematic. However, some re- 2 octopetala) (32). All plots are located within an area less than 1 km . The wet searchers have proposed the use of the bootstrap to estimate CIs around the fen and arid heath plots typically have snowmelt 2 wk earlier than the mesic coefficients (74), and we use this approach to provide information on un- heath plots and soil moisture is highest in the wet fen and lowest in the arid certainty in the estimates of association between the climate variables and heath plots. Soil temperature (at 0-, 5-, and 10-cm depth) in mesic heath each arthropod family/taxon. dominated by C. tetragona and precipitation (mm) were measured hourly by The LASSO models were fitted using the glmnet package (75). For each a meteorological station located centrally and with a distance of less than family, a lag variable was produced consisting of previous year abundance 1 km from each arthropod sampling plot (66). values (Nt−1). The inclusion of this variable in the explanatory variable matrix allowed for temporal autocorrelation to be addressed where necessary, but Data and Analyses. We standardized weekly abundance counts for each ar- also meant that the first year was removed for each family (i.e., where the thropod group by calculating the abundance per trap per day for each plot in value for Nt−1 is unavailable). Year was also included in order to avoid over- each season. In subsequent abundance (but not diversity) analyses, we fo- interpreting linear trends. All explanatory variables were standardized during cused on taxonomic groups for which total abundance in each habitat across the model fitting. Models were initially cross-validated to obtain optimal λ the time series was higher than 500 individuals in total to ensure robust values using the leave-one-out approach (76) due to the small sample size (n = estimates of trends even for taxa that do not occur in all years. We note that 21). We also restricted the maximum number of explanatory variables that although we are omitting rare taxa, the likelihood of detecting trends is ECOLOGY could be selected to 5, to avoid overfitting and favor ease of interpretation in possibly lower for the less abundant taxa included in our analyses. We derived the selected models. Values of λ that minimized the mean square error were a range of climate variables of relevance to arthropod populations from the taken as optimal and used to obtain predictions and deviance-explained temperature and precipitation data. Summer precipitation was calculated values and subsequently assess residuals using the plotres function of the from the sum of June, July, and August measurements. Most Arctic arthro- plotmo package (77). Where residuals displayed problems with model as- pods overwinter beneath the snow. Thus, in order to estimate average sumptions of normality or heteroscedasticity, attempts were made to address seasonal temperatures experienced by arthropods for the previous fall these issues by removing a maximum of two outliers or removing problematic (October − and November − ), winter (December − through March ), spring t 1 t 1 t 1 t predictor variables and rerunning the analysis. Models with persistent and (April and May ), and summer (June ,July, and August ), we calculated the t t t t t extreme residual patterns were discarded as invalid, but results from models mean hourly temperature across these seasons using data collected at 0-, 5-, with moderate assumption violations are displayed and indicated for com- and 10-cm depth within the soil profile. We defined the length of winter as pleteness. Finally, coefficients were bootstrapped with 999 replicates using the number of days from the first day in fall with an average temperature the bootLasso function of the HDCI package (78). All statistical analyses were below 2 °C to the last day in spring with an average temperature below 2 °C. performed in the R 3.6.1 platform (79). The number of freeze–thaw cycles over the previous winter was calculated from the soil temperature data by counting the number of days with a maximum temperature above 0 °C and a minimum temperature below 0 °C. Data Availability. Species records and climate data reported in this paper have We also included lagged variables for summer precipitation (precip- been retrieved from the Greenland Ecosystem Monitoring Database (https:// doi.org/10.17897/V285-Z265). itationt−1) and summer temperature (summert−1), as conditions during the previous growing season could influence arthropod abundance. With the iNEXT package, we measured diversity with Hill numbers qD,which ACKNOWLEDGMENTS. D. L. W. is gratefully thanked for convening the “ ” quantify diversity in the number of taxa with increasing weight placed on session Insect Declines in the Anthropocene at the Entomological Society of America annual meeting 2019 in St. Louis, MO, which brought the group more abundant taxa at higher orders of diversity q (67, 68). We estimated of contributors to the special feature together. T. T. H. acknowledges fund- diversity for orders 0, 1, and 2, which correspond, respectively, to Hill 0D,the 1 ing from Independent Research Fund Denmark (Grant 8021-00423B). Data effective number of families in each habitat (richness), Hill D (the exponential were kindly provided by the Greenland Ecosystem Monitoring program. We 2 of Shannon’s entropy), and Hill D (inverse Simpson’s concentration index). thank the Danish Environmental Protection Agency for funding over the We used individual-based extrapolation curves for each year, family, and years. Valuable inputs to the manuscript from Matthew Forister, D. L. W., habitat to compute those three diversity metrics. The extrapolation end point M. R. B., and four anonymous reviewers are greatly appreciated.

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