This is an Accepted Manuscript of the article Reyes-García, V., García-del-Amo, D., Benyei, P., Fernández-Llamazares, Á., Gravani, K., Junqueira, A. B., ... & Mortyn, P. G. (2019). A collaborative approach to bring insights from local observations of climate change impacts into global climate change research. Current Opinion in Environmental Sustainability, 39, 1-8, available online at https://doi.org/10.1016/j.cosust.2019.04.007. ©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. Current Opinion in Environmental Sustainability Special Issue: Networks and networking: Collaborative work, innovation and far- reaching approaches to multiple sustainability challenges A collaborative approach to bring insights from local indicators of climate change impacts into global climate change research Victoria Reyes-Garcíaa,b,*, David García-del-Amob, Petra Benyeib, Álvaro Fernández-Llamazaresc,d, Konstantina Gravanib, André B. Junqueirab, Vanesse Labeyriee,f, Xiaoyue Lib, Denise M.S. Matiasb,g, Alex McAlvayh, P. Graham Mortynb,i, Anna Porcuna-Ferrerb, Anna Schlingmannb, Ramin Soleymani-Fardb a Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain b Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, 08193 Bellatera, Barcelona, Spain c Global Change and Conservation (GCC), Helsinki Institute of Sustainability Science (HELSUS), Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland d Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland e CIRAD, UPR GREEN, F-34398, Montpellier, France f GREEN, Univ Montpellier, CIRAD, Montpellier, France g German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE), Bonn, Germany h Department of Botany Ecology and Evolutionary Biology, University of Wisconsin- MadisonCornell University, MadisonIthaca, WisconsinNew York, USAUSA i Department of Geography, Universitat Autònoma de Barcelona, 08193 Bellatera, Barcelona, Spain * Corresponding author Victoria Reyes-García ICREA Research Professor Institute of Environmental Science and Technology (ICTA-UAB) ICTA-ICP, Edifici Z Carrer de les Columnes Universitat Autònoma de Barcelona E-08193, Bellaterra (Cerdanyola del Vallès-Barcelona) Tel: +34 93 586 8976 E-mail: Victoria.reyes@uab.cat Declarations of interest: none Abstract Bringing insights from Indigenous and local knowledge into climate change research requires addressing the transferability, integration, and scalability of this knowledge. Using a review of research on place-based observations of climate change impacts, we explore ways to address these challenges. Our search mostly captured scientist-led qualitative research, which -while facilitating place-based knowledge transferability to global research- did not include locally-led efforts documenting climate change impacts. We classified and organized qualitative multi-site place-based information into a hierarchical system that fosters dialogue with global research, providing an enriched picture of climate change impacts on local social-ecological systems. A network coordinating the scalability of place-based research on climate change impacts is needed to bring Indigenous and local knowledge into global research and policy agendas. Key words: Indigenous and local knowledge; Indigenous Peoples and Local Communities; local indicators of climate change impacts. Highlights 1. Place-based research on climate change impacts can benefit global climate change science; 2. Qualitative data can support place-based knowledge transferability to global research; 3. Local observations of climate change impacts can be organized to foster dialogue with global climate change research; 4. Research on local observations of climate change impacts is geographically biased and not universally connected; 5. A coordinated community of practice is needed to bring place-based climate knowledge into global climate change research and policy agendas. 2 Introduction There is overwhelming evidence that climate change has not only direct effects on the climatic system, but also a discernible influence on physical and biological systems [1–3], with resulting impacts on local livelihoods and cultures [4]. Most of this evidence comes from research in the natural sciences relying on large-scale weather records and the use of modelling techniques to describe impacts in data deficient regions [5]. While such research has advanced our understanding of climate change’s global magnitude, its methods are too coarse to detect impacts on local social-ecological systems [6] for which scientists have called for exploration of locally-grounded data sources [3]. Indigenous and Local Knowledge (ILK) has an untapped potential to contribute to research on climate change impacts on local social-ecological systems [7,8]. Indigenous Peoples and Local Communities (IPLC) with a history of interaction with the environment have developed intricate and complex knowledge systems (e.g., information, management techniques, institutions) that allow them to detect changes in local weather and climatic variability [8,9]. Attempts to bring insights from ILK into climate change research range from comparing ILK and scientific reports to validate the former [7] to encouraging synergies between both knowledge systems to obtain an enriched understanding of local climate change impacts [10]. Nevertheless, ILK continues to be largely absent in climate change impacts research [11] as epistemological [10], methodological [8], and scaling issues [12] challenge the transferability, integration, and scalability of ILK. Bringing insights from ILK into climate change impact research would require addressing such challenges. Addressing transferability calls for bringing ILK’s 3 qualitative and interpretative nature into standardized categories while recognizing the incommensurability of some aspects [10,13,14]; addressing integration calls for combining inputs from multi-site place-based research [15]; and addressing scalability calls for the creation of a community of practice that considers both the need to effectively downscale global models to resolutions useful for local climate adaptation and the need to ensure that placed-based information is effectively upscaled to global climate models [16]. Here, we analyse the academic literature documenting observations of local climate change impacts to explore how it addresses ILK transferability, integration, and scalability. Capitalizing upon previous efforts [8], we review scholarly publications documenting first-hand IPLC observations of changes in social-ecological systems attributed to climate change. Specifically, we reviewed 135 documents reporting 1363 first-hand observations of changes locally perceived as climate-driven on 198 locations in all inhabited continents (SM1 contains a methodological description). Transferability of observations of local climate change impacts Observations of local climate change impacts have been mainly documented using qualitative data collection techniques, with only 64 studies reporting the use of surveys (e.g., [17]). Qualitative data collection methods include participant observation (n=15 studies e.g., [18]), open-ended (n=18, e.g., [19]) and semi-structured interviews (n=60, e.g., [20]), community gatherings (n=7, e.g., [21]), and focus group discussions (n=50, e.g., [22]). Six studies relied on participatory methods for data collection, (e.g., [23]), and only two were steered or led by IPLC ([24,25]). Finally, only 17 studies embarked on cross-cultural comparisons (e.g., [26]). In other words, the predominant 4 approach used to document observations of local climate change impacts has relied on the collection of rich qualitative data. While not easily transferable, such work has been used in climate change research to buttress quantitative models and to assist in the triangulation and interpretation of results [9]. In response to calls to move anthropology to a “cross-scale, multi-sited research design and an interdisciplinary mix of interactive and structured tools and techniques” so “that the analytical focus is expanded to encompass local communities and their multiple action spaces as well as the higher spheres of decision-making, where policy and science are shaped” [27], researchers have recently started to look for patterns in qualitative reports from multiple sites (e.g., [6,7]). While interesting, this effort has been done a posteriori, without a clear a priori strategy that improves the prospects for comparability and transferability of qualitative observations (e.g., [28]). Examples exist of data collection methods designed to gather place-specific, yet comparable, knowledge from different locations (e.g., [29,30]). Such an approach would boost the transferability of multi-site observations of climate change impacts while valuing local ways of understanding and interacting with the environment. Integrating observations of local climate change impacts to the global setting Researchers [1–3], environmental agencies [31,32], and the Intergovernmental Panel on Climate Change (IPCC) [33] have proposed several categorizations of climate change impacts. Building on this, we propose a classification of qualitative place-based observations of climate change impacts. For this categorization, we specifically draw on the IPCC´s 5th Assessment Report (AR5) Working Group (WG) II´s [33]. We started creating a list of all observations of local climate change impacts documented in our 5 search and grouping verbatim observations referring to the same phenomenon (e.g., “higher temperatures” and “hotter”). We then classified observations in indicators, or more general descriptions of observations; what we call ‘local indicators of climate change impacts’ (LICCI) (SM2). We then grouped LICCIs based on the natural element or process reportedly being impacted; and further grouped these elements in 19 sub- systems ultimately corresponding to the four main systems: climatic, physical, biological and socioeconomic (Table 1). Drawing on scientific reports [31-33], we added some categories on the “Element impacted” level to encompass impacts not reported in the documents reviewed. We differentiate between ‘slow onset’ impacts (i.e., gradual trends observed in long timescales) and ‘rapid onset’ impacts (i.e., abrupt changes and/or extreme episodic events) [34]. TABLE 1 Our classification suggests that observations most commonly documented through qualitative research refer to impacts on the climatic system (n=609 observations, 44.7%), and particularly to changes in precipitation (n=269, 19.7%) (Table 1). Some of the impacts observed refer to very specific phenomena, such as trends in mean precipitation and extremes [35], but others refer to complex phenomena, such as changes in drought patterns [36] or seasonal events [37]. Some impacts detected with instrumental measurements (e.g., changes in atmospheric moisture) are not documented in the literature reviewed. Most observations on the climatic system refer to slow onset impacts (83.3%) (e.g., changes in the length of seasons). IPLC also report impacts on the local physical system (n=320; 23.5%), including observations of impacts on the marine [38] and the terrestrial physical systems [39], among which observations of impacts on the freshwater (e..g, [40], 10.1%) and the 6 cryosphere systems (e.g., [41], 7.7%) (Table 1). Impacts on some elements of the physical systems documented in the IPCC AR5 are rare in the literature (e.g., impacts on ocean salinity and currents are only mentioned once) and others (e.g., impacts related to ocean acidification, hypoxia, or soil salinization) are not documented. Almost all observations of impacts on elements of the physical system (91%) correspond to slow onset impacts (e.g., permafrost). IPLC also observe impacts on the biological system (n=224, 14.6%), and particularly changes in terrestrial wild flora (n=73, 5.4%, such as changes in abundance of species [22] or phenology [26]) (Table 1). There are several differences between observations of impacts on elements of the biological systems reported in our search and in the IPCC report. For example, local observations of climate change impacts on forests focus on changes in vegetation cover or height (e.g., [23]), whereas the IPCC report emphasizes forests’ productivity. Similarly, the IPCC report mentions impacts on the extent of agricultural areas and provides indicators of impacts on marine and freshwater species, while these are not reported on documents in our search. Inversely, the IPCC report points at an information gap regarding impacts on hunting and wild food collection, but such impacts are documented by IPLC (e.g., [42]). Only 3.1% of the local observations of impact on elements of the biological system are rapid onset impacts, mostly corresponding to forest fires. Finally, 210 (15.4%) documented observations refer to impacts on elements of the human system, of which 103 (7.6%) correspond to impacts on the agricultural system (Table 1). The literature includes few mentions of impacts on health and nutrition (3.4%) or infrastructure (0.3%), probably reflecting sampling biases (see SM1). As for impacts on the biological system, the IPCC report lists impacts on health, 7 nutrition, and agricultural infrastructures not reported in the reviewed literature. All impacts documented in the human system are slow onset impacts. The categorization of local climate change impact observations provides several insights. First, in all inhabited continents, IPLC observe slow onset climate change impacts on multiple elements of their social-ecological system. While IPLC might not detect some impacts (e.g., soil salinization), they seem to observe changes on the biological system resulting from them (e.g., changes in wild flora). Second, local observations can be organized in a way that fosters dialogue with global climate change research, including the IPCC. Categorization, however, is dependent on the existence of qualitative data that permits the correct interpretation of information. And third, the literature reviewed suggests that, to detect change, IPLC use multiple elements of their knowledge system simultaneously. While this highlights IPLC´s understanding of complex interactions in social-ecological systems, it also adds an unsolved layer of complexity on the integration of this body of knowledge to global climate research. Scalability of local climate change impacts observations To explore the scalability potential of observations of local climate change impacts to global research, we analyse document’s spatial distribution and connectivity. The analysis of the 198 locations documented shows an unbalanced geographical distribution (Fig. 1; SM3). Most locations concentrate on tropical regions (n=65), and particularly on the Congo Basin and the East African Mountains. Locations in the temperate climate (n=49) concentrate in the Himalayan range. Polar Regions (n=33), cold (n=29), and arid climates (n=22) have drawn less scholarly attention (SM4). The higher diversity of LICCI has been documented in Polar Regions (n=69) and the lowest 8 in arid regions (n=39). A similar number of LICCI has been documented in tropical (n=62), temperate (n=62), and cold regions (n=59). FIGURE 1 An important characteristic of the locations where observations of climate change impacts were documented is their distance to weather stations whose data is included in the datasets CRUTEM4 used for assessing anthropogenic climate change [43]. Thus, half of the locations documented are in areas with <6 and <27 weather stations within a 200km and a 500km radius (compared to a maximum of 61 and 346 weather stations for a case study in Italy) (SM5). Given the deficient weather station coverage, observations of local climate change impacts could become an alternative data source to evaluate the performance of climate models in these areas. We also explored the potential for scalability by analysing the connectivity through time within the literature reviewed, measured through a bibliometric direct citation network using CitNetExplorer (SM1). We found that 36.0% of the documents analysed had no citation relations with the other publications, indicating minimal integration of more than a third of the literature surveyed. The remaining publications formed two interconnected components. Our clustering analysis produced seven clusters and indicated some degree of regional patterning, suggesting a citation pattern based on geographical criteria. For example, 71.4% of the publications in the blue cluster focused on Asia, with 52.4% centred around the Himalayas; similarly 94.4% of publications in the green cluster focused on Arctic regions. Thus, while incipient regional networks seem to be emerging, much of the literature is not integrated nor in communication with global research efforts. FIGURE 2 9 Conclusion The review of research documenting ILK-based observations of local climate change impacts provides three important insights that should guide future efforts to bring ILK into global climate change impacts research. First, the use of qualitative methodologies for data collection might facilitate the transferability of local observations into global research by providing the context needed to bring into standardized categories ILK interpretative nature. However, ensuring that holistic observations of complex social-ecological processes are meaningfully captured remains a challenge. Future strategies to improve transferability should include a conscious focus on the web of relations between elements of the social-ecological systems and how climate change impacts on them are captured through ILK holistic view. Future strategies should also foster continuous dialogue with ILK-holders to ensure that ILK historical and contextual complexities are not overlooked [10,11,44]. Second, multi-site qualitative place-based information can be integrated in a way that provides an enriched picture of climate change impacts on local social-ecological systems (see also [16]). Given IPLC increasing global interest to build cross-cultural narratives around climate change impacts and to connect their local realities to global climate change discourses (e.g., [45]), the classification proposed here might allow synergies across different knowledge systems documenting climate change impacts. Finally, while the literature used illustrates ILK potential to become an alternative data source to evaluate the performance of global climate models, it also shows important geographical gaps and insufficient coordinating efforts to reach that potential. Thus, despite research increase, we still lack a community of practice (i.e., researchers, IPLC, practitioners, decision-makers) committed to upscaling ILK-based 10 observations of climate change impacts in a coordinated way. Such strategy is common in research collecting large volumes of social-ecological data (e.g., [46]) and is increasingly combined with citizen science and community-based environmental monitoring initiatives gathering multi-site grounded data (e.g., [15]). Creating such community of practice is a necessary step to bring place-based climate knowledge into resolutions that can influence climate change-related research and policy agendas. Acknowledgements Research leading to this work has received funding from the European Research Council (ERC) under grant agreement No 771056-LICCI-ERC-2017-COG and from the Spanish government through a grant of the Ministry of Economy and Competitiveness (CSO2014-59704-P). García-del-Amo and Reyes-García acknowledge financial support from the Spanish Ministry of Economy and Competitiveness, through the “María de Maeztu” Programme for Units of Excellence in R&D (MdM-2015-0552). Thanks to M. Gueze for cartographical help. References 1. Peñuelas J, Sardans J, Estiarte M, Ogaya R, Carnicer J, Coll M, Barbeta A, Rivas-Ubach A, Llusià J, Garbulsky M, et al.: Evidence of current impact of climate change on life: a walk from genes to the biosphere. Glob Chang Biol 2013, 19:2303–2338. 2. Scheffers BR, De Meester L, Bridge TCL, Hoffmann AA, Pandolfi JM, Corlett RT, Butchart SHM, Pearce-Kelly P, Kovacs KM, Dudgeon D, et al.: The broad footprint of climate change from genes to biomes to people. Science (80- ) 2016, 354:aaf7671. 3. Rosenzweig C, Neofotis P: Detection and attribution of anthropogenic climate change impacts. Wiley Interdiscip Rev Clim Chang 2013, 4:121–150. 4. Adger WN, Barnett J, Brown K, Marshall N, O’Brien K: Cultural dimensions of climate change impacts and adaptation. Nat Clim Chang 2013, 3:112–117. 5. Stocker T, Qin D, Plattner G-K, Tignor M, Allen S, Boschung J, Nauels A, Xia Y, Bex V, Midgley P: Climate Change 2013: The Physical Science Basis. 11 Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 2013. 6. Alexander C, Bynum N, Johnson E, King U, Mustonen T, Neofotis P, Oettlé N, Rosenzweig C, Sakakibara C, Shadrin V, et al.: Linking Indigenous and Scientific Knowledge of Climate Change. Bioscience 2011, 61:477–484. **7. Savo V, Lepofsky D, Benner JP, Kohfeld KE, Bailey J, Lertzman K: Observations of climate change among subsistence-oriented communities around the world. Nat Clim Chang 2016, 6:462–473. This is a meta-analysis of 10,660 observations of climatic changes by subsistence- oriented communities. Results show that increases in temperature and changes in seasonality and rainfall patterns are widespread and show patterns consistent with simulated trends in surface air temperature taken from the ensemble average of CMIP5 models, for the period 1955-2005. **8. Reyes-García V, Fernández-Llamazares Á, Guèze M, Garcés A, Mallo M, Vila- Gómez M, Vilaseca M: Local indicators of climate change: the potential contribution of local knowledge to climate research. Wiley Interdiscip Rev Clim Chang 2016, 7:109–124. This is a meta-analysis of published peer-reviewed documents reporting local indicators of climate change including both local observations of climate change and observed impacts on the biophysical and the social systems. It suggest that the rich and fine- grained knowledge in relation to impacts on biophysical systems could provide more original contributions to our understanding of climate change at local scale. 9. Fernández-Llamazares Á, Garcia RA, Díaz-Reviriego I, Cabeza M, Pyhälä A, Reyes-García V: An empirically tested overlap between indigenous and scientific knowledge of a changing climate in Bolivian Amazonia. Reg Environ Chang 2017, 17. 10. Tengö M, Hill R, Malmer P, Raymond CM, Spierenburg M, Danielsen F, Elmqvist T, Folke C: Weaving knowledge systems in IPBES, CBD and beyond—lessons learned for sustainability. Curr Opin Environ Sustain 2017, 26–27:17–25. *11. Ford JD, Cameron L, Rubis J, Maillet M, Nakashima D, Willox AC, Pearce T: Including indigenous knowledge and experience in IPCC assessment reports. Nat Clim Chang 2016, 6:349–353. 12. Gamble DW, Campbell D, Allen TL, Barker D, Curtis S, McGregor D, Popke J: Climate Change, Drought, and Jamaican Agriculture: Local Knowledge and the Climate Record. Ann Assoc Am Geogr [date unknown], 100:880–893. *13. Pyhälä A, Fernández-Llamazares Á, Lehvävirta H, Byg A, Ruiz-Mallén I, Salpeteur M, Thornton TF: Global environmental change: local perceptions, understandings, and explanations. Ecol Soc 2016, 21:art25. 14. Klenk N, Fiume A, Meehan K, Gibbes C: Local knowledge in climate adaptation research: moving knowledge frameworks from extraction to co- production. Wiley Interdiscip Rev Clim Chang 2017, 8:e475. **15. Mosites E, Lujan E, Brook M, Brubaker M, Roehl D, Tcheripanoff M, Hennessy T: Environmental observation, social media, and One Health action: A description of the Local Environmental Observer (LEO) Network. One Heal 2018, 6:29–33. The Local Environmental Observer (LEO) Network initiative (www.leonetwork.org) engages tribal health workers and local observers in Alaska in the gathering and sharing 12 of environmental change observations related to human and animal wellbeing by using a website and a mobile application. 16. Balvanera P, Calderón-Contreras R, Castro AJ, Felipe-Lucia MR, Geijzendorffer IR, Jacobs S, Martín-López B, Arbieu U, Speranza CI, Locatelli B, et al.: Interconnected place-based social–ecological research can inform global sustainability. Curr Opin Environ Sustain 2017, 29:1–7. 17. Kosmowski F, Leblois A, Sultan B: Perceptions of recent rainfall changes in Niger: a comparison between climate-sensitive and non-climate sensitive households. Clim Change 2016, 135:227–241. 18. Moerlein KJ, Carothers C: Total Environment of Change: Impacts of Climate Change and Social Transitions on Subsistence Fisheries in Northwest Alaska. Ecol Soc 2012, 17:art10. 19. Manandhar S, Vogt DS, Perret SR, Kazama F: Adapting cropping systems to climate change in Nepal: a cross-regional study of farmers’ perception and practices. Reg Environ Chang 2011, 11:335–348. 20. Bollig M, Schulte A: Environmental Change and Pastoral Perceptions: Degradation and Indigenous Knowledge in Two African Pastoral Communities. Hum Ecol [date unknown], 27:493–514. 21. Sukh T, Fassnacht SR, Fernández-Giménez M, Laituri M: Local Understanding of Hydro-climatic Changes in Mongolia. 2011. 22. Postigo JC: Perception and Resilience of Andean Populations Facing Climate Change. J Ethnobiol 2014, 34:383–400. 23. Gill H, Lantz T: A Community-Based Approach to Mapping Gwich’in Observations of Environmental Changes in the Lower Peel River Watershed, NT. J Ethnobiol 2014, 34:294–314. 24. Ashford G, Castleden J: Inuit Observations on Climate Change Final Report. 2001. 25. Douglas V, Chan HM, Wesche S, Dickson C, Kassi N, Netro L, Williams M: Reconciling Traditional Knowledge, Food Security, and Climate Change: Experience From Old Crow, YT, Canada. Prog Community Heal Partnerships Res Educ Action 2014, 8:21–27. 26. Chaudhary P, Bawa KS: Local perceptions of climate change validated by scientific evidence in the Himalayas. Biol Lett 2011, 7:767–770. 27. Roncoli C: Ethnographic and participatory approaches to research on farmers’ responses to climate predictions. Clim Res 2006, 33:81–99. 28. Burton I, Bizikova L, Dickinson T, Howard Y: Integrating adaptation into policy: upscaling evidence from local to global. Clim Policy 2007, 7:371–376. 29. Reyes-García V, Guèze M, Díaz-Reviriego I, Duda R, Fernández-Llamazares Á, Gallois S, Napitupulu L, Orta-Martínez M, Pyhälä A: The adaptive nature of culture. A cross-cultural analysis of the returns of local environmental knowledge in three indigenous societies. Curr Anthropol 2016, 57:761–784. 30. Reyes-García V, Fernändez-Llamazares À, Gueze M, Gallois S: Does weather forecasting relate to foraging productivity? An empirical test among three hunter-gatherer societies. Weather Clim Soc 2018, 10. 31. Agency UEP: Climate change indicators in the United States, 2016. 2016. 32. Füssel H-M, Kristensen P, Jol A, Marx A, Hildén M: Climate change, impacts and vulnerability in Europe 2016. An indicator-based report. 2017. 33. Cramer W, Yohe G, Auffhammer M, Huggel C, Molau U, Dias M, Solow A, 13 Stone D, Tibig L: Detection and attribution of observed impacts. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change. Edited by Field C, Barros V, Dokken D, Mach K, Mastrandrea M, Bilir T, Chatterjee M, Ebi K, Estrada Y, Genova R, et al. Cambridge University Press; 2014:979–1037. 34. Siegele L: Loss and Damage: The Theme of Slow Onset Impact. Germanwatch; 2012. 35. Savo V, Caneva G, McClatchey W, Reedy D, Salvati L: Combining Environmental Factors and Agriculturalists’ Observations of Environmental Changes in the Traditional Terrace System of the Amalfi Coast (Southern Italy). Ambio 2014, 43:297–310. 36. Leonard S, Parsons M, Olawsky K, Kofod F: The role of culture and traditional knowledge in climate change adaptation: Insights from East Kimberley, Australia. Glob Environ Chang 2013, 23:623–632. 37. Marin A: Riders under storms: Contributions of nomadic herders’ observations to analysing climate change in Mongolia. Glob Environ Chang 2010, 20:162–176. 38. Lauer M, Aswani S: Indigenous Knowledge and Long-term Ecological Change: Detection, Interpretation, and Responses to Changing Ecological Conditions in Pacific Island Communities. Environ Manage 2010, 45:985– 997. 39. Below TB, Schmid JC, Sieber S: Farmers’ knowledge and perception of climatic risks and options for climate change adaptation: a case study from two Tanzanian villages. Reg Environ Chang 2015, 15:1169–1180. 40. Oyerinde GT, Hountondji FCC, Wisser D, Diekkrüger B, Lawin AE, Odofin AJ, Afouda A: Hydro-climatic changes in the Niger basin and consistency of local perceptions. Reg Environ Chang 2015, 15:1627–1637. 41. Riseth JÅ, Tømmervik H, Helander-Renvall E, Labba N, Johansson C, Malnes E, Bjerke JW, Jonsson C, Pohjola V, Sarri L-E, et al.: Sámi traditional ecological knowledge as a guide to science: snow, ice and reindeer pasture facing climate change. Polar Rec (Gr Brit) 2011, 47:202–217. 42. Tam BY, Gough WA, Edwards V, Tsuji LJS: The impact of climate change on the well-being and lifestyle of a First Nation community in the western James Bay region. Can Geogr / Le Géographe Can 2013, 57:441–456. 43. Jones PD, Lister DH, Osborn TJ, Harpham C, Salmon M, Morice CP: Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to 2010. J Geophys Res Atmos 2012, 117:n/a- n/a. *44. Löfmarck E, Lidskog R: Bumping against the boundary: IPBES and the knowledge divide. Environ Sci Policy 2017, 69:22–28. *45. Galloway-McLean K: Advance guard: climate change impacts, adaptation, mitigation and indigenous peoples: a compendium of case studies. 2017, **46. Rogora M, Frate L, Carranza ML, Freppaz M, Stanisci A, Bertani I, Bottarin R, Brambilla A, Canullo R, Carbognani M, et al.: Assessment of climate change effects on mountain ecosystems through a cross-site analysis in the Alps and Apennines. Sci Total Environ 2018, 624:1429–1442. 14 The LTER Network (http://www.lter-europe.net/) uses a network of research sites in the Apennines and the Alps managed by professional scientists that gather high-quality ecological data periodically to assess climate change impacts on ecological processes. 47. Peel MC, Finlayson BL, McMahon TA: Updated world map of the Köppen- Geiger climate classification. Hydrology and Earth System Sciences Discussions 2007, 11:1633-1644. 48. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F: World Map of the Köppen- Geiger climateclassification updated. Meteorol. Z. 2006, 15:259-263. 15 Figure captions Fig. 1: Distribution of world meteorological stations based on the CRUTEM.4.6.0.0 dataset [43] and locations of the reviewed case studies along with the main climates according to the Koeppen-Geiger classification [47,48]. Figure 2: Citation network of publications reviewed. Circles represent publications and are labeled with the first author’s surname. The position of the publication on the y-axis indicates time of publication. Lines indicate citation relations between publications. 16 Table 1 Classification and number of observations (N) of local climate change impacts on systems, sub-systems, and elements System Sub-system Element N Onset Climatic Temperature Mean temperature 70 S (n=609) (n=102) Temperature extremes 32 R Precipitation Mean precipitation 90 S (n=269) Precipitation extremes 32 R Precipitation distribution, variability, predictability 90 S Drought 45 S Clouds and fog 12 S Air masses (n=78) Wind 40 S Storm (hail/dust/sand) 28 R Cyclones, tornadoes 10 R Seasonal events Seasonal ice formation changes 26 S (n=160) Duration and timing of seasons 68 S Seasonal temperature changes 42 S Seasonal precipitation changes 24 S Physical Marine physical Sea temperature 3 S system systems (ocean & Sea level 17 S (n= 320) sea) (n=34) Coastal erosion/sedimentation 10 S Ocean currents 3 S Ocean salinity 1 S Freshwater Mean river flow 36 S physical system River and lake floods 20 S (continental Fresh water availability/quality 52 S waters) (n=138) Water temperature of rivers and lakes 2 S Lake level 10 S Phreatic/underground water 10 S River bank / pond erosion/sedimentation 8 S Terrestrial physical Soil erosion/landslides 27 S/R system (soil & Soil moisture 14 S land) (n=43) Soil temperature 1 S Edaphic properties (fertility, structure & biology) 15 S Earthquake and tsunamis 1 R Cryosphere (ice & Snowfall and snow cover 41 S snow) (n=105) Ice sheet / lake and river ice 18 S Glaciers 21 S Permafrost 11 S Sea ice 14 S Biological Marine biological Marine spp abundance 16 S (n=224) system (n=46) Marine spp composition * S Marine spp habitat range (distribution) 5 S Marine spp invasive alien species 3 S Marine spp disease/pest/mortality 16 S Marine spp phenology 2 S Marine spp reproduction 1 S Marine game spp quality 3 S 17 System Sub-system Element N Onset Freshwater wild Fresh water spp abundance 14 S fauna (n=31) Fresh water spp. composition * S Fresh water spp habitat range (distribution) 4 S Fresh water spp invasive alien species 1 S Fresh water spp disease/pest/mortality 1 S Fresh water spp phenology 10 S Fresh water spp reproduction 1 S Fresh water spp quality * S Terrestrial wild Terrestrial fauna abundance 16 S fauna (n=56) Terrestrial fauna composition * S Terrestrial fauna habitat range (distribution) 12 S Terrestrial fauna invasive alien species 5 S Terrestrial fauna disease/pest/mortality 13 S Terrestrial fauna phenology 10 S Terrestrial fauna reproduction * S Terrestrial game spp quality * S Terrestrial wild Wild flora abundance (excluding timber & NTFP) 14 S flora (fungi-plants- Wild flora composition * S shrubs-trees) (n=73) Wild flora habitat range (distribution) 2 S Wild flora invasive alien species 2 S Wild flora disease/pest/mortality 5 S Wild flora phenology 13 S Wild flora productivity and quality 6 S Timber forest sp. composition and structure 12 S Timber forest sp. availability and quality 7 S Non-timber forest products availability and quality 12 S Land cover change Habitat degradation 11 S (n=18) Forest fires 7 R Human Aquaculture Aquaculture productivity and quality * S (n=210) (marine & fresh Aquaculture disease/pest/mortality * S water) Aquaculture phenology and reproduction * S Cultivated plant Cultivated spp productivity and quality 43 S spp (crops, Seed or propagule availability or quality * S orchards) Disease/pest/mortality of crops 37 S (n=103) Crop weeds (invasive alien species) 4 S Phenology and reproduction 19 S Pastures & Pasture availability and productivity 17 S grassland (n=28) Pasture spp composition, distribution & quality 7 S Pasture disease/pest/mortality * S Pasture weeds (invasive alien species) 3 S Pasture phenology and reproduction 1 S Livestock (n=29) Livestock productivity and quality 7 S Livestock spp. composition * S Livestock disease/pest/mortality 20 S Livestock phenology and reproduction 2 S Human health Diseases 19 S (n=47) Health injuries, physical affection 9 S Hunger 11 S Conflicts * S 18 System Sub-system Element N Onset Cultural/spiritual/ identity values 8 S Infrastructure(n=3) Transport (e.g. trails) 3 S [S] slow onset impacts;[R] rapid onset impacts. * we did not find observations corresponding to these LICCIs in the literature, but it is possible that these LICCIs were overlooked in our search as they are not evident in the papers. 19 Fig. 1 20 Figure 2 21