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Healthy ecosystems for human and animal health: Science diplomacy for responsible development in the Arctic

The Nordic Centre of Excellence, (Climate-change effects on the epidemiology of infectious diseases and the impacts on Northern societies)

Published online by Cambridge University Press:  18 October 2021

B. Evengård
Department of Clinical Microbiology, Umeå University, 901 87Umeå, Sweden
G. Destouni
Department of Physical Geography, and Bolin Centre for Climate Research, Stockholm University, 106 91, Stockholm, Sweden
Z. Kalantari
Department of Physical Geography, and Bolin Centre for Climate Research, Stockholm University, 106 91, Stockholm, Sweden Department of Sustainable Development, Environmental Science and Engineering, Sustainability Assessment and Management, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden
A. Albihn
Department of Chemistry, Environment, and Feed hygiene, National Veterinary Institute, Uppsala, Sweden
C. Björkman
Department of Ecology, Swedish University of Agricultural Sciences, 750 07Uppsala, Sweden
H. Bylund
Department of Ecology, Swedish University of Agricultural Sciences, 750 07Uppsala, Sweden
E. Jenkins
Department of Veterinary Microbiology, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
A. Koch
Greenland Center for Health Research, Ilisimatusarfik-University of Greenland, 3905Nuuk, Greenland Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
N. Kukarenko
Department of Philosophy and Sociology, Northern Arctic Federal University, 163002Arkhangelsk, Russia
D. Leibovici
School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
J. Lemmityinen
Finnish Meteorological Institute, FIN-00101Helsinki, Finland
M. Menshakova
Department of Natural Sciences, Murmansk Arctic State University, 183038Murmansk, Russia
G. Mulvad
Greenland Center for Health Research, Ilisimatusarfik-University of Greenland, 3905Nuuk, Greenland
L.M. Nilsson
Várdduo, Centre for Sámi Research, Umeå University, 901 87Umeå, Sweden Department of Epidemiology and Global Health, Umeå University, 901 87Umeå, Sweden
A. Omazic
Department of Chemistry, Environment, and Feed hygiene, National Veterinary Institute, Uppsala, Sweden
N. Pshenichnaya
Central Research Institute of Epidemiology, 111123Moscow, Russia
S. Quegan
School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
A. Rautio
Arctic Health, Faculty of Medicine, University of Oulu, FI-90014Oulu, Finland Thule Institute, University of the Arctic, FI-90014Oulu, Finland
B. Revich
Institute of Economic Forecasting, Russian Academy of Science, 117418, Moscow, Russia
P. Rydén
Department of Mathematics and Mathematical Statistics, Umeå University, 901 87Umeå, Sweden
A. Sjöstedt
Department of Clinical Microbiology, Umeå University, 901 87Umeå, Sweden
N. Tokarevich
Laboratory of Zoonoses, St Petersburg Pasteur Institute, St Petersburg, Russia
T. Thierfelder
Department of Energy & Technology, Swedish University of Agricultural Sciences SLU, Uppsala, Sweden
D. Orlov
Faculty of Geography, Lomonosov Moscow State University, 119991Moscow, Russia
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Climate warming is occurring most rapidly in the Arctic, which is both a sentinel and a driver of further global change. Ecosystems and human societies are already affected by warming. Permafrost thaws and species are on the move, bringing pathogens and vectors to virgin areas. During a five-year project, the CLINF – a Nordic Center of Excellence, funded by the Nordic Council of Ministers, has worked with the One Health concept, integrating environmental data with human and animal disease data in predictive models and creating maps of dynamic processes affecting the spread of infectious diseases. It is shown that tularemia outbreaks can be predicted even at a regional level with a manageable level of uncertainty. To decrease uncertainty, rapid development of new and harmonised technologies and databases is needed from currently highly heterogeneous data sources. A major source of uncertainty for the future of contaminants and infectious diseases in the Arctic, however, is associated with which paths the majority of the globe chooses to follow in the future. Diplomacy is one of the most powerful tools Arctic nations have to influence these choices of other nations, supported by Arctic science and One Health approaches that recognise the interconnection between people, animals, plants and their shared environment at the local, regional, national and global levels as essential for achieving a sustainable development for both the Arctic and the globe.

Review Article
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© The Author(s), 2021. Published by Cambridge University Press


Our civilisation reached the Anthropocene when the activities of Homo sapiens became the dominant influence on climate and the environment. This started when the first industrialisation occurred at the end of the 18th century. Early predictions of its effects came from internationally active scientists like Alexander von Humboldt and Ernst Haeckel, who developed the concepts of ecology and ecosystems. The latter includes humans as integral parts of ecosystems along with other species and has developed into the “One Health concept” describing the interconnections of people, animals and their shared environment. Charles Darwin carried these revolutionary thoughts further, while Svante Arrhenius proposed that industrialisation would lead to emissions of CO2 that would change the climate. In 1958, Charles D. Keeling began documenting continually and rising atmospheric CO2 levels, a crucial example of many different phenomena showing that we are living in the midst of such anthropogenic change. With changing climate, fauna and flora are on the move, bringing microbial organisms with them into virgin territory. Zoonotic pathogens, that is microorganisms transmitted between animals and humans, constitute at least 70% of emerging infections, sometimes with global spread, as illustrated by the SARS-CoV-2 pandemic. It is only through international collaboration that understanding of these rapidly ongoing processes can be deepened and that the required preparedness, and mitigation and adaptation can develop. Through the funding body NordForsk of the Nordic Council of Ministers, a Nordic Center of Excellence; Climate-change effects on the epidemiology of infectious diseases and the impacts on Northern societies, Clinf, was established. has for five years focused on climate change and its impact on ecosystems, health of humans and animals, and development of societies in the North.

Here we describe how scientists from eight countries have worked to interpret nature in the Arctic and predict the conditions leading to outbreaks of climate-sensitive infections, describing possible methods illustrated with the case of tularemia, and thus support evidence-based policy aimed at preventing and mitigating their consequences.

The Arctic

The Arctic provides an extreme natural environment, manifested in many climatic, geochemical and biotic factors. The natural world and human societies have adapted to cope with these extreme conditions, but a new and major main threat to the health of people and animals in the Arctic is associated with global warming, both because of its intense effects on human and animal organisms, and the scale of the territories it covers (Isaev, Reference Isaev2003; Parkinson et al., Reference Parkinson, Evengard, Semenza, Ogden, Børresen, Berner, Brubaker and Albihn2014). Over the past few decades, the Arctic has warmed more than twice as rapidly as the rest of the world, as both sentinel for and driver of global change (Overland et al., Reference Overland, Hanna, Hanssen-Bauer, Kim, Walsh, Wang, Bhatt and Thoman2020). This pattern is true for the entire territory of the Arctic. According to Roshydromet (The Russian Federal Service for Hydrometeorology and Environmental Monitoring), climate change in the Russian Arctic is more intense than in any other part of the country (Roshydromet, 2019). Over 30 years (1990–2019), the average annual temperature rose by 0.81°С every 10 years, that is 2.43°C over 30 years. Warming is also evidenced by a rapid decrease in Arctic ice cover, an increase in the thickness of the seasonally thawed permafrost layer, a decrease in the duration of the snow cover and other indicators (AMAP 2021). Likewise, the Canadian Arctic is warming at twice the rate of the rest of Canada and three times the global average, with a warming trend of 2.30 C over the last 68 years (Bush & Lemmen, Reference Bush and Lemmen2019).

Expected increase of climate-sensitive infection

With regard to recent and historic epidemics and pandemics, climatic changes can lead to shifts in the geographic boundaries of pathogens, hosts and vectors, and amplify transmission of endemic climate-sensitive pathogens (Kovats, Campbell-Lendrum, McMichael, Woodward & Cox, Reference Kovats, Campbell-Lendrum, McMichael, Woodward and Cox2001; Kutz, Hoberg, Polley&Jenkins, Reference Kutz, Hoberg, Polley and Jenkins2005; Parkinson et al., Reference Parkinson, Evengard, Semenza, Ogden, Børresen, Berner, Brubaker and Albihn2014; Pecl et al., Reference Pecl, Araújo, Bell, Blanchard, Bonebrake, Chen, Clark and Williams2017). Climate change can transform the geographic distribution and seasonal patterns of transmission of a range of infectious and parasitic diseases. Birds are known being vectors of importance. Warming causes some species to migrate to higher latitudes and altitudes, bringing new diseases; conversely, endemic Arctic-adapted species may face extinction at the top of a warming world. Both human-specific and zoonotic pathogens can cause disease in humans; however, even non-zoonotic pathogens can cause significant economic damage, threatening trade, livestock production and conservation of wildlife important for human harvest, especially in the Arctic (Evengård & Thierfelder, Reference Evengård and Thierfelder2020).

Temperature and humidity affect the rate of development, survival and reproduction of pathogens and disease vectors. Higher temperatures can allow infected carriers to survive the unfavourable period of the year, thereby increasing the population size and expanding the range of the species. A role is played by changes in the timing and thickness of snow cover, which affects the conditions of overwintering for hosts and vectors (Kershengolts, Chernyavsky, Repin, Nikiforov & Sofronova, Reference Kershengolts, Chernyavsky, Repin, Nikiforov and Sofronova2009; Revich, Reference Revich2008). The Arctic and other high-latitude regions with low diversity of animal, plant and microbial species, and with increases of surface temperature above the global average, are excepted to experience more severe changes in infectious disease patterns than other regions (Hassol, Reference Hassol2004).

Reappearance of old microorganisms

There is a probability that microorganisms, including serious pathogens like anthrax, will re-appear in ecosystems due to thawing of burial places of people and animals in permafrost and glaciers in the Arctic. For example, microorganisms currently conserved in the frozen remains of mammoth fauna can be brought to the surface by permafrost thaw (Kershengolts et al., Reference Kershengolts, Chernyavsky, Repin, Nikiforov and Sofronova2009). Pathogens related to modern anthrax bacteria, Bacillus anthracis, have been isolated from remains of mammoths. Metabolically active aerobic and anaerobic bacteria and fungi, microalgae, yeasts, moss spores, seeds of higher plants capable of germination, viable protozoan cysts and some viruses have been found in permafrost (Elpiner & Dzyuba, Reference Elpiner and Dzyuba2017). The danger of paleoviral and paleobacterial contamination of surface and underground water sources has been very poorly studied. Due to natural and artificial recharge of groundwater, ancient bacteria and paleoviruses have penetrated into the soil and can now, due to permafrost thaw, return to the surface, enter the atmosphere and spread over considerable distances. It is widely thought that even small climatic shifts are sufficient to change the life cycles and parasitic systems of Arctic animal pathogens and vectors that have existed for a long time under severe environmental constraints (Hoberg Reference Hoberg2005; Strathdee & Bale Reference Strathdee and Bale1998). The most obvious parameters that can influence the spread of natural focal infections are temperature, precipitation and fluctuations in the length of the seasons. Changes in these parameters can affect the suitability of the habitats of hosts and vectors, and their reproduction rates, distribution and abundance. These relationships are often complex, and socio-economic factors, such as human behaviour and land use, are often added to the climatic and environmental drivers involved (Hedlund, Blomstedt & Schumann, Reference Hedlund, Blomstedt and Schumann2014).

Indigenous peoples

A range of indigenous populations lives in the Arctic region. These include Saami in circumpolar areas of Finland, Sweden, Norway and Northwest Russia; Nenets, Khanty, Evenk and Chukchi in Russia; Aleut, Yupik and Inuit (Iñupiat) in Alaska; Inuit (Inuvialuit, Nunavut, Nunavik and Nunatsiavut) in Canada; and Inuit (Kalaallit) in Greenland. All of the above-mentioned countries except Iceland have indigenous peoples living within their Arctic territory.

Although parts of the Arctic, such as northern Scandinavia, have living standards comparable to the rest of the Scandinavian countries, Indigenous populations in many parts of the Arctic experience health disparities (Yansouni, Pernica & Goldfarb, Reference Yansouni, Pernica and Goldfarb2016). Infectious diseases remain the leading source of infant mortality in parts of the Arctic as Greenland and Denmark as investigated using data until 1997 (Friborg J, Koch A, Stenz F, Wohlfahrt J & Melbye M., Reference Friborg, Koch, Stenz, Wohlfahrt and Melbye2004). Arctic communities are often disadvantaged by socio-economic disparities such as inadequate drinking water, sanitation and housing, decreased access to medical and educational facilities, and high levels of unemployment and food insecurity. In the Nenets Autonomous Okrug of the Russian Arctic, these contribute to the spread of various parasitic diseases (Bobyreva & Degteva, Reference Bobyreva and Degteva2015; Bobyreva, Korneeva & Degteva, Reference Bobyreva, Korneeva and Degteva2016).

The most vulnerable groups are those living in remote areas where adaptation to climate change is most difficult, for example due to insufficient economic support or lack of infrastructure (Hedlund et al., Reference Hedlund, Blomstedt and Schumann2014). At the same time, research on these conditions is insufficient, especially in the most remote regions. In addition, comparison of data on incidence of certain diseases in different countries is often hindered by differences in reporting systems and regulatory documents due to the lack of international standardisation of data (Omazic, Berggren, Thierfelder, Koch & Evengård, Reference Omazic, Berggren, Thierfelder, Koch and Evengård2019; Orlov et al., Reference Orlov, Menshakova, Thierfelder, Zaika, Böhme, Evengard and Pshenichnaya2020).

In addition to infectious diseases transmitted among humans in most societies, a range of zoonotic infections, many of which are climate-sensitive, occur among in particular indigenous populations of the Arctic (Jenkins et al., Reference Jenkins, Castrodale, de Rosemond, Dixon, Elmore, Gesy, Hoberg and Thompson2013). The Saami people are unique among the indigenous peoples in the world in having the same public health standard as citizens in the Nordic countries (Sjolander P, Reference Sjolander2011; Anderson et al., Reference Anderson, Robson, Connolly, Al-Yaman, Bjertness, King, Tynan and Yap2016). However, due to climate change, an increased susceptibility to infectious disease among semi-domesticated reindeer – an important Saami food source – is an increasing threat to food security and safety (Haider, Laaksonen, Kjær, Oksanen & Bødker, Reference Haider, Laaksonen, Kjær, Oksanen and Bødker2018). In the indigenous populations of the North American Arctic and Northern Russia, transmission of some zoonoses is associated with the unique food practices and habits of the indigenous population. For example, the consumption of raw meat from marine mammals, especially walruses and bears, is associated with recurring outbreaks of trichinellosis in Greenland and Northern Canada (Hotez, Reference Hotez2010), and the preparation of fermented walrus or seal meat (“igunaq”) is associated with outbreaks of botulism in Alaska, Northern Canada and Greenland (Austin & Leclair, Reference Austin and Leclair2011). These unique fermentation practices can bring in unique microorganisms that give the food a special flavour (Aviaja et al., Reference Aviaja, Hauptmann, Hestbjerg Hansen, Sicheritz-Ponten, Mulvad and Nielsen2020). Human seroprevalence for toxoplasmosis is 2–4 times higher in Inuit in regions of the Canadian Arctic than in the rest of North America, with risk factors including consumption of harvested wildlife as well as contaminated drinking water (Jenkins et al., Reference Jenkins, Castrodale, de Rosemond, Dixon, Elmore, Gesy, Hoberg and Thompson2013; Messier et al., Reference Messier, Lévesque, Proulx, Rochette, Libman, Ward, Serhir and Dixon2009). On the other hand, consumption of traditional foods of wildlife origin is critical for food security, cultural continuity and intact relationships with the land for Inuit. The benefits of these close relationships among people, animals and the land are generally considered to outweigh the risks; however, rapid climate change may alter pathogen distribution, prevalence, transmission routes and diversity in Arctic wildlife, and environments faster than Arctic residents, however innovative, can adapt. Knowledge is urgently needed in many parts of the Arctic on the prevalences and impact of zoonoses in humans, given the sparse populations and the lack of microbiological laboratories and other diagnostic facilities. In addition to building local capacity for food safety, veterinary and human diagnostics, more work is needed integrating scientific and indigenous knowledge to monitor, detect and mitigate old and new threats in the Arctic.

According to a recent analysis of policy documents from the Arctic Council, traditional ecological knowledge has only been incorporated to a limited extent in policy documents so far, and co-production of knowledge is recommended to improve integration of traditional ecological knowledge into research activities in the North (Sidorova, Reference Sidorova2020).

Co-production of knowledge

There is no uniform definition of traditional knowledge, often described as knowledge conveyed narratively or through practical learning from one generation to the next for a long period of time. A key element is also that it is embedded in a cultural framework. Traditional knowledge on how to survive and thrive in the Arctic has been passed on since time immemorial among Indigenous and Local people in the North. Climate change requires major reconsideration of this knowledge. Already, reindeer herders in Sweden and Finland have described changed herding conditions and animal behaviours and health, especially in winter (Furberg, Evengård & Nilsson, Reference Furberg, Evengård and Nilsson2011; Rasmus et al., Reference Rasmus, Turunen, Luomaranta, Kivinen, Jylhä and Räihä2020).

Indigenous theory emphasises unequal power relations in society; however, indigenous cultures do naturally not theorise in the same way, and each culture needs to be respected in a dialogue. Co-produced knowledge is a central concept. Reciprocity is emphasised as an important ethical aspect: knowledge is expected to be produced in dialogue with the respective Indigenous society. Research designed for co-produced knowledge, where academic and traditional knowledge meet with respect, is important in a world with an increasing climate crisis (Rasmus et al., Reference Rasmus, Turunen, Luomaranta, Kivinen, Jylhä and Räihä2020).

In the Arctic Council, the Indigenous peoples of the Arctic are represented as permanent participants by six Non-Governmental Organisations (NGOs): the Aleut International Association (the islands in the Bering Sea between the US and Russia), the Arctic Athabaskan Council (Canada and USA), the Gwich’in Council International (Canada and USA), the Inuit Circumpolar Conference (Greenland, Canada, USA and Russia), the Saami Council (Norway, Sweden, Finland and Russia) and the Russian Association of Indigenous Peoples of the North, Raipon (representing 40 different Indigenous peoples in Russia). The structure of these NGOs differs regarding true representation of these Indigenous Arctic communities, which is important to consider in research aiming at dialogue with a specific Indigenous community.

For example, the Inuit Circumpolar Council (ICC) is designed to represent the entire Inuit population either by parliamentarian representation (ICC Greenland and ICC Canada) or by Indigenous organisations working on a direct mission from the local communities (ICC Alaska and ICC Chukotka). Other Indigenous NGOs of the Arctic Council are less representative of an Indigenous people‘s perspective – for example the Saami. The Council is dominated by reindeer herding interests. Consequently, it only represents approximately 10 - 40 % of the Saami community. Saami parliamentarians representing the entire registered Saami population in Fennoscandia have neither influence nor representation in the Saami Council. Thus, researchers aiming at a community dialogue based on the full registered Saami population should address the Saami Parliamentarian Council instead, a governmental collaboration body consisting of representatives of the Saami Parliaments of Norway, Sweden and Finland, including Russian non-parliamentarian Saami observers. However, collaboration with the Saami Council would be better for research focusing on the sub-community of reindeer herding Saami, especially since lack of trust means some are not registered as Saami according to the parliamentarian system.

These examples demonstrate the need to be well informed about the different structures and organisations of Indigenous peoples and governmental bodies in the Arctic before approaching and building a relationship with an Indigenous community, aimed at co-production of knowledge. Depending on the research question, organisations recognised by the Arctic Council are not guaranteed to be representative from a research – or international law – perspective.

There are only a few examples of Saami community-based co-production of knowledge, as needed for research on environmental and climate change. One example from Russia is about how to improve dialogue among researchers, locals and Indigenous peoples and decision-makers (Callaghan et al., Reference Callaghan, Kulikova, Rakhmanova, Topp-Jørgensen, Labba, Kuhmanen, Kirpotin and Johansson2020), and another one is about mountain birch utilisation in a research project in northern Finland. Saami perceptions and practices were published both in scientific papers and books of the EU-funded project HIBECO (e.g. Aikio & Muller-Wille, Reference Aikio and Muller-Wille2003, Reference Aikio, Muller-Wille and Wielgolaski2005), and joik, music and videos. In recent research, Eriksen, Rautio, Johnson, Koepke & Rink, (Reference Eriksen, Rautio, Johnson, Koepke and Rink2021) support the need to develop formalised ethical protocols and use of community-based participatory approach in Saami research to the co-production of knowledge and mutually beneficial research for all involved. The importance of compulsory feedback to the communities from the academic world cannot be too overstressed and should be a part of protocols.

Predicting disease outbreaks

Predicting potential increases in infectious diseases under ongoing climate change is a key challenge for science and society. Changes in climate and water conditions that influence the spread of disease have been observed or are projected globally (Barnett, Adam & Lettenmaier, Reference Barnett, Adam and Lettenmaier2005; Milly, Dunne & Vecchia, Reference Milly, Dunne and Vecchia2005) and reported to change the geographic range, prevalence and/or severity of some infectious diseases (Garrett et al., Reference Garrett, Dobson, Kroschel, Natarajan, Orlandini, Tonnang and Valdivia2013; Baker-Austin et al., Reference Baker-Austin, Trinanes, Taylor, Hartnell, Siitonen and Martinez-Urtaza2013; Harvell, Altizer, Cattadori, Harrington & Weil, Reference Harvell, Altizer, Cattadori, Harrington and Weil2009; Burge et al., Reference Burge, Mark Eakin, Friedman, Froelich, Hershberger, Hofmann, Petes and Willis2014; Rodó et al., Reference Rodó, Pascual, Doblas-Reyes, Gershunov, Stone, Giorgi, Hudson and Stenseth2013). Understanding and predicting potential future changes in the spread of infectious diseases in the Arctic requires validated quantitative mechanistic or statistical disease model(s), (e.g. Balci et al., Reference Balci, Borlu, Kilic, Demiraslan, Oksuzkaya and Doganay2014; Desvars-Larrive et al., Reference Desvars-Larrive, Liu, Hjertqvist, Sjöstedt, Johansson and Rydén2017; Nakazawa et al., Reference Nakazawa, Williams, Peterson, Mead, Staples and Gage2007; Palo, Ahlm & Tärnvik, Reference Palo, Ahlm and Tärnvik2005; Rydén, Sjöstedt & Johansson, Reference Rydén, Sjöstedt and Johansson2009; Rydén et al., Reference Rydén, Björk, Schäfer, Lundström, Petersén, Lindblom, Forsman and Johansson2012), which can be linked with relevant landscape and hydro-climatic modelling, data and future projections (Ma, Vigouroux, Kalantari, Goldenberg & Destouni, Reference Ma, Vigouroux, Kalantari, Goldenberg and Destouni2020; Leibovici et al., Reference Leibovici, Bylund, Björkman, Thierfelder, Evengård and Quegan2021). Such disease models can be combined with climate model projections of future temperature, precipitation, thawing of permafrost changing land cover, soil moisture, snow cover, atmospheric pollution and other disease-relevant factors in order to assess potential impacts of landscape and hydro-climatic change on future disease spreading. As far as possible, multiple alternative models should be tested for both diseases (often not available) and environmental conditions including various landscape and hydro-climatic data (often available and should be used), in order to quantify and understand multi-model uncertainty and robustness of inferred implications of future disease evolution (Bring et al., Reference Bring, Goldenberg, Kalantari, Prieto, Ma, Jarsjö and Destouni2019; Leibovici et al., Reference Leibovici, Quegan, Comyn-Platt, Hayman, Val Martin, Guimberteau, Druel and Ciais2020).

A case study: Tularemia

Tularemia is one of the most studied zoonotic diseases in high-latitude regions, with human and wildlife outbreaks observed in, for example, Alaska (Hansen, Vogler, Keim, Wagner & Hueffer, Reference Hansen, Vogler, Keim, Wagner and Hueffer2011), Canada (Isaac-Renton, Morshed, Mak, Loyola & Hoang, Reference Isaac-Renton, Morshed, Mak, Loyola and Hoang2010), the Nordic countries (Rossow et al., Reference Rossow, Ollgren, Hytönen, Rissanen, Huitu, Henttonen, Kuusi and Vapalahti2015; Larssen, Bergh, Heier, Vold & Afset, Reference Larssen, Bergh, Heier, Vold and Afset2014; Desvars et al., Reference Desvars, Furberg, Hjertqvist, Vidman, Sjöstedt, Rydén and Johansson2015) and Russia (Timofeev et al., Reference Timofeev, Bakhteeva, Titareva, Kopylov, Christiany, Mokrievich, Dyatlov and Vergnaud2017). For the Nordic-Arctic region, it is one of the diseases identified as likely to be affected by hydro-climatic change (Waits, Emelyanova, Oksanen, Abass & Rautio, Reference Waits, Emelyanova, Oksanen, Abass and Rautio2018). Tularemia is caused by the bacterium Francisella tularensis, which is typically spread to humans by deer flies, mosquitoes, ticks or through contacts with infected animals, for example hares, rodents or beavers. In Sweden, the geographical distribution of the disease is uneven, and the majority of the human cases occurs in seven high-risk regions (Desvars-Larrive et al., Reference Desvars-Larrive, Liu, Hjertqvist, Sjöstedt, Johansson and Rydén2017). The number of human cases varies greatly between years, with local outbreaks with annual incidences of more than 500 cases/100 000 inhabitants and years with no cases in some of the high-risk regions. Seasonal variation is also large, with most cases occurring during the late summer and early autumn. The annual distribution of human tularemia cases within the high-risk regions has been modelled (Rydén et al., Reference Rydén, Björk, Schäfer, Lundström, Petersén, Lindblom, Forsman and Johansson2012) by considering five environmental variables: relative mosquito abundance, summer temperature in the preceding year, summer precipitation, number of cold days with low snow coverage (such weather conditions decreases the rodent and hare populations) and the number of tularemia cases in the preceding year. The relative mosquito abundance estimated from two hydro-climatic parameters, daily river flow and temperature, was then found to have the most correlation with the outcome. The highest mosquito abundance was observed when river flooding was followed by warm weather. Interestingly, all the environmental variables can be predicted from meteorological data, but the disease modelling suggests that the relation between weather data and tularemia incidences is highly complex and needs to be modelled with high temporal resolution, using daily or hourly meteorological data. Without a model that correlates tularemia cases and climate data, it will be very difficult to understand the consequences of climate change. The model proposed for tularemia has also been further adapted to various high-risk regions and was able to predict most of the outbreaks, although it failed to represent the magnitude of the outbreaks in two regions (Desvars-Larrive et al., Reference Desvars-Larrive, Liu, Hjertqvist, Sjöstedt, Johansson and Rydén2017). This highlights the importance of geographically local modelling and awareness that extrapolations to other regions may not always be possible. Although we have models that explain the annual variation of tularemia cases within high-risk regions, we do not fully understand why it is endemic in these regions, nor which regions are at risk of becoming endemic in the future. This knowledge gap needs to be addressed when modelling how climate change will affect tularemia.

Projection of disease evolution under climate change

In general, a combination of climate and water conditions (hydro-climate) can directly or indirectly influence important disease mechanisms by affecting the abundance of disease vectors, such as mosquitoes and ticks (Rogers & Randolph, Reference Rogers and Randolph2006), and pathogen survival outside the host (Lowen, Mubareka, Steel & Palese, Reference Lowen, Mubareka, Steel and Palese2007). Hydro-climatic conditions can also influence host-pathogen interactions, related to community ecology and biodiversity (Altizer, Ostfeld, Johnson, Kutz & Harvell, Reference Altizer, Ostfeld, Johnson, Kutz and Harvell2013; Callaghan et al., Reference Callaghan, Björn, Chernov, Chapin, Christensen, Huntley, Ims and Jonasson2004), and environmental contamination and exposure to water-borne infections (Reiner et al., Reference Reiner, King, Emch, Yunus, Faruque and Pascual2012). Other possible hydro-climatic factors include damping of host immunity (Foxman, Storer, Vanaja, Levchenko & Iwasaki, Reference Foxman, Storer, Vanaja, Levchenko and Iwasaki2016), disruptions of health status due to malnutrition linked to droughts or floods and disruption of health care systems by disasters such as floods (Kouadio, Aljunic, Kamigaki, Hammad & Oshitani, Reference Kouadio, Aljunid, Kamigaki, Hammad and Oshitani2012).

For the example of tularemia, Ma, Bring, Kalantari & Destouni (Reference Ma, Bring, Kalantari and Destouni2019) linked a statistical disease model with the historically observed ranges of relevant hydro-climatic variables, in order to quantify the sensitivity of future disease evolution to measured variations in the variables. This revealed that relatively small variations and changes in the variables could greatly shift the level of tularemia outbreaks. Ma et al. (Reference Ma, Vigouroux, Kalantari, Goldenberg and Destouni2020) further tested multiple disease model versions relevant to different high-risk areas across Sweden. This showed that the impacts of climate change on tularemia can differ greatly among geographic regions and that predictions depend on the specific disease models and hydro-climatic models used. Overall, Ma et al. (Reference Ma, Vigouroux, Kalantari, Goldenberg and Destouni2020) quantified high uncertainty levels in projections of future disease scenarios, which poses significant challenges to related policy, management and diplomacy for the Arctic (Azcárate, Balfors, Bring & Destouni, Reference Azcárate, Balfors, Bring and Destouni2013).

A few other studies have also attempted to quantify the impacts of projected hydro-climatic change on tularemia outbreaks (Nakazawa et al., Reference Nakazawa, Williams, Peterson, Mead, Staples and Gage2007; Palo et al., Reference Palo, Ahlm and Tärnvik2005; Rydén et al., Reference Rydén, Sjöstedt and Johansson2009). Consistent with the findings of Ma et al. (Reference Ma, Vigouroux, Kalantari, Goldenberg and Destouni2020), their results vary due to different models assumptions and perspectives adopted in the different studies. For example, Rydén et al. (Reference Rydén, Sjöstedt and Johansson2009) concluded that a future increase of approximately 2°C in monthly summer temperature would increase the duration of tularemia outbreaks in Sweden. In contrast, Palo et al. (Reference Palo, Ahlm and Tärnvik2005) concluded that a future warmer climate will not lead to higher frequency of tularemia outbreaks in Sweden. Such contradictions often emerge in projections of diseases with highly localised transmission (Desvars-Larrive et al., Reference Desvars-Larrive, Liu, Hjertqvist, Sjöstedt, Johansson and Rydén2017). A focus on smaller geographical scales may mean higher accuracy for local disease models, but generally implies higher uncertainty and lower accuracy for climate models. Going to larger spatial scales instead implies likely lower applicability of local disease models, but considerably more robust and accurate projections by climate models. The choice of geographic problem and model scale thus involves tradeoffs, which need to be acknowledged and accounted for in projections of coupled future disease and climate change scenarios: Arctic science diplomacy will be most effective when it considers, and uses, such model projections based on best climatic and disease data. The information required for projection of disease evolution under climate change includes systematically procured long-term data on environmental indicators, increased understanding of the ecology of the disease-causing agents, appropriate indicators to monitor (including traditional knowledge) and information on the occurrence of infections in humans and animals. Different countries host various databases containing a wealth of such information, but often in incompatible forms, making it difficult or impossible to extract consistent data for urgent cross-border comparisons. Rapid development of new harmonised technologies and databases is needed to access relevant data from rich, but currently highly heterogeneous data sources. Furthermore, the largest uncertainties in modelling climate change scenarios are not scientific, but sociological, with the future of contaminants and infectious diseases in the Arctic depending on what paths the majority of the globe chooses to follow. Diplomacy is a powerful tool Arctic nations have to influence the choices of other nations and should have at its core recognition of the interconnection between people, animals, plants and their shared environment at the local, regional, national and global levels.


International harmonised databases and forecasts like those for tularemia should be pursued and made openly and routinely available to support decisions aimed at keeping humans and animals healthy and societies sustainable in the Arctic. This requires diplomatic efforts to establish a solidly based network for international collaboration, including Indigenous theory as mentioned above and community-based participatory research. With a strong enough mandate, such an organisation/network would be able to rapidly share results, strengthen the input of resources from different nations and reinforce swift exchange of information across nations for the benefit of a globally sustainable environment that benefits human and animal health.


This research was funded by the NordForsk Centre of Excellence CLINF (grant number 76413).


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