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Climate change and agriculture: an institutional perspective
In Pakistan, public institutions are considered among the key stakeholders in irrigated agriculture due to their importance in providing a range of services, i.e., surface irrigation, on-farm water management, pest and disease management, advisory, credit, and marketing services12. Hence it is pertinent to understand how these institutions perceive climate variability and its impacts in the study area.
Regarding observation on changes in climate, the majority of the office bearers reported substantial changes in temperature, rainfall, and cropping season expansion over the past 2 decades (Table 3). Notably, a significant increase in temperature and a decrease in rainfall is observed. Specifically, many respondents were of the view that summer seasons have become warmer. In contrast, monsoon rains, which account for two-thirds of the annual precipitation, has significantly decreased (shifting to late summer months). These observations are in line with the historical temperature and rainfall trends in the study area1,3. Further, respondents also indicated a variation in the duration of both Rabi (winter) and Kharif (summer) cropping seasons. An official from DoAE described that during the past few years, winter wheat cultivation is merged nearly a month to the summer season due to which the next crop faces delays in sowing and subsequent yield losses.
Table 3 Perceived climate changes and impacts at the farm level.
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In terms of climate-induced impact, the findings show that most of the effects reported are biophysical (droughts, floods, and water resources) and biological (insect, diseases, and weeds) in nature. Officials from PID and OFWM reported increasing water scarcity due to the reduced surface water flows and critical depletion of groundwater reserves that lead to the overall reduction in cultivated area under rice crop. Further, increased incidents of extreme temperature during early crop growth stages and intensive rainfall during harvesting seasons have severely affected rice yield. Heavy rain in late monsoon season leads to flooding in plain areas of Punjab and poses a severe threat to the sustainability of agriculture in the province.
Further, officials indicated that high temperatures and heatwaves have resulted in an increase in crop water requirements due to high evapotranspiration. Similarly, changing patterns of rainfall and extreme temperature events have increased the presence of fungal diseases, insect and weed attacks. Similar findings have been reported by a recent study showing a significant increase in the incidence and severity of climate-induced biological and biophysical risk in Pakistan5. Moreover, an official from DoAE reported a 100–150 kg/ha in general and 150–200 kg/ ha (in worst case scenario) reduction in wheat and rice yields due to increases in weed germination. Several respondents revealed that due to excessive use of insecticides and pesticides, aiming to control pests and diseases, the penetration of various harmful chemicals has alarmingly increased in both soil and water and resulted in degradation of water and soil quality.
In general, various respondents also highlighted the increase in unrest among farmers due to decreasing profit margins on account of the increasing cost of production and productivity decline due to climate change. Many farmers have been switched to non-farm businesses, and this lacking interest may further risk the national goal of sustainable food self-sufficiency and security.
Institutional capacities regarding CCA/ CRM in agriculture
This study further analyzed the capabilities of agricultural institutions using seven indicators-based index approach. Results of the selected indicators are given in Table 4, which shows a medium level of preparedness and capacities of the selected institutions. Specifically, the results of each indicator are explained in the following.
Table 4 Institutional Capacities Index (ICI).
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Perception and knowledge
Literature shows that stakeholders’ perception and knowledge of climate change and its impact are among the key factors that define the level of intentions to make efforts regarding CCA/CRM19. These attributes allow an actor to formulate practices based on their knowledge and beliefs, which leads towards adequate risk management support19,37. Hence, officials’ perception and understanding of climate change impacts and risk management strategies were selected as the first indicator of institutional capacities assessment. Results (Table 4) show that overall, this indicator’s index maintained a good score, which is highest amongst all indicators. Specifically, most of the respondents had a significant perception of climate change and its induced impacts at the farm level. However, their knowledge and beliefs on adaptation strategies and their effectiveness are limited. Most of the respondents with negative beliefs about climate change adaptation were mainly from research and credit institutions. As reported by Farani37, a vigilant understanding of climate change is imperative to implement risk management mechanisms. Hence these findings imply to mainstream the climate change agenda across all agricultural institutions as they are part of the same institutional chain. This may lead to an equal understanding of climate-smart practices and hence improve institutions’ tendency to design and implement risk management mechanisms at the local level. A study reports similar findings on public health institutions, which also indicated the positive behavior of supervisors as an essential determinant of effective risk management services38.
Training and expertise
Institution’s technical resources, such as professional training and expertise, are also considered as crucial elements while dealing with climate hazards19. Such training helps office bearers to be well prepared and respond to catastrophes39. Current findings show that public institutions attained a medium level of training and expertise, as only 39% of the respondents possessed some knowledge regarding CCA/CRM. Similarly, two-third of the officials did not have any prior experience in climate risk management. Similarly, results show that only 12% of the officials received appropriate training related to CCA and CRM. However, one of the officials reported that since the last few years, some understanding of climate change had been developed at their department, and more officials are being invited for climate change-related training. Low training and expertise of agricultural office bearers in dealing with climatic risks may be translated into little support from public institutions to farming communities and hence may further increase the vulnerability of agriculture. Roosli39 was also of the view that skilled human resource is a pivotal attribute of institutions’ risk management capacity, as they have exceptional ability to provide technical aid to the disaster-prone communities by integrating and effectively using available resources. Fideldman19 has also raised the importance of staff’s skills in terms of integrating and implementing knowledge and mobilizing available resources against the environmental uncertainties. Further, professional knowledge and expertise not only improve the emergency response against climatic catastrophes but also improve the farmers’ and peers’ skills39.
Human resources
According to the Gupta’s Adaptive Capacity Wheel (ACW) framework, human resource has critical significance in determining the institutions’ abilities while dealing with climate risks16. Following ACW, human resources were also chosen as an indicator to assess institutional capacities. According to the findings, the HR index of the institutions reported a deficient value of 0.44. Sub-indicators further revealed that only 31% of institutions had sufficient human resources, and particularly only 26% of the institutions had adequate human resources to meet the operational requirement dealing with risk management emergencies. Officials from DoAE, OFWM, and PID indicated a severe shortage of skilled human resources to meet climate change challenges in the field operations. An official from PID described that, in case of any extreme climate event such as canal breakage, windstorm, or extreme hailing, sometimes quick response and technical support was not provided or possible due to limited skilled human resources.
These findings revealed that lack of human resources in public institutions might lead to limited risk management support and hence may further increase the vulnerability of farming communities to climate change. These results are supported by a study conducted in Congo, where forest institutions lacked in human resources in terms of climate change response15. Gupta was also of this view that institutions with adequate human resources have a greater ability to mobilize climate change adaption and risk management processes in agriculture. These findings conclude that sufficient human resources in public institutions are the prerequisite of active risk management support.
Plan and priorities
Institutions’ priorities, planning, and emergency response mechanism are widely reported as important factors in dealing with the environmental uncertainties10,17,38. According to our findings, public institutions attained a satisfactory score regarding this indicator (0.66). Specifically, one-third of the office barriers indicated climate change as an important agenda for their department. Similarly, in terms of programs and initiatives regarding climate change, 42% of the institutions reported that they are carrying related initiatives and programs. While one-third of the respondents were of the view that they are planning to add CCA/CRM in their priorities. Further, 35% of the institutions, mainly the field institutions such as PID, DoAE, DoAF, and CRS, indicated having an active emergency response mechanism dealing with climatic catastrophes.
Wenger40 reported that effective risk management response is closely associated with emergency planning within the institutions. Huq10, has also stressed the significance of defined objectives and plan among the key factors of successful implementation of adaptation and risk management response to flood disasters. Hence our study implies further strengthening the planning infrastructure by removing existing gaps, which will increase the institution’s abilities in dealing with environmental catastrophes.
Coordination and collaboration
A wide range of literature shows that coordination between different stakeholders is among the critical determinants of the institution’s adaptive and risk management capacities15,16,28 and often support collective action and decision making regarding climate change adaptation15,41. The CCI value of 0.45 showed that institutions had a minimal level of coordination with other stakeholders. For instance, in terms of community interaction, one-third of the institutions reported direct coordination with the farmers, indicating a reduced level of cooperation between the farmers and institutions. The officials who indicated coordination with farmers were mainly from the field institutions (PID, OFWM, DoAE, and SWTL). However, the research institutions had also acknowledged the significance of institution-community coordination. An official from a research institution (FTAR) stated that it is very pertinent for all institutions to have interactive communication with the farmers. However, most of the research institutions have a deficient level of community coordination, due to which most of the contingency plans and alerts (which usually go through the filed institutions) do not reach to the farmers timely. There is a need to develop such a communication system that could connect agricultural institutions and the farmers on a single communication platform.
In terms of inter-departmental collaboration, 27% of the respondents indicate that their respective institutions have a coordination mechanism with other public sector institutions. In comparison, merely 6% of them stated coordination with the private sector’s institutions. However, a decent level of coordination (63%) was indicated within the same institution. A minimal level of coordination, particularly between public and private institutions, is worrisome, as non-governmental bodies of Pakistan, which are already at the emerging stage, could face further marginalization12. Literature also advocates smooth coordination between the public and private organizations for effective adaptation and risk management support in agriculture16. Brown15 stated that a well-coordinated network between the actors of the same institution chain is critical for an active response to a challenge like climate change. Hence these findings conclude that a well-coordinated institutional setup may be more capable in coping with agricultural hazards.
Financial resources
Financial resources are also widely quoted among the significant determinants of institutional adaptive capacity16,28. Financial resources of the institution facilitate the actors’ preparedness and emergency response-ability towards natural disasters42. However, in the current study, the financial resources of the agricultural institutions were severely deficient (FRI 0.36). Findings revealed that only 15% of the institutions indicated funds availability for the CCA/CRM related operations. A significant majority of the officials (85%) reported the insufficiency of the financial resources available for climate change. Overall, a gap of nearly 40% was reported in terms of funds availability and requirement.
The respondents who indicated the availability of funds, particularly for CCA/CRM, were mainly from the research intuitions such as FTAR, SWTR, PWQP. Even though field institutions such as DoAE, DoAF, OFWM, PID have significant importance to carry community-level activities did not indicate enough financial support specified for CCA/CRM related operation. For instance, an official from DoAE reported a severe shortage of funds for launching emergency awareness campaigns and training seminars during the period of extreme weather events such as droughts, floods, heavy rains, and insect attacks. Due to financial constraints, such activities have been restricted to a few official visits or small gatherings in a few villages.
Apart from the field institutions, some credit providing institutions have also raised similar concerns. An official from ZTBL mentioned that in some situations when a cropping season faces unexpected yield losses due to rainfall or insect and disease attack. Farmers, particularly the smallholders, desperately need a loan to cultivate the next crop, and due to the unavailability of credit for such emergencies, the institution is unable to offer credit to these farmers.
Our findings are in line with the studies conducted in Cambodia28 and Cameron15, where institutions reported similar challenges while implementing climate response strategies. As argued by Gupta16, institutions’ financial resources are among the foremost determinants of effective adaptive and risk management in agriculture. These findings imply that the institutions, which are farmers’ first line of defense in an emergency, need to be strengthened in such a significant resource.
Physical resources
Access to adequate physical resources is considered as another critical component to define their role in supporting farmers to manage climate risks at the community level15,43. In terms of physical resources, availability of vehicles, machinery (harvesters, bulldozers, cranes), communication equipment, and hardware are considered for the capacity assessment of field and market institutions. In contrast, instruments, apparatuses, and laboratory equipment are considered for research institutions.
According to the results, the critical index value of the physical resources (0.39) indicates insufficient availability of infrastructure and physical resources in public institutions. Results of sub-indicators further revealed a vast gap (51%) between the availability and actual requirement of these resources. Only 21% of institutions indicated enough availability of machinery and hardware for extreme climatic conditions and emergencies. These figures are alarming as physical resources are pivotal elements while providing community support against catastrophes. Field intuitions, particularly the DoAE, DoAF, and PID, have indicated the critical shortage of these resources.
The officials from DoAE and PID have specifically indicated the lack of vehicles as the critical constraint limiting their efficiency while conducting the field operations. An official from DoAE revealed that most of the available vehicles are either very old or non-functional, which means filed staff has to wait hours and days to complete assigned field operations. Similar challenges were reported in terms of communication infrastructure as the officials from the DoAE highlighted a huge communication gap between farmers and their department due to the unavailability of contemporary communication tools. Previous studies43 have also reported similar findings of lacking logistic and communication resources and urged the provision of these resources for capacitated community support regarding natural disasters. In a nutshell, the physical resources of agricultural institutions are deficient in terms of meeting catastrophic challenges and seek serious consideration from concerned authorities.
Institutional capacities across different types of institutions
To have a comprehensive understanding of institutional capacities across different types of Institutions, ICI was compared by categorizing the agricultural institutions into three categories, i.e., research, field, and market and credit institutions. Cumulative ICI values (Fig. 1) across these categories show that research institutions have attained higher index value, while credit and market, and field institutions are among the low capacitated institutions. The ICI values further show that perception and knowledge were high in case of field institutions, which could be due to their more field experience and interaction with farming communities. Such communication enables them to have a better understanding of climatic risks and farm level CCA/CRM practices. Moreover, financial resources showed the lowest value across all types of institutions. In terms of plans and priorities regarding CCA/CRM, research institutions maintained a higher index value.
Figure 1

Institutional capacities index (ICI) across different categories of institutions.

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In contrast, field, and credit and market institutions lacked in this indicator, highlighting the need for planning and prioritizing climate change agenda among these institutions. In terms of physical resources, which are regarded among the most critical resources, revealed alarming indications as both research and field institutions had a deficient amount of machinery and hardware resources. These findings imply that focus should be given to these institutions as they play a more crucial role (in terms of community support) when compared to credit and market institutions. Field institutions were also found lacking in terms of human resources, which could constraint the efficiency of these institutions in managing farm-level activities.
Gaps and solutions
After exploring institutions’ capacities in the selected indicators, officials were asked to indicate existing gaps and related solutions, which are essential to increase the capacities in the context of climate governance and CCA/CRM in agriculture. The following gaps and solutions were identified and prioritized.
Need for an effective administrative mechanism
An effective administration and coordination mechanism has been listed as a top priority by most of the office-bearers to enhance the institutional capacity in managing climate risks. Officials also highlighted the importance of ensuring effective administrative mechanisms to implement and monitor the individual and collective performances in ongoing projects. That will improve the output of resources being invested at various levels. Fidelman and Madan19 have also indicated a sound administrative system among the critical components of the institution’s capacity dealing with CCA/CRM. Bettini raised the importance of constructing such a rule system that identifies accountability and defines boundaries and hierarchy in water management institutions18. Hence it is needed to develop or customize such institutional arrangements that are interactive, effectively administered, and target oriented.
Need for physical and financial resources
The second suggested measure is the provision of physical and financial resources required to support farm-level adaptation. Officials indicated that the current state of these resources is not enough to meet the institutional operational requirements to conduct CCA/CRM related operations. Brown has also identified similar gaps among the Congo’s forest institutions dealing with climate risk management15; however, Grecksch14 reported a higher level of physical and financial resources among the German institutions. Officials suggested that an appropriate amount of financial support should be specified for extreme climate events, along with emphasizing the need for communication and logistic resource. Literature also ranks these resources among the pertinent element of effective risk management44. The institutions equipped with such crucial resources would be more likely to overcome the climatic challenges. For instance, at the farm level, well-equipped institutions may have a better ability to reach farmers’ knowledge as well as technical requirements, to reduce the actual and potential losses. Similarly, the research institutions having contemporary technology apparatuses and instruments may create better innovation, i.e., climate-resilient farm inputs (seeds, water-efficient measures) that will ultimately reduce the farmers’ vulnerability of climate risks.
Need for professional training
Thirdly, a considerable portion of the respondent indicated the training need of staff regarding CCA and CRM. Institutions reported that human resources generally in the non-administrative and research positions, while particularly in field operations, are in much need of training. As indicated by Roosli that stakeholders may enhance the skilled humane resource by launching a series of training and disaster management programs that may lead to effective risk management response39. This study stresses that departmental training courses could be launched where indigenous and research knowledge could be integrated. Field staff should particularly be trained regarding emergency response in extreme climate events such as excessive rains, floods, wind storms. At the same time, the researcher’s skills should be enhanced in terms of the development of climate-smart practices and modeling farm-level risks and vulnerability.
Need for enhanced support
The last indicated challenge by the public institutions was the lack of support from the higher authorities. Institutions urged the need for a shared understanding and realization of agricultural vulnerability to climate change at both policy and higher administrative levels, which may put the energy into the local level. Similar capacity recommendations were identified by Brown15, where institutions reported a need for a common understanding between the stakeholders of forest communities for effective climate response. More

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The principle of dose reconstruction supposes to gather a significant amount of data in multiple areas, from radiological measurements to ecological information for each species. Since our primary dataset was not acquired with this objective in mind, we faced a lack of information for some descriptors. We filled these missing values using reasonable assumptions founded on scientific justification as described below.
Study sites and mammals tracks
We re-analyzed the dataset described by Møller and Mousseau1. They used foot prints following fresh snow fall to estimate abundances of mammals, as counted by a single observer on a single ca. 2 day period of 3–4 February 2009. A total of 161 line transects were surveyed, each with a length of 100 m. Transects were separated by at least 50 m (but usually 100–500 m), and were located along roadsides (Fig. 1). A rigorous examination of the consistency and homogeneity of the dataset led us to exclude 16 transects from our analysis. Sixteen of these transects (see SI) were investigated during a different period (January 21 or February 17 and 18, 2009), applying a different experimental logic. They did not belong to the same sampling plan, and their use would have reduced the statistical significance of our analysis. This reduction did not affect the diversity of mammal species observed. The revised dataset included the abundance of 12 species of mammals distributed over 145 transects (Fig. 7). Foxes were the most frequently observed species, followed by wolves. Large prey (deer, horse, moose, and wild boar) had count numbers from about ten to twenty individuals.
Figure 7

Count numbers per mammal species over the 145 studied transects.

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About 18% of the transects were devoid of mammal activity during the study period (Fig. 8). Predators tended to be more widely distributed than prey, being observed in 72% of transects while prey were not seen in about half the transects. Most transects ( > 80%) had fewer than 4 individuals. The paucity of observations prevented a more refined analysis of the taxonomic structure of these data.
Figure 8

Number of transects with a given count of individuals per category of animals.

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We used a number of additional descriptors for each site as covariates in our analysis, as described by Møller and Mousseau1. These included temporal descriptors (i.e. time of day the counting started, time since the last snow fall) and environmental factors (percentage cover with grass, bushes and trees to the nearest 5%). Ambient radiation levels were determined from averaged repeated measurements (2–3) at ground level with a hand-held Geiger counter (Model: Inspector, SE International, Inc., Summertown, TN, USA).
Dose reconstruction
The principles for reconstruction of total radiological doses absorbed by animals were described in detail in a previous similar study of dose reconstruction for birds in the Fukushima area5 (SI). According to this method, we estimated for the mammal species j and the radionuclide r the external and internal dose rates (respectively EDR(j,r) and IDR(j,r); µGy h−1, see supporting Excel® file) absorbed by mammals of a given species. We used radionuclide activity concentrations measured in soils and calculated for animals, multiplied by the ad-hoc Dose Coefficients (DCs, Table S8). These coefficients, specific for the combination of radionuclides, species and irradiation pathway, were determined per radionuclide and daughter element(s) for adult stage of each species with the EDEN v3.1 software27.
The internal and external irradiation dose rates absorbed by mammal species were calculated according to the equations described elsewhere5 (and recalled in SI). The total dose rate absorbed by the mammal species j, TDRj, is the sum of the internal and external dose rates assessed for each radionuclide, applied to all radionuclides of interest (SI Excel® file).
We assessed the total dose to a given mammal species j considering that adult individuals are exposed to ionizing radiation at a constant dose rate during a period that corresponds to the generation time LGj of the species (average age of parents of the current cohort reflecting the turnover rate of breeding individuals in a population; SI). Consequently, the total dose absorbed by each species j at a given transect i noted tdj,i resulted from

$$ td_{j,i} = TDR_{j,i} {text{x}}L_{Gj} $$
(1)

This total dose was calculated for each species and each transect, whether the presence of the species on this given transect has been confirmed or not. This is the theoretical total dose the animal would receive if living there.
The final objective of the re-analysis was to study how abundance of different groups of mammals (all mammals, prey and predators) identified at the study sites vary according to their exposure to ionizing radiation. We needed a unique value of dose per transect, representative of the average exposure of all animals indirectly observed along this transect. The tdj,i values presented large ranges of variation (intra-transect ratio from ca. 30—transect n°10—to 6,000—transects n°83, 84 and 92). Their geometric mean for all species on a given transect i was calculated as the most relevant indicator, named Transect Total Dose and abbreviated TTDi. Calculating the mean on all species, whatever they have been counted on the transect or not, gives a highly representative estimate of the level of exposure on the considered transect, not of the exposure of the counted species. This was also justified because we considered the zero count as relevant information. Such a number may have two origins: either the species has never occupied the surroundings of the transect (e.g. area not suitable for its needs) or it has disappeared. In both cases, the series of possible confounding variables considered in our statistical analyses will allow us to include this information (see “Methods”, Statistics).We used the geometric mean value in order to limit the influence of extreme values on the results28.
Mammal species and associated assumptions
We deliberately chose to limit our analysis to adults in order to minimize the assumptions required to achieve our calculations. It is generally recognized that juveniles may be more sensitive to exposure to pollutants than adults. Juvenile development and growth mobilize resources that are no longer available for their protection. Juveniles differ from adults in their diet, behavior and physiological characteristics. Moreover, these characteristics change with time from birth to maturity. Such changes can have large implications in terms of dose reconstruction and associated uncertainties. Thus it is necessary to identify periods of development during which individual characteristics can be considered constant, and to be able to collect data corresponding to the needs for dose reconstruction. This approach is possible for a single species, but would be much more speculative for all 12 of our species of interest. Since our understanding of adult life history is likely to be more robust than that of juveniles for the purposes of dose reconstruction, we have ignored juvenile stages for this analysis. Moreover, time from birth to maturity is generally short with regard to generation time (Table S1), and discounting the corresponding contribution to the total dose would underestimate its actual value in a way that makes our results an acceptable proxy for the quantification of the response of mammals to their exposure to ionizing radiation.
For each of the 12 species under consideration, DC calculation required us to simplify the representation of adults as ellipsoids of known mass and size (geometric characteristics, Tables S1 and S2), and to define media elementary composition (Table S3). In the same way, a basic animal life style was described considering the time spent (i) in a burrow if relevant for the species and (ii) standing or lying on soil for all species (Table S4). Finally, as much attention as possible was paid to the species’ diet (omnivorous, carnivorous or herbivorous) to select the most appropriate value for the concentration ratios (CR) required to quantify the radionuclide aggregated transfer from soil to the animal (Table S5). When available, site-specific CRs were preferentially used, to reduce the large uncertainty associated with the choice of a CR value. This uncertainty is a well-known weakness of the assessment of activity concentrations in animals applying the equilibrium approach29,30. By default for site-specific data, the choice was made to refer to best-estimates published in an international compilation of data31. All data depending on the nature of the radionuclide were collected or calculated for the elements Cs and Sr and their isotopes present in the accidental releases for the Chernobyl NPP accident (Table S6). Since dose (rate) is additive in terms of the resulting effects of exposure to ionizing radiation, it is essential to exhaustively characterize the source of radioactivity under examination in terms of quality and quantity.
Soil contamination data
We conducted two preliminary studies to streamline and optimize data collection, and to limit the assumptions required to fill potential data gaps. First, we explored the depth of contaminated soil for consideration in the calculation of DCs. A potential maximal depth of 10 cm has been reported for the radioisotopes characteristic of the accident fallout (Table S6), which is in agreement with observed and predicted contamination profiles for 137Cs (Fig. S1). In the end we used a 20 cm layer, in a conservative but realistic way, as increasing reasonably the soil depth increases the amount of radioactivity to which mammals could be exposed. Despite the much larger original spectrum of radionuclides, it is largely assumed that today both 137Cs and 90Sr should be the main markers of the impact on the environment of the NPP accident, due to the emitted quantities and their radioactive half-lives. Radionuclides contribute very differently to the total dose absorbed by animals depending on the energies and nature of their emissions32. We thus secondly investigated the role of the 10 radioisotopes for which we found activity concentrations in soil considering their realistic extreme values in a given location (Table S7). We assessed the corresponding total dose rates on one hand for the pair 137Cs + 90Sr and their daughters, and on the other hand for the remaining radionuclides, for two contrasting mammal species, a small carnivore and a large herbivore (Fig. S2). Whatever the scenario, the dose rate due to 137Cs and 90Sr represents at least 94% of the total exposure. We assumed that other radionuclides can be ignored without significantly skewing our results, taking into account all the associated uncertainties. This considerably limits the data search and collection (focused on selected isotopes, e.g. DC values, Table S8) as well as the assumptions necessary to achieve the dose reconstruction. It results in a reduction of calculations needed but also of conservatism of the approach, while keeping it at a level sufficient for our needs.
Measurements of soil radionuclide activity concentrations have been extensively conducted in the CEZ and around since the accident. To best cover the spatial and temporal scales of our study, we combined different data sources6,33,34,35 (plus the REDAC database, V. Kashparov, personal communication). 137Cs and 90Sr soil activities were assessed for each transect. We took into account both the transect length (100 m) and the species home range (Table S1) to define a potential exposure area for each species present on a given transect (dosimetry area, Table S2). This circular area is centered on the transect origin, located by its GPS coordinates, with a radius of 100 m (transect length) plus the radius of the species’ home range (Fig. 9). Using GIS, we crossed referenced this information with the geo-located contamination data from all the references identified. When several measurements were available for the same dosimetry area, we retained their extreme and mean values (i.e. in general three different values per dosimetry area). When only one single measurement was available, we used these data for both extreme and mean values. When no data were available in a given area, soil activity was assumed equal to the one measured at the nearest soil sampling point. The radioactive decay occurring during the period of dose reconstruction (i.e. the generation time) was ignored with regard to the ratio between the generation times (highest value for the red deer LG: 5,210 days, ca. 14 years) and the radionuclide periods (about 30 years for both 137Cs and 90Sr). This assumption contributes to the conservatism of the approach. The final dataset included three values of soil activity concentration per radionuclide (137Cs and 90Sr) for each species on each transect (i.e. more than 10,000 values). We arbitrarily decided not to use more complex data treatment such as krieging. Due to the highly heterogenous “leopard skin” pattern of the soil contamination, we considered such approaches not particularly robust as they give an apparent continuity to soil contamination between measured values. Using only actually measured values helped to limit the number of assumptions required by our calculation, already high. We acknowledge however that a spatialized statistical approach to better assess the soil contamination is an interesting perspective to refine the dose reconstruction.
Figure 9

Definition by species of its potential exposure area from which the dose (rate) is calculated.

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Uncertainties
The first source of uncertainty in this study was its field protocol, which did not allow screening of the possibility of a double count of the same animal. This is a well-known weakness of such census methods. This “old fashioned” approach of field counting was largely applied in the past, as it is something relatively easy and simple to implement, requiring relatively few resources in contrast to more technological methods. The related and inherent disadvantage is the uncertainty around the count that is difficult to quantify. The way the census was conducted was though to reduce this uncertainty, by exploring somewhat distant transects in a short period of time. This is not a guarantee that individuals have not been counted more than once, but the application of recommended best practices when using such methods.
Other sources of uncertainty appear in such a dose reconstruction, which is a highly uncertain exercise requiring numerous assumptions. The use of CRs was previously acknowledged as a major source of uncertainty that we managed by constraining the value by the diet and using preferentially site-specific values or by default best-estimates such as CR values provided by the IAEA31. Using these values under-predicted the transfer of 137Cs to predatory versus to prey species31. Wood et al.36 reported from previous studies that the transfer of cesium to carnivorous species such as those classified in our study as predatory was suggested to be higher than for mammals at lower trophic levels. At the opposite, values summarized by the IAEA showed a significantly higher transfer of cesium to omnivorous and herbivorous mammals, as data in this database relate31 mainly to insectivorous small mammals rather than to species similar to our predatory mammals. In the absence of site-specific CR values, the use of best-estimates remained the best option.
We applied this logic to any other ecological or biological parameter required by the dose calculation (home range, animal size, etc.). Our choice was to make an assessment as specific and realistic as possible, without propagating uncertainty characterized at a global scale. For all parameters, there were insufficient local data to characterize their local variation. Soil activities showed rapid spatial change. This is a well-known characteristic of the contamination in the CEZ and it was the only data that we were able to characterize locally in terms of range of variation. We decided to consider only this site-specific uncertainty in our calculations.
A last source of uncertainty in approaches such as the one applied here is the existence of confounding factors. A number of additional variables are known as potentially affecting mammal abundance, such as environmental characteristics or human activities. The first problem consists in identifying these variables, and then to characterize them. What are the necessary and sufficient parameters to collect, when and how? Regarding the description of the environment, the minimal dataset usually acknowledged as relevant has been collected during the census (soil cover, time of observation, time elapsed since the last snowfall). If time data provide objective information, soil cover is observer dependent. This uncertainty was reduced due to observations done by a single observer. This ensured a high comparability between transects. The interaction between animal abundance and human activities is somewhat more complex to characterize. The nature of the CEZ led us to consider only the potential for repulsion of industrial activity linked with the NPP or attraction of farming areas. Characterizing the latest areas was highly uncertain (see dedicated paragraph in SI). There may be a significant time shift between the time of required data were acquired and the time of census. There may be also problems of spatial definition due to labels used in the available sources of information.
Statistics
The re-analysis of the dataset gave the opportunity to investigate the role of complementary data related to the impact of human activities. Potential spatial interactions between industrial and farming activities, present in the CEZ, and exposure areas of all or parts of the mammals were considered before to be dismissed as non-significant in the conditions of this study (see SI). The set of confounding variables finally retained was the same as for the initial study, that is to say the environmental descriptors that were recorded during the census (time of observation, time since snowfall and soil cover type expressed as % of tree, bush and grass).
All statistical analyses were performed in R37. We first tested the variation in mammal abundance with the TTDi increase through the development of a Generalized Linear Mixed Model (GLMM), assuming Poisson error distribution. The main predictor (TTDi) was log-transformed and then centered on the mean and scaled by the standard deviation. The multicollinearity between possible confounding variables was checked through the Pearson correlation coefficient (omitted if Pearson correlation coefficient  > 0.85, and using38 a Variance Inflation Factor  More