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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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Formal attribution, as defined by the IPCC, requires three steps to be fulfilled: extreme event attribution, impact detection, and impact attribution (Fig. 1). At each step, three essential components are required22. First, the relationship between cause and effect must be demonstrated. Then the detected change must be shown to be inconsistent with changes due to alternative possible drivers. And finally, a quantification of the strength of the attribution statement is required to acknowledge the uncertainty and limitations of the available data and analyses. The challenges associated with these steps are clearly illustrated in the floodplain grassland community of the River Elbe.
Recent attribution studies have evaluated the extent to which human-induced climate change has affected extreme heatwaves, drought and floods in the Elbe River region. Stott et al.38 estimated the likelihood of a heatwave of the magnitude of the 2003 European one was at least doubled under human induced climate change (confidence level  > 90%). Similarly, anthropogenic forcing was found to have played a substantial role in the hot, dry summer of 2013, both in terms of the high temperatures observed and the northward shift of the North Atlantic summer storm track which led to reduced rainfall over western Europe39. In contrast, a large simulation ensemble and observation-based analysis concluded that climate change had not made the extreme rainfall of 2013 in the upper Danube and Elbe basins more likely40. The attribution of rainfall events is substantially more difficult than temperature events because event attribution relies on the model’s ability to simulate the climate conditions generating the weather event. This remains challenging for rainfall, which is naturally highly variable, and generated by processes that are not captured well at the scale of current-generation climate models41,42. Flood time series are similarly highly variable in response to natural variability and factors such as urbanization, deforestation and dike construction. These factors occur simultaneously across a catchment and often interact at multiple temporal and spatial scales, limiting attribution of extreme floods to climate change42,43.
Understanding natural modes of variability plays a crucial role in attribution studies, particularly for rainfall and associated flooding events. For example, the apparent increase in intense rainfall that we identified in the current period, particularly in summer, could have been caused, in part or fully, by natural variability. The 20-year time periods used here are sufficient to capture interannual drivers of variability such as the El Nino–Southern Oscillation and decadal modes such as the North Atlantic Oscillation and the Pacific Decadal Oscillation. However, a significantly longer time period would be needed to capture multidecadal patterns such as the AMO, which was predominantly negative during the historical period and positive during the recent period44. The lack of long-term observations means there are few studies of the influence of the AMO on the study region, although increased summer rainfall has been associated with a positive AMO in modelling over North Western Europe45.
It was possible to detect a community response to the extreme events. The composition and abundance of all taxa displayed considerable inter-annual variability, but changes in community turnover, species abundance and dominance were detected in years following the extreme events as previously shown by31. Decreased carabid species richness was found in years following drought and heatwave events, while plant species richness increased or remained stable in years after heatwaves and/or drought. Extreme floods reduced the species richness of plants and beetles in the short term. In contrast, and consistent with other studies, molluscs showed higher species richness following flooding and lower richness after heatwave and drought years46,47,48. Many of the changes detected were consistent with expectations based on understanding of life cycle biology, such as timing of reproduction and traits enabling inundation and drought tolerance (e.g. molluscs with diaphragms or lids that close shells), although not all changes had simple explanations (described in more detail in Supplementary Information).
While “detection” does not depend on an explanation of the causes of the observed change20, it does require a demonstration that the likelihood of the response is significantly different from that due to natural variability. This is challenging with biological data, for several reasons. First, extreme events occur infrequently and are difficult to predict, so it is rare to have baseline data to characterise the community prior to an event. Second, biological data are generally highly variable in space and time. As illustrated in the floodplain community, species abundance, composition and dominance commonly vary over time, with responses differing both within and between sites. In some cases, the same species responded differently under different conditions.
Further, a community response to an extreme event may be sudden or gradual, periodic or episodic, and the effects may be short-term or permanent. Without continuous observations, it is impossible to determine whether changes in community indices occurred gradually over the years from 1999 to 2003, or suddenly in response to the 2002 extreme flooding event. It is difficult to quantify the extent to which the community is altered permanently by one extreme event. In the dynamic floodplain system there may be some capacity to return to a previous state, since many organisms are adapted to cope with regular flooding events. However, species interactions and feedbacks could lead to lagged responses due to changes in resource availability or competitive interactions49.
Additionally, ecosystem responses may not always occur immediately after a single weather event, but in response to the long-term stress of the changing climate, in combination with extreme weather events9 (the ‘press and pulse’ framework17). Mean temperature has increased in the River Elbe region over recent decades, so any community response may be influenced by this change in the background ‘press’, as well as the magnitude, duration, frequency and timing of the extreme ‘pulse’ events.
The severity of flood impact will also be affected by changes in timing in relation to the traits and phenological stages of species within the community50. Annual spring floods resulting from snowmelt represent natural variability to which the community is adapted. This is supported by the fact that all taxa declined in species richness following the year in which the spring flood did not occur. However, the summer floods of 2002 and 2013 were extreme in terms of severity, duration, and timing. Floods occurring in summer were associated with reduced species richness in carabids and plants. Carabids exhibit adaptations to flood such as autumn emigration, hibernation as adults or physiological adaptations such as low physiological activity or higher submergence resistance in low temperatures. These adaptations enable survival through the usual winter and spring floods, but do not confer resilience to summer floods, which occur when many species are in sensitive larval or pupal phases31.
Differences in traits across taxa mean that different responses will be shown by different taxonomic groups51. The ability to detect a response will therefore depend on what “community” is of interest. Here we found, for example, the pattern of species re-ordering over time was in the opposite direction in the carabids and molluscs after the 2002 flood and 2003 heatwave. Both aquatic and terrestrial molluscs are well adapted to floods, as even land snails can survive in water provided the water is oxygenated and not too warm. In contrast, carabid beetles range in their ability to survive inundation and dispersal ability is important for recolonising after floods52.
Multiple events also complicate detection53. Here, for example, the low mean monthly precipitation recorded in 2003 fell within the lower 25th percentile, so does not represent a climatological extreme in isolation. However, at the same time, extreme maximum temperatures were recorded for extended periods (Table 1) and water levels were significantly lower than the long-term mean (Supplementary Figure S1), with the maximum water level below the 5th percentile of the historical period (Fig. 3d). A strong biological response in all taxonomic groups was associated with the year 2003, in which extended heatwaves coincided with low water levels. However, plant data from 2010 suggest that a similar response as that found in 2003 (increased species richness and decreased turnover) is also associated with recurrent flooding in combination with heatwaves. The mechanisms driving the response are obviously quite different, associated with the added nutrients provided by the fine sediments carried by floods54.
In the current case, not only were the impacts of droughts and heatwaves superimposed on the impacts of floods (natural and extreme), but these events are likely to act in opposing directions. In the short term, floods act to homogenise the habitat and provide nutrients, while drought and heatwaves are more likely to increase heterogeneity across microhabitats with differing elevations and exposure to water50. Over longer timescales, however, increased homogeneity could be expected as the habitat dries out in the absence of regular flooding events. The impact of drought and heatwaves on floodplain communities is likely to be greater than that of floods, given the high proportion of aquatic and inundation-dependent and tolerant species.
Attribution in the climate system relies on the ability to quantitatively model the system55. The mismatch in temporal and spatial resolution between available biological data and climate observations and models42 limits the ability to apply statistical analyses and develop models, and reduces the chances that responses at fine spatial resolution will be successfully attributed to climate change. This is compounded by the high natural variability in the climate variables of interest, in addition to the variability in biological communities, as discussed above. Each extreme event is essentially not replicable16, and even where multi-event responses are available, each event has specific characteristics. Attempts to link biological responses to climate change are therefore likely only to be possible at continental to global scales, or over the timescale of decades56.
Interactions and feedbacks are important structuring factors in natural systems. Extreme events can alter species interactions by reducing populations of common species, allowing another species to increase in population sufficiently to prevent the dominant species re-establishing. In many cases, quantification of such interactions remains problematic, and cause and effect cannot be inferred from correlations between observations and events.
Separation of drivers is a key element of formal detection and attribution analysis21. Biodiversity responses, however, are likely to be driven by multiple factors, acting on a range of timescales. Hydrologic conditions, land use and management, for example, are important drivers of vegetation and invertebrate floodplain communities (e.g.57,58,59,60). In some cases, such as the impact of mowing, riverbed erosion or water extraction, the non-climate driver is easily identified. However, many land use changes develop over decades to centuries, and so would more likely have a long-term effect on biodiversity. Multiple-driver attribution would require the role of such non-climatic drivers to be accounted for and shown to be inconsistent with the observed community response.
The case of the floodplain community suggests that the formal joint attribution of community responses to extreme events caused by climate change will rarely be possible. The detection of responses to extreme events, however, is feasible, and important to improve understanding of the connections between climate, extreme weather events and biodiversity. Such knowledge is essential to inform conservation management attempts to mitigate the impacts of extreme events or ongoing climate change.
Monitoring is essential, ideally before and particularly after an extreme event, to improve our knowledge of the connections among climate, weather and biodiversity. Baseline data needs to be long-term and spatially extensive, due to the highly variable nature of biological data61. More intensive temporal monitoring is essential to better understand patterns and drivers of natural variability so that extreme responses may be identified. Improved spatial replication would increase the likelihood of having biological data from areas that did not experience the extreme event and could also enable other important, non-climatic, drivers to be identified16. Better spatial monitoring is also necessary to predict a response to extreme events in ecosystems other than those with long-term observations.
Long-term monitoring should be designed within a sound hypothesis testing framework16 to encourage a more thorough consideration of the important (and possibly interacting) drivers and their potential effects on biological communities and the mechanisms driving change. Although some taxa are more difficult and expensive to sample, it is important that the best taxon to identify a community response is monitored. While plants are the cheapest and easiest taxon to monitor, they might not be the most appropriate group, as differences in biology across taxonomic groups are likely to lead to a range of responses56.
To strengthen our understanding of impacts and responses, biological monitoring should be complemented with evidence from observations, remote sensing, experimental data, models and ecological theory. Experiments to identify mechanisms driving a response can support observational studies and establish causal relationships11,16,62. For example, Rothenbucher and Schaefer63 used exclosure plots on the Lower Oder floodplain after the 2002 flood to identify species responses in relation to inundation tolerance and immigration. Such experimental information, combined with observations over time, could contribute to greater understanding of how extreme events affect the distribution of species and the structure of communities.
Mesocosm experiments are particularly appropriate for testing the impacts of extreme events on aquatic and riparian invertebrate communities. Experimental manipulations of temperature and moisture can be used to test hypotheses generated by observations of community responses and determine cause and effect64. An additional advantage of mesocosm experiments is the ability to incorporate the effect of carbon dioxide on species responses, an important component that is frequently ignored in observational studies of global change. While short-term experiments can provide important biological knowledge, longer-term mesocosm experiments are essential to identify ongoing impacts of extreme events on the structure and function of communities, including potential lag effects, feedbacks and interactions65.
Meta-analyses to combine results from studies of different single extreme events are needed to consolidate observations and identify trends, similarities, divergences and exceptions. Such analyses will be more informative if studies report similar aspects in a comparable way. For instance, the magnitude of the extreme event should be defined and robust estimates of the magnitude of ecological responses and other drivers reported1,16. Global and regional trends are more likely to be identified through such syntheses.
Reanalysis products based on climate observations could help link weather patterns associated with an extreme event and an observed biological response. Such products are now available at resolutions fine enough to capture processes at biologically relevant scales. For example, the NWP model COSMO regional reanalysis data sets66,67 provide hourly atmospheric data for Europe with a resolution of 6 km for the years 1995–present. The application of reanalysis products, seasonal forecasts and high-resolution projections will strengthen the link between biological and climate knowledge.
Central Germany, in common with many regions of the world, has experienced several extreme weather events over recent decades, in addition to gradual background warming. There is increasing interest in attributing biological responses to extreme events and climate change, but there are many challenges that limit our ability to achieve formal, quantified joint attribution. Nevertheless, it is important that we detect responses and improve our understanding of the mechanisms behind change to inform conservation management and restoration. This is particularly important as the incidence of extreme events is projected to increase in the future. More

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Study site and experimental setup
Our experiment was conducted at the La Hampa field station of the Spanish National Research Council (CSIC) in Seville, Spain (37°16′58.8″ N, 6°03′58.4″ W), 72 m above sea level. The climate is Mediterranean, with mild, wet winters and hot, dry summers. Soils are loamy with pH = 7.74, C/N = 8.70 and organic matter = 1.16% (0–10-cm depth). Precipitation totaled 532 mm during the experiment (September 2014–August 2015), similar to the 50-y average. We used ten common annual plants, which naturally co-occur at the study site, for the experiment. These species cover a wide phylogenetic and functional range and include members of six of the most abundant families in the Mediterranean grasslands of southern Spain (Table 1). Seeds were provided by a local supplier (Semillas silvestres S.L.) from populations located near to our study site. Our experiments were located within an 800 m2 area, which had been previously cleared of all vegetation and which was fenced to prevent mammal herbivory. Landscape fabric was placed between plots to prevent growth of weeds.
Theoretical background for quantifying niche and fitness differences
Here we summarize the approach developed in ref. 38 to quantify the stabilizing effect of niche differences and average fitness differences between any pair of species. Both these measures are derived from mathematical models that capture the dynamics of competing annual plant populations with a seed bank19,39. This approach has been used in the past to accurately predict competitive outcomes between annual plant species38. Population growth is described as:

$$frac{{N_{i,t ,+, 1}}}{{N_{i,t}}},=,left( {1,-,g_i} right)s_i,+,frac{{lambda _ig_i}}{{1,+,alpha _{ii}g_iN_{i,t},+,{mathrm{{Sigma}}}_{j = 1}^{mathrm{S}}alpha _{ij}g_jN_{j,t}}},$$
(1)

Where ({textstyle{{N_{i,t + 1}} over {N_{i,t}}}}) is the per capita population growth rate, and Ni,t is the number of individuals (seeds) of species i before germination in the fall of year t. Changes in per capita growth rates depend on the sum of two terms. The first describes the proportion of seeds that do not germinate (1 − gi) but survive in the seed soil bank (si). The second term describes how much the per germinant fecundity, in the absence of competition (λi), is reduced by the germinated density of conspecific (giNi,t) and various heterospecific (left( {{mathrm{{Sigma}}}_{j = 1}^{mathrm{S}}g_jN_{j,t}} right)) neighbors. These neighbor densities are modified by the interaction coefficients describing the per capita effect of species j on species i (αij) and species i on itself (αii).
Following earlier studies14,38, we define niche differences (1 − ρ) for this model of population dynamics between competing species as:

$$1,-,rho,=,1,-,sqrt {frac{{alpha _{ij}}}{{alpha _{jj}}}frac{{alpha _{ji}}}{{alpha _{ii}}}} .$$
(2)

The stabilizing niche differences reflect the degree to which intraspecific competition exceeds interspecific competition. 1 − ρ is 1 when individuals only compete with conspecifics (i.e., there is no interspecific competition) and it is 0 when individuals compete equally with conspecifics and heterospecifics (i.e., intra and interspecific competition are equal). Niche differences between plant species can arise for instance from differences in light harvesting strategies29,37,38,39, or in soil resource use and shared mutualisms40.
The average fitness differences between a pair of competitors is ({textstyle{{kappa _j} over {kappa _i}}})38, and its expression is the following:

$$frac{{kappa _j}}{{kappa _i}},=,frac{{eta _j,-,1}}{{eta _i,-,1}}sqrt {frac{{alpha _{ij}}}{{alpha _{ji}}}frac{{alpha _{ii}}}{{alpha _{jj}}}} .$$
(3)

The species with the higher value of ({textstyle{{kappa _j} over {kappa _i}}}) (either species i or species j) is the competitive dominant, and in the absence of niche differences excludes the inferior competitor. This expression shows that ({textstyle{{kappa _j} over {kappa _i}}}) combines two fitness components, the “demographic ratio” (left( {{textstyle{{eta _j – 1} over {eta _i – 1}}}} right)) and the “competitive response ratio” (left( {sqrt {{textstyle{{alpha _{ij}} over {alpha _{ji}}}}{textstyle{{alpha _{ii}} over {alpha _{jj}}}}} } right)). The demographic ratio is a density independent term and describes the degree to which species j has higher annual seed production, per seed lost from the seed bank due to death or germination, than species i

$$eta _j,=,frac{{lambda _jg_j}}{{1,-,left( {1,-,g_j} right)s_j}}.$$

The competitive response ratio is a density-dependent term, which describes the degree to which species i is more sensitive to both intra and interspecific competition than species j. Note that the same interaction coefficients defining niche differences are also involved in describing the competitive response ratio, although their arrangement is different. Because of this interdependence, a change in interaction coefficients (( {alpha _{ji}^prime s} )) simultaneously changes both stabilizing niche differences and average fitness differences21.
With niche differences stabilizing coexistence and average fitness differences promoting competitive exclusion, the condition for coexistence (mutual invasibility) is expressed as14,38:

$$rho, More

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