Climate Resilience and Sustainability

ORIGINAL ARTICLE
Wallace’s pARCs—Making Climate, Climate Change and Biodiversity Data Available to Protected Area Managers and Conservation Planners With an Example From Biebrza National Park, Poland
Jeff Price1 Nicole Forstenhäusler1 Erin M. Graham2 Timothy J. Osborn3 Rachel Warren1
1Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK 2eResearch Centre, James Cook University, Townsville, Australia 3 Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK
Correspondence: Jeff Price (jeff.price@uea.ac.uk)
Received: 29 October 2025 Revised: 11 December 2025 Accepted: 22 December 2025
ABSTRACT
We document the development, and potential uses, of an open-access dataset—Wallace’s pARCs. This dataset currently contains ∼98,000 reports, one each for most of the terrestrial protected areas (PAs) in the World > 1km2 in size. Each report contains a standard set of information designed to assist managers to increase the resilience of PAs to climate change. We present an example of how to apply the data for adaptation planning for Biebrza National Park, Poland.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
© 2026 The Author(s). Climate Resilience and Sustainability published by Royal Meteorological Society and John Wiley & Sons Ltd.

1		Introduction
Climate change threatens the world’s major centres of biodiversity, jeopardising many of the collective conservation and development efforts and investments to date. Observed changes, and the amount of climate change that society and ecosystems are already committed to, indicate that climate change is already damaging ecosystems and livelihoods, and that the amount of damage has been increasing over time (Parmesan et al. 2022; Root et al. 2003).
Protected areas (PAs) have long played a role in the maintenance and conservation of biodiversity. They appear as Target 3 in the Convention on Biological Diversity’s 2030 Kunming-Montreal Global Biodiversity Framework with a recommendation of setting aside 30% of each country’s land for biodiversity (https:// www.cbd.int/gbf/targets). However, many current PAs were not specifically set aside for biodiversity, and many conservation strategies have largely been developed under the assumption that the world’s climate is ‘stable’. A changing climate, in tandem with other existing and future human pressures, means there is a high risk that these strategies will ultimately fail in their goals unless the level of climate change can be constrained.
Outside of a few areas, access to PA-specific information on observed climate changes, projected climate changes and how biodiversity is projected to change has not been readily available. While climate change data, of varying accessibility to PA managers, has been around for years, the same cannot be said for data on the projected impacts of climate change on species groups within PAs.
2		Wallace’s pARCs
This paper documents the development, and potential uses, of an open-access dataset—Wallace’s pARCs (protected Area Resilience to Climate change). This dataset contains reports, one each for most of the terrestrial PAs in the World > 1km2 in size (∼98,000 reports). Each report contains a standard set of information designed to assist PA managers in working to increase the resilience of the PA to climate change. To this end, the reports hopefully help managers to understand the level of exposure of the PA to climate change. These reports also contain information on current land cover, current and projected human population changes and projected impacts of climate change on biodiversity to aid in assessing overall vulnerability and resilience.
Reducing biodiversity’s vulnerability to climate changes requires an understanding of the projected magnitude of the risks. A key element of climate change impacts on biodiversity, and one of the few that can be quantified in a harmonised and consistent fashion across many tens of thousands of species using available datasets, is the projected changes in species climate suitability (usually displayed as changes in distributions). Previously, we ran species distribution models to determine the climatic range relationships of ∼135,000 species of terrestrial fungi, plants, vertebrates and invertebrates (see Wallace Initiative (https:// wallaceinitiative.org/; Warren, Price, VanDerWal, et al. 2018). In areas where future climate change is projected to exceed the modelled climatic tolerance of many species, the species currently present may not be able to persist into the future. On the other hand, there are places where the climatic tolerance of most species (> 75%) is not projected to be exceeded by projected climate change, and we classify these as climate refugia. These may be the best places to protect and to conserve biodiversity (i.e., noregrets action) in the future under climate change (i.e., they become ‘arks’).
Refugia not only conserve species but also help preserve the functioning of the ecosystems and hence ensure the continuing provision of key ecosystem services (Parmesan et al. 2022). Examples of these functions of refugia or ‘arks’ include acting as seed banks, potentially conserving plant species during an overshooting of the Paris Agreement targets (Schleussner et al. 2024, Price and Forstenhäusler 2025), providing natural food resources to humans, nurseries for wild species and habitats for pollinators. These refugia will also perform and preserve important services that have large-scale benefits to the surrounding regions, and even in some cases globally, such as nutrient cycling, carbon storage, air purification, water collection and purification, flood mitigation and soil conservation (Price, Warren, Forstenhäusler, Jenkins, et al. 2024). As such, these areas may be seen as natural capital ‘banks’ for surrounding areas.
There are two types of reports in the database. The main ones are for the individual PAs, but there are also separate reports for each country. Each Wallace’s pARCs report (∼50 pp in length) contains the same level of information, except the country reports do not contain an overall ‘ranking’ of the exposure or resilience of the PA relative to all other PAs. An overview of a report and how to interpret it for the Biebrza National Park (Biebrzański Park Narodowy) in Poland is provided here to provide insight into the types of information provided in each report (Price, Forstenhäusler, et al. 2024) and ways the information may be useful for adaptation planning. All of the reports can be accessed at https:// wallaceparcs.uea.ac.uk/. The overall repository of reports is being updated periodically to bring in additional PAs such as those classified as other effective area-based conservation methods (OECMs).
3		Methods
Each of the reports uses a standard approach to the analysis of observed and projected climate change, observed land cover, observed and projected human population size and projected biodiversity datasets to provide a consistent assessment between PAs. The approach is summarised here. Additional information is available in the report for Biebrza National Park, included as Supporting Information.
Firstly, observed changes in climate variables are analysed, to provide information to managers about how the climate has changed in their PA over two 30-year time periods. Secondly, projected climate data are presented, so that managers can understand how the projected changes in climate variables compare to the observations including information about the size of the projected changes relative to the observed variability. Thirdly, observed changes in land cover over time (based on remote sensing data) are provided to inform managers as to how much change may have already occurred owing to human pressures or, potentially, from climate change. Fourthly and fifthly, observed and projected changes in human population size both within and within a 15-km buffer around each PA are provided.
Finally, the projected changes in climate suitability for the PA’s biodiversity are analysed. This information is extracted from the Wallace Initiative database which underpins our earlier study of the global scale impacts of climate change on the geographical distribution of species (Warren, Price, VanDerWal, et al. 2018). This database has also underpinned a number of scientific papers, as well as figures and tables in several chapters of the Intergovernmental Panel on Climate Change (IPCC) Working Group 2 Sixth Assessment Report (Bednar-Friedl et al. 2022; Costello et al. 2022; Jenkins et al. 2021; Manes et al. 2021; Mycoo et al. 2022; Parmesan et al. 2022; Price, Warren, and Forstenhäusler, et al. 2024; Price et al. 2022; Saunders et al. 2023; Smith et al. 2018; Trisos et al. 2022; Warren et al. 2013; Warren, Price, Graham, et al. 2018; Price, Warren, Forstenhäusler, Jenkins, et al. 2024; WWF 2018). The Wallace Initiative database was based on an empirical approach in which the existing statistical relationships between observed climate, expressed in terms of bioclimatic variables and extant species distributions, are assumed to hold into the future.
3.1		Global Warming Levels
The analyses use scenarios based on alternative global warming levels (GWLs), independently of the year in which those levels are reached. The guidance applies to the 30-year time period when the average global temperature reaches the given level in question, not the first year it is exceeded. This approach helps reduce the level of uncertainty compared to projecting climate changes by a specific future time period, shifting the uncertainty to the time at which the level of warming is projected to be reached. Species typically do not respond to ‘time’ but rather to the future climatic conditions associated with a level of warming. As science and policy (or lack of policy) evolve around the pace of warming, it is better to provide guidance to managers on how to adapt to different GWLs, alongside standard information about the possible timescales. Thus, information on timing may shift depending on shifts in the policy landscape or improvements in understanding how the climate responds to differing levels of emissions. By presenting, and updating (as needed), the potential time when a GWL may be reached, a practitioner has information on how much time there is to apply adaptation measures.
The timing of when a given level of warming is reached is dependent on current and projected emissions. The latest published work shows that, for a 20-year average of global surface air temperature, a level of 1.5◦C global warming above 1850–1900 levels is likely to be reached by the early 2030s (Lee et al. 2021). Projecting into the future based on current published mitigation strategies, 2◦C global warming would be reached on average by 2043, and a level of 3◦ C global warming by 2062. A GWL of 4◦ C warming arises in the most pessimistic scenarios during the period 2081–2100. There is uncertainty about the timing of future global warming because it depends on humanity’s combined efforts to reduce emissions in the future and uncertainties in the response of the earth’s climate system to greenhouse gas emissions (known as the Transient Climate Response). Current policies would result in ∼3.1◦C of global warming, while full implementation of both unconditional and conditional nationally determined contributions (NDCs) to emission reduction under the Paris Agreement leads to projections of up to 2.6◦C of global warming. To get on a least-cost pathway for 1.5◦C, global greenhouse gas emissions must fall 42% by 2030, compared to 2019 levels. For 2◦C, global greenhouse gas emissions must fall 28 per cent by 2030 (United Nations Environmental Programme 2024). However, currently emissions are still rising at just over 1% annually.
The tables of projected results in these reports give the projected monthly average changes in the different climate variables relative to 1961–1990, tied to the corresponding projected global annual mean surface temperature rise relative to pre-industrial levels (1861–1890; Osborn et al. 2018). The GWLs used in this report are 1.5◦ C (Paris Accord’s aspirational goal), 2◦C (upper limit of Paris Accord goals), 2.5◦C, 3◦C (spanning approximately the range of warming projected if countries meet their Intended Nationally Determined Contributions and make no additional improvements), 3.5◦C and finally 4◦C as an upper limit based on continuing high-levels of emissions and/or climate model sensitivity. In general, many impacts (not just biodiversity) are tied to an amount of global warming and its accompanying climate change and are not strongly dependent on time (as different scenarios reach the same temperature (e.g., 2◦C) at different times). This approach is used to aid the reader in determining potential projected changes depending upon agreed (and followed) global policies from international negotiations.
3.2		Climate (0.5◦ spatial resolution, ∼50 km)
Observed climate data come from (or were derived from for drought and waterlogging) the University of East Anglia Climatic Research Unit (CRU) TS 4.07 dataset (Harris et al. 2020). Versions of these data have previously been used extensively in IPCC reports and in many different works on climate change impacts. The data presented here cover two time periods: 1961–1990 and 1991–2020 (except drought and waterlogging [too little or too much precipitation relative to potential evapotranspiration], which uses 1986–2015 as the later baseline). Summary statistics give the difference between the climates in 1961–1990 and 1991– 2020 to provide information on what changes (if any) in temperature and precipitation have been observed between these two time periods. The first period, 1961–1990, is one of the standards used for climate modelling results and is a commonly used baseline for impact models (including the biodiversity results presented here); by this time, the world had warmed by 0.35◦ C since 1861– 1890 (see HadCRUT4 dataset of Morice et al. [2012]). By the second period (1990–2020), warming had increased to ∼0.9◦ C– 1.0◦ C since 1861–1890. Similarly, global land temperatures were 1.59◦C (1.34◦C–1.83◦C)warmerbetween 1850–1900 and 2011–2020 (IPCC 2023).
Projected climate data come from the Community Integrated Assessment System (CIAS; Warren et al. 2008) and its component module ClimGEN (Osborn et al. 2016). In this approach, a simple climate model is first used to project global temperature rises (using a probabilistic approach to encompass the key uncertainties in state-of-the-art global climate change projections) over the 21st century, as a time series. CRU has a database of stored outputs from 21 general circulation models (GCMs) from the CMIP5 model inter-comparison project. These outputs provide patterns of how climate variables are projected to change regionally for specific levels of global temperature rise. ClimGEN scales these patterns to the amount of warming provided by the time series, to create 21 new patterns of projected changes corresponding to the desired future time periods/warming levels for each area (Osborn et al. 2016). One advantage of using pattern scaling is the ability to look at the overall projected local climate change for a specific GWL, rather than approximating by using the closest match to that level of warming using different published scenarios (e.g., particular time periods within specific Representation Concentration Pathways [RCPs] and time periods). Thus, ClimGEN was run to determine the temperature and precipitation changes at warming levels of 1.5◦C, 2◦C, 3◦C and 4◦C. The projected climate change data are expressed as quantified changes, typically called anomalies (e.g., degrees of temperature rise, millimetres of precipitation or percentage change in precipitation), relative to the baseline climate of 1961–1990. In preparing these reports, these projected changes are generally averaged (median for precipitation) across the 21 patterns and then compared against the observed baseline, as well as to the standard deviation in the observed (1961–1990) baseline.
To match the base biodiversity model resolution, these anomalies were applied to a higher resolution observed climatology (WorldClim 1.4; Hijmans et al. 2005) to harmonise with the 10-arc-min (∼20 km) spatial resolution used in the underlying biodiversity models. Thus, most of the climate tables presented in the reports are at 10-arc-min resolution. The exception to this is that drought and waterlogging were derived from the 0.5◦ raw ClimGEN output and remain at 0.5◦ spatial resolution.
3.3		Drought (0.5◦ Spatial Resolution)
The drought metric used in the reports is the Standardized Precipitation Evapotranspiration Index (SPEI) that includes changes in both precipitation and temperature (potential evapotranspiration). Specifically, the metric used here is SPEI12 -1.5 (severe drought), which accumulates the effective rainfall (precipitation minus potential evaporative losses) over the preceding 12 months and expresses any excess (positive values) or deficit (negative values) relative to the variations typically observed in the region. This metric is often used when looking at potential drought issues for agricultural and natural lands. SPEI12 means that the drought has been developing over the preceding 12 months before the ‘counting’ begins. Thus, an area identified as having a drought duration of 12 months has really had a deficit in effective rainfall for up to 24 months. The values in the table are calculated for the 30-year period including the GWL stated. More data on the drought and waterlogging metrics and interpretation can be found in Price et al. (2022).
3.4		Waterlogging (0.5◦ Spatial Resolution)
Waterlogging is the reverse of the drought metric and uses an SPEI12 of +1.5. This is an indication of areas having excess moisture for extended periods of time, potentially leading to waterlogged soils.
Two different metrics are provided for both variables, for both observed and projected warming levels. The first is the number of months, and the second is the maximum number of consecutive months, both in a 30-year period. The first could have other months not meeting the threshold of -1.5 or +1.5. Consecutive months means there is no break in the run of month’s meeting the threshold (there could also be other, shorter, consecutive months with severe drought/waterlogging in the given 30-year period).
3.5		Population Data (1 km Spatial Resolution)
The population data present the projected population for the years 2000 through 2100 (Gao 2017). These data are provided both in terms of the total (summed across all 1 km cells in the area) population within the PA boundary and those within the area including a 15 km wide buffer zone around the boundary. The data from 2000 and 2010 are interpolations of observed population sizes; the other time periods are projections of future change. There are several caveats around these data: Firstly, in many cases, a PA may be managed in a way that precludes any population or population growth within the PA. These may not have been captured in the modelling underpinning these population data but would still be captured in the area +15 km buffer. Secondly, the population change projections are from what is known as the Shared Socioeconomic Pattern (SSP) 2 scenario. This is one of five scenarios used in the IPCC to look at alternative pathways for climate change. In the SSP2 scenario, the trends into the future basically follow those existing today, sometimes referred to as a ‘middle of the road scenario’ (Riahi et al. 2017). More information on the process used to derive the population projections and subsequent downscaling can be found in Jones and O’Neill (2016) and Gao (2017).
3.6		Landcover Changes (300 m Spatial Resolution)
The reports provide information on the percentage of different land cover types in 1992 and 2020 as well as the change between these two time periods within the boundaries of the area in each report. These data come from the European Space Agency Climate Change Initiative (Copernicus Climate Change Service, Climate Data Store 2019; ESA 2017) and have a resolution of 300 m. The main limitation of the ESA data is that it is classified without supervision and not ‘ground-truthed’. While these data are thought to have among the highest accuracy for global land cover data, they are still not 100%. Thus, some areas designated as ‘forest’ may be plantations of non-native species. These figures are provided to assist in understanding how land cover classes have changed over time as this may have immediate biodiversity implications in the area studied for this report. Furthermore, the satellite platforms are unable to detect areas where the land cover may be degraded but to an extent that it is not identified as being a different land cover type. Biodiversity may already have been depleted due to such land degradation.
3.7		Biodiversity Data
The biodiversity information presented here includes projections of species richness remaining under future alternative GWLs and the resultant climate refugia for biodiversity. This is mined from the Wallace Initiative database (https://wallaceinitiative.org/). Refugia are GWL-specific. They are defined as areas in which > 75% of the species present (as defined by the models relative to the climate of 1961–1990) remain present under the changed climate according to more than half of the climate models (GCMs). The Wallace Initiative modelled ∼135,000 terrestrial fungi, plants, invertebrates and vertebrates, at warming levels ranging from 1.5◦C to 6◦C, across 21 CMIP5 climate model patterns at an original spatial resolution of ∼20 km × 20 km based on occurrence data obtained from the Global Biodiversity Information Facility (GBIF.org 2015). The original model runs for the Wallace Initiative used ClimGEN data based on 30-year means based on RCP pathways. The results were subsequently interpolated (per cell) to match the warming levels presented here. The data used in these reports were then subsequently elevationally downscaled to ∼1 km × 1 km (Saunders et al. 2023) to better understand which areas of each modelled 20 km cell or pixel might be lost sooner or persist longer. In short, a given 50 or 20 km cell is an average of the temperatures for all elevations within that cell (i.e., the average elevation). In areas with a varied terrain, some areas will be warmer than the average, and some will be cooler. Species in areas that are warmer than average would be expected to potentially be more exposed to warming, while those in cooler areas would be expected to potentially be less susceptible (or be able to shift into these areas if they are currently too cool). Therefore, species within cooler areas within a climate ‘cell’ or ‘pixel’ would be expected to potentially be able to persist in that area longer. These differences are better able to be captured through the elevational downscaling to 1 km2.
3.8		From Data to Reports
The overall databases underlying the reports consist of 480 raster files representing the biodiversity information (species richness remaining, refugia, adaptation effort) from the Wallace Initiative, 2 raster files holding the land cover and population data, and 11 tables holding climate information based on monthly data from CRU TS 4.07 and downscaled CMIP5 projections derived from ClimGEN outputs. The climate data were pre-processed and converted to a tabular format to reduce the computational demand during report generation. Pre-processing consisted of calculating the values for the periods 1961–1990 and 1991–2020, the difference between them and the changes at each temperature level. To facilitate easy selection of grid cells belonging to an area of interest, each cell was associated with a unique identifier prior to converting the climate rasters to tables.
To automate the generation of reports, an R software tool was developed, consisting of a pipeline of scripts and an R Markdown template. The tool uses a shapefile of an area of interest as input and first determines all PAs within that area based on the spatial intersection of the area shapefile from the World Database on Protected Areas (WDPA, March 2024, all non-marine areas >1 km2 in size; UNEP-WCMC and IUCN 2024). For each of the selected areas, the tool then mines through the overall database, using the shapefile to extract the relevant information. This information is then stored as temporary tables. Depending on data type, the extraction is either directly performed on the raster files or cell identifiers are extracted from a global grid and used to subset the climate data tables. Finally, an R Markdown template uses the temporary tables to generate the final report.
3.9		Caveats
Pattern scaling has been shown to perform well for the range of climate model patterns and RCP scenarios originally run but may not function as well in overshoot scenarios. Nevertheless, the results from subsequent analyses looking specifically at overshoot, using a different set of climate model runs, show similar results to those obtained from the biodiversity models at equivalent levels of warming (Schleussner et al. 2024, Price and Forstenhäusler 2025). Specifically, the model runs in the overshoot scenarios show that biodiversity does not completely recover, at least within the timeframe examined (to 2100). Thus, many of the biodiversity projections should be seen as irreversible, or only partially reversible, in an overshoot scenario where global warming peaks and then slowly declines. This finding suggests that managers should plan to adapt to the peak warming expected and not count on substantial recovery during subsequent warming declines, at least on a century timescale.
The results presented here are able to use warming levels as any potential CO2 fertilisation effects are not part of the models. Key among these, at least for plants, may be reduced water stress from lower precipitation levels driven by CO2-mediated stomatal closure. However, potential benefits to at least some plant species would potentially be offset by potential increases in impacts on animal species owing to reduced protein availability in food plants. This is a topic for further research.
The reports also do not include any potential dispersal into new areas, focusing on existing modelled biodiversity richness. While dispersal benefits some species (Warren, Price, Graham, et al. 2018), this should be seen as being a species-level benefit and not a wholesale benefit within a community of species or a PA. Firstly, the differing rates of dispersal between groups are substantial (Warren, Price, Graham, et al. 2018), with only a few taxa having published rates of dispersal matching the current rate of climate change. Secondly, there are many barriers to dispersal (e.g., transformed landscapes, urban areas), potentially blocking the ability of a species to track changes in the climate. Finally, owing to the differential rate of dispersal, a given area would be projected to have some species lost (climate becomes unsuitable), some remain the same and some ‘new’ species move into the area. Thus, communities would be different than those currently existing, with potential impacts difficult to foresee as there would be many potential changes in interactions (see below).
Interactions between species are not captured in the models, so changes in potential host/food plant, predator/prey, polli- nator/pollen source and so forth potentially make the overall risks of biodiversity loss a lower bound to the potential overall impact. This would potentially be exacerbated by differential rates of dispersal between species. Other caveats (e.g., fire, extreme weather) can be found in Warren, Price, Graham, et al. (2018) and its supplemental material.
3.10		Example Report and Interpretation
At nearly 60,000 ha, Biebrza National Park is the largest National Park in Poland, is among the largest peat bogs in Europe and is important for the conservation of biodiversity, especially birds. The area has been designated both as a Wetland of International Importance and an Important Bird Area. The area is composed of both flooded and unflooded habitats. The flooded habitats include regularly flooded bogs, wet meadows and oxbow lakes. The unflooded habitats include both pine and willow/alder woodlands. The key bird habitats are mostly those for wetland bird species. There are some large agricultural areas, but these have been declining over time (Walesiak et al. 2022).
Each report begins with an executive summary designed to provide as much detail as possible in a concise form, with greater detail provided in the remainder of the report. The exception is that the executive summary also provides a ranking of the overall resilience of the biodiversity of the park at 4◦C warming relative to all other PAs examined in the project, and this is not present in the country-level reports. While the current policies made by nations may limit warming to 2.7◦C (range 2.2◦–3.4◦C) above pre-industrial, and current pledges under the Paris Agreement, if delivered, would hold warming to around 2.1◦ C (range 1.7◦– 2.7◦C) (Climate Action Tracker, https://climateactiontracker.org, Home | Climate Action Tracker, accessed July 23, 2025), the higher level was chosen to try and identify the most resilient areas at the highest potential temperature, particularly in light of the potential additional warming caused by earth system feedbacks (Wunderling et al. 2024). The higher level was also chosen as a potential proxy for climate/weather extremes not included in the species distribution models—higher average climates providing insights into impacts (especially local extinctions) that may be driven by weather extremes at lower levels of warming. This level of warming also allows for capturing uncertainties from the lack of overall Government mitigation activities as well as changes in the understanding of climate sensitivity.
Amongst all the reports prepared, Wallace’s pARCs found that Biebrza National Park is in the bottom 11% of all non-marine PAs
TABLE 1 Observed average monthly high temperature (◦C) with a comparison of the amount of change occurring between 1961–1990 and 1991– 2020 for Biebrza. The average is over the geographic extent of the PA and also the time period in question. ‘Warmest’ refers to the temperature in the warmest year in the 30-year period and coolest to the coolest year; the actual year is not given as this might vary spatially within a large area. In the ‘warmest’ column, yellow shading indicates a temperature equal to that occurring approximately 1 in every 3 years (> 1 SD) compared to 1961–1990; red shading indicates a temperature equal to or greater than that occurring 1 in every 20 years (> 2 SD) compared to 1961–1990. These years may represent “proxies” for conditions during an average year in the future under climate change.
Difference 1991–2020 to
Month	1961–1990	1991–2020	1961–1990
Average	
	Coolest	Average	Warmest	Coolest	Average	Warmest		
Jan	-10.7	-3.0	2.9	-8.0	-1.5	3.0	1.5	
Feb	-8.5	-1.5	5.9	-5.8	0.0	4.6	1.5	
Mar	-0.6	4.1	9.4	0.2	5.6	10.0	1.5	
Apr	10.2	12.1	14.9	9.6	13.8	17.9	1.7	
May	14.2	18.5	21.7	15.7	19.1	23.8	0.6	
Jun	18.4	21.5	25.8	19.5	22.1	26.5	0.6	
Jul	19.1	22.8	26.0	20.9	24.3	27.6	1.6	
Aug	19.9	22.3	25.7	20.2	24.0	27.6	1.6	
Sep	14.0	17.3	21.8	14.0	18.1	20.6	0.8	
Oct	8.2	11.4	14.7	7.6	11.4	15.1	0.0	
Nov	-0.1	4.3	7.3	-1.3	4.8	7.4	0.4	
Dec	-5.9	-0.4	2.9	-5.5	0.3	5.0	0.8	

in the World for projected overall biodiversity resilience to climate change at 4◦C warming above pre-industrial. The models project that this area will require substantial amounts of adaptation to try and build resilience for many of the species for which it is currently important. It also indicates that, in the absence of major mitigation action, adaptation for current species may ultimately fail.
4		Report Details
4.1		Climate
The reports provide information on the projected changes in monthly high, low and average temperatures, precipitation and derived meteorological drought and waterlogging for the area in the report. These projected changes are explored in the context of the recent climate variability to which the area is already exposed (i.e., whether the projected changes are > 1 or > 2 standard deviations relative to 1961–1990, shaded as yellow or red, respectively). All of these values are averaged across all of the cells within the PA boundary. These values were chosen under the assumption that many human and ecological systems may be resilient to changes laying within these bounds of recently experienced natural variability, depending on the return rate of the event. For most observed variables, comparisons are also provided between two recent time periods to show the current trends in climate in the area. Thus, if a month is shaded as red for a particular climate variable, it means that the future average value of that variable during that month is projected to exceed that currently occurring only once in every 20 years (see Table 1 [observed] and Table 2 [projected], for example). The average differences between the climates in 1961–1990 and 1991– 2020 are provided to give information on what changes (if any) in temperature and precipitation have been observed between these two time periods. This will enable managers to understand whether recently observed trends in climate variables that they have observed are projected to continue or not. For example, for Biebrza, the recent winter (DJF) warming in average minimum temperature is greater than that projected to occur at a GWL of 1.5◦ C warming, suggesting the ensemble projection may be an underestimate (see Supporting Information for data on average minimum temperature).
Thus, for the average high temperature, the period from 1961 to 1990 had more abnormally warm months than did the period from 1991 to 2020, with all months showing an increase in average temperature, with January–April, July and August all already 1.5◦ C (locally) warmer than in 1961–1990. A practitioner can use this information to inform their conservation activities if they have data or a memory of how they responded to past warm events. The table provides practitioners with indicative information that potentially helps them reconstruct what past adaptive actions they needed to take in their conservation activities. This understanding can then be used to provide an indication of what resilience-building measures may be necessary under different GWLs.
Thus, events that have occurred rarely in the recent past (and more commonly in 1961–1990) become the new normal by 3◦C of warming. The conservation measures practitioners may have had to employ only occasionally will need tobe employed regularly by 3◦ C for every month, and for July, August and October even with 1.5◦C warming. However, this is the new mean, and there will still

TABLE 2 Projected changes in average monthly high temperature (◦ C) relative to 1961–1990 in Biebrza under alternative scenarios of future change in global annual mean surface air temperature of between 1.5◦ C and 4◦C relative to pre-industrial levels—yellow shading indicates when the projected new average monthly maximum temperature (Tmax) is equal to that occurring approximately 1 in every 3 years (> 1 SD) compared to 1961–1990; red shading indicates when the new average is equal to or greater than that occurring 1 in every 20 years (> 2 SD) compared to 1961–1990.
Scenario
Month	1.5◦C	2◦C	2.5◦C	3◦C	3.5◦C	4◦C	
Jan	1.7	2.5	3.2	4.0	4.7	5.5	
Feb	1.7	2.4	3.1	3.8	4.6	5.3	
Mar	1.6	2.4	3.1	3.8	4.5	5.2	
Apr	1.4	2.0	2.7	3.3	3.9	4.5	
May	1.3	1.9	2.4	3.0	3.6	4.1	
Jun	1.5	2.2	2.9	3.5	4.2	4.9	
Jul	1.9	2.7	3.5	4.3	5.1	6.0	
Aug	2.0	2.9	3.7	4.6	5.5	6.3	
Sep	1.8	2.6	3.4	4.2	5.0	5.7	
Oct	1.7	2.4	3.1	3.8	4.5	5.2	
Nov	1.5	2.2	2.9	3.6	4.3	4.9	
Dec	1.7	2.4	3.1	3.8	4.5	5.3	

be some months in some years with means even warmer than those depicted here. Thus, adaptation plans need to be considered for temperatures in excess of those depicted here to capture the warmer years linked to climate variability and teleconnection events. Especially relevant for Biebrza National Park are the projected increases in temperature in the winter period (DJF). The ensemble model results project that the average monthly high temperature will be above freezing (0◦ C) in December and February at a GWL of 1.5◦ C and 2◦C–2.5◦C in January. This would indicate that it is likely that precipitation that may have formerly fallen as snow in December may now fall as rain, and that snowmelt may begin earlier in the year than currently. The ensemble models suggest that once the GWL exceeds 2.5◦C, there may be impacts on the amount of snow remaining in the landscape. These data suggest that there may be less time for water to be held in the landscape and have the opportunity to percolate into the soils/groundwater. They also indicate the potential for greater winter evaporation that would limit the benefit of any additional precipitation. The data also suggest an increased importance on adaptation efforts that would act to help ‘hold’ water on the landscape, at least in biodiversity-critical areas (especially wetlands).
It should be noted that due to the pattern scaling approach used to project changes in temperature and precipitation, the projected changes in climate variables depend only on the magnitude of global warming and not the rate of warming. Adaptation will be easier when rates of global warming are slower, since there is more time to implement changes, and also because ecosystems will have more time to adapt naturally.
In many parts of the world, observed monthly precipitation data show greater interannual variability than corresponding temperature data. Thus, future precipitation anomalies due to anthropogenic climate change are less likely to push the mean precipitation to levels that exceed the > 1 or > 2 SD thresholds of past (1960–1991) natural variability (Tables 3 and 4).
There have been both wet and dry extreme years (> 2 SD) during both observational periods, with slight declines in the number of extreme wet years and increases in extreme dry years. Some months, particularly the winter months, have become on average wetter, and others, particularly the summer months, drier. However, some of the increases in precipitation are potentially offset with greater evaporation from the increases in temperature.
The climate change projections for precipitation show that the new mean climate does not exceed past extremes owing to the large amount of interannual variability. Nevertheless, the data do show that July–September are projected to become increasingly drier (as well as warmer) with increasing levels of warming.
More informative than looking at precipitation directly is the information on severe drought and waterlogging (Tables 5 and 6) as measured by the standardised precipitation evaporation index (SPEI12 -1.5, +1.5). Drought is a measure of when the SPEI is negative (potential evaporation exceeding precipitation), and waterlogging is a measure of when the SPEI is positive (potential evaporation less than precipitation). The metric looks at droughts developing over the preceding 12 months before the ‘counting’ begins. Thus, an area identified as having a maximum drought duration of 12 months has potentially been in drought for up to 24 months. The values in the table are calculated for the 30- year period for the observed or warming level given. Waterlogging is the reverse of the drought metric (SPEI12, +1.5) and is an indication of areas having excess moisture for extended periods, potentially leading to waterlogged soils.
Thus, since 1961, the amount of time that Biebrza has been in severe drought has been increasing, and that in waterlogging has

TABLE 3 Observed average monthly precipitation (mm/month) with a comparison of the amount of change occurring between 1961–1990 and 1991–2020 in Biebrza. Wettest refers to the year with the greatest precipitation in the 30-year period and driest to the year with the least precipitation. In the wettest and driest columns, yellow shading indicates precipitation amounts exceeding 1 SD above (wettest year) or below (driest year) the 1961–1990 average, that is, amounts comparable to those that occur approximately 1 in every 3 years during 1961–1990; red shading indicates precipitation amounts occurring 1 in every 20 years compared to 1961–1990 (> 2 SD). One way of looking at this is that these years could be seen as ‘proxies’ for what an average year in the future looks like.
Difference 1991–2020 to
Month	1961–1990	1991–2020	1961–1990
Average	
	Wettest	Average	Driest	Wettest	Average	Driest		
Jan	69.7	33.0	11.5	86.6	36.3	8.3	3.3	
Feb	51.5	26.7	2.0	61.9	33.0	8.1	6.3	
Mar	61.0	31.6	8.2	64.1	34.5	12.6	2.9	
Apr	75.1	36.6	5.9	75.0	35.5	4.4	-1.1	
May	103.3	54.3	21.3	105.9	57.2	25.5	2.9	
Jun	121.0	71.5	32.9	127.9	63.7	23.0	-7.8	
Jul	154.6	76.6	25.0	154.6	84.0	11.5	7.4	
Aug	151.2	66.5	17.5	151.0	65.0	9.0	-1.6	
Sep	111.8	53.9	21.8	113.4	52.4	18.2	-1.6	
Oct	175.4	45.2	7.8	126.5	49.5	3.8	4.3	
Nov	86.5	46.2	25.3	79.7	38.6	10.0	-7.6	
Dec	73.3	41.1	9.9	71.8	39.4	14.7	-1.7	
TABLE 4 Projected average monthly precipitation change (mm) relative to 1961–1990 for Biebrza under alternative scenarios of future change in global annual mean surface air temperature of between 1.5◦C and 4◦C relative to the pre-industrial level.

Scenario
Month	1.5◦C	2◦C	2.5◦C	3◦C	3.5◦C	4◦C	
Jan	2.3	3.3	4.3	5.4	6.4	7.4	
Feb	2.0	2.9	3.8	4.6	5.5	6.4	
Mar	2.5	3.6	4.7	5.7	6.8	7.9	
Apr	2.3	3.2	4.2	5.2	6.2	7.2	
May	2.3	3.2	4.2	5.2	6.2	7.1	
Jun	1.3	1.9	2.5	3.1	3.7	4.3	
Jul	-2.1	-2.9	-3.8	-4.7	-5.5	-6.3	
Aug	-1.2	-1.7	-2.2	-2.6	-3.2	-3.6	
Sep	-1.2	-1.7	-2.1	-2.6	-3.1	-3.6	
Oct	1.4	2.0	2.6	3.2	3.8	4.4	
Nov	2.6	3.7	4.9	6.0	7.1	8.2	
Dec	2.5	3.6	4.7	5.9	7.0	8.1	

TABLE 5 Observed number of months in severe drought or waterlogged in Biebrza in a 30-year period, also showing the amount of change that has occurred between 1961–1990 and 1986–2015.
	1961–1990	1986–2015	Difference 1986–2015 to
1961–1990	
In drought	18.8	27.3	8.5	
Waterlogged	22	9.3	-12.9	
	
TABLE 6 Observed maximum number of consecutive months in severe drought or waterlogged in a 30-year period, also showing the amount of change that has occurred between 1961–1990 and 1986–2015.
	1961–1990	1986–2015	Difference 1986–2015 to
1961–1990	
In drought	4.8	10.2	5.4	
Waterlogged	10.8	3.6	-7.2	

TABLE 7 Projected changes in the number of months in severe drought or waterlogged in a 30-year period in Biebrza under alternative GWLs of between 1.5◦C and 4◦C relative to the pre-industrial level.
	1.5◦C	2◦C	2.5◦C	3◦C	3.5◦C	4◦C	
In drought	50.9	74.9	101.5	128.1	152.8	175.1	
Waterlogged	-7.4	-9.5	-10.9	-12.2	-13.2	-14.0	

TABLE 8 Changes in the maximum number of consecutive months in severe drought or waterlogged in a 30-year period in Biebrza under alternative GWLs.
	1.5◦C	2◦C	2.5◦C	3◦C	3.5◦C	4◦C	
In drought	10.3	12.8	16.4	23.8	35.7	45.2	
Waterlogged	-2.6	-4.0	-4.8	-5.7	-6.4	-6.8	

been declining. This is significant in that severe drought is known to have an impact on many wetland species in the area, as well as potentially leading to drying of the underlying peat and increased fire risk. While fires are an annual occurrence in the park, largely started with illegal burning, the number of large fires may be increasing (Walesiak et al. 2022).
Thus, the maximum period when there has been a severe drought has more than doubled. Similarly, there has been more than a threefold reduction in the number of consecutive months of waterlogging. The number of consecutive months is potentially more critical in resilience planning (Tables 7 and 8).
Looking forward, under projected levels of warming, the number of months of severe drought increases substantially, and the number of months undergoing waterlogging is projected to decrease. The likely impact of this increase on wetland biodiversity will be high even at lower levels of warming.
With 1.5◦C warming, severe droughts of greater than 1 year are projected to occur, increasing to more than 2 years with 3◦C warming and 4 years with 4◦C warming. This will likely have large impacts on existing wetland biodiversity as wetlands dry out.
4.2		Land Cover and Human Population Changes
Biodiversity is impacted directly and indirectly by climate but also by human activities ranging from habitat conversion to direct exploitation. While human activities to date have had the greatest impact on biodiversity, the impacts from climate change are rapidly increasing. Looking into the future means not only looking at climate drivers but also human drivers of change.
Satellite data show that, between 1992 and 2020, the park has seen changes in land cover, with decreases in the land cover type Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (< 50%) of 11.8% and increases in Tree cover, broadleaved, deciduous, closed to open (>15%) of 8.8% (ESA 2017). This suggests that there has been a succession taking place, especially in areas where agricultural activities have been abandoned. The key land cover type for biodiversity (flooded shrub or herbaceous cover, flooded) has also declined slightly (1.7%). However, the remote sensing data would be unable to detect levels of environmental degradation until it has reached a point where the land cover would be classified as changed.
The human population within the Biebrza is sparse and has been declining over time. Between 2000 and 2010, the area surrounding Biebrza National Park (within 15 km of the border) has seen a decrease in human population of 20,900 (from 193,384), and this is predicted to continue to decrease to a total of 132,416 by 2050 and to 86,153 by 2100 (SSP2) using population growth projections standardly used in integrated assessment models. In some parts of the world, human populations have been declining in more rural areas as people abandon farming and move into more urban areas. The techniques used in the population projections tend to follow the existing trends in a given area. Thus, areas with declining populations are projected to see continued declines, and those with increasing populations continued increases.
4.3		Impacts on Biodiversity
The bulk of the reports cover the projected changes to biodiversity at increasing GWLs. As the direct impacts of CO2 are not included in the species distribution models, the information associated with GWLs can be used independently of the time period in which the GWL is reached. This obviates the need to create a large cascade of uncertainty by considering a range of potential alternative warming levels that might occur in a particular year in the future. The uncertainty cascade is thus avoided by not considering a specific time period but rather creating advice relevant to any timescale upon which the warming manifests. Instead, it is the speed of adaptation that would need to be altered in the future (as the same level of warming could be reached in different time periods depending on the emission scenario).
The reports contain tables looking at the following terrestrial species groups (taxa) at GWLs of 1.5◦C, 2◦C, 3◦C and 4◦C: biodiversity as a whole; plants as a whole and with subgroupings of ferns, mosses, pines, flowering plants, magnoliopsida, liliopsida (subgroupings of grasses, lilies, orchids, palms and vines) and timber (commercial) species; animals as a whole and with subgroupings of arthropoda, arachnida (with spiders as a subgroup) and insecta as a whole and with subgroupings of bees, beetles, true bugs, flies, lepidoptera (with butterflies and moths considered as subgroups), dragonflies and insect pollinators; chordata (vertebrates) as a whole and with subgroupings of amphibians, birds, mammals and reptiles. Tables are provided for local extinction risk (Table 9) and refugia (Table 10), averaged over the entire PA. Maps showing the spatial variability are provided in the Supporting Information. Both are partial extracts of the full tables in each report. All of the tables and figures are provided in the Supporting Information.
This table shows that projected changes in biodiversity as a whole are > 25% even at 1.5◦C, increasing to 70.8% by 4◦C. Animals show even greater impacts with projected climate unsuitability for 41.7% at 1.5◦ C, increasing to 81.6% at 4◦ C. While vertebrate animals (Chordata) fare somewhat better, the projected local extinctions of invertebrates (especially insects) would have an additive impact on many of the chordates.
Thus, for Biebrza, at 1.5◦C warming, none of the park is a refugium for biodiversity as a whole (i.e., all taxa) for more than half of the models. However, all parts of the park are considered a refugium for at least one climate model—however, the definition we take of refugia requires some confidence despite uncertainty in regional climate projections, and hence, we impose a requirement that at least half of the climate models must concur that a refugium exists.
The likelihood that plant refugia will persist under a GWL of1.5◦C is greater than that for biodiversity as a whole, but this falls to 0% at 2◦ C for more than half the models. Chordates (not shown; see Supporting Information) show more area as being refugia, but, as with local extinctions, the potential loss of insects would potentially have a major impact. Some models (< 10) may indicate that refugia persist longer; however, the uncertainty would be higher. Thus, potential impacts are already projected tobe notable by 2◦C.
In the reports (see Supporting Information for Biebrza), figures (maps) are also provided for species richness remaining, refugia and adaptation effort. The figures can be more informative to practitioners as they show the spatial variation in the projected impact. Thus, while the PA as a whole may not be a refugium, some areas within the PA may be projected to persist. Some of this spatial information can be seen in the maps for adaptation effort, below. The same warming levels are used, but fewer taxa are mapped to save space. The taxa provided as maps are biodiversity as a whole, plants, amphibians, birds, mammals, reptiles, insects, pollinators and timber species. The maps potentially provide the PA managers and conservation planners with a spatial representation of the data to aid in developing adaptation strategies. In terms of adaptation planning, the most useful maps are provided in a new metric called Adaptation Effort (Figure 1).
4.4		Adaptation Effort
Adaptation effort (Figure 1) is a spatial representation of the potential ‘adaptation effort’ potentially needed to maintain at least 75% of the species modelled. Adaptation effort is a way of visualising the number of climate models (+1 to 21) projecting an area to be a refugium as well as the number of climate models (-1 to -21) projecting the area to be an Area of Concern (becoming climatically unsuitable for > 75% of the species). We consider areas with high values (+18 to +21) as being less exposed to climate change and thus potentially less vulnerable, with lower levels of uncertainty from the climate models. Business-as- usual conservation, especially if coupled with building resilience around extreme climates (e.g., drought, heat waves), might therefore be a reasonable adaptation approach to take. As the score drops, increasingly greater amounts of adaptation will likely be needed to maintain the existing species composition. Once the adaptation effort drops into the negative zone, adaptation to maintain existing species is likely to become increasingly difficult. At a score of -15 to -21, the best approach might be to consider facilitating change as opposed to putting large efforts into trying to maintain existing species. Scores this low indicate that the area becomes climatically unsuitable for a large percentage of species modelled. The concept of facilitating change, especially when undertaken in tandem with restoration activities, would be to consider restoration with more climate-resilient species. Potentially, this may mean considering using species currently found in a different biome than currently occurring in the park. This calculation is done in the absence of any consideration of dispersal. The models themselves would capture ‘dispersal’ into the area of the same species that are more equatorward than the current location. As the response of each species to climate change is modelled independently, then the species model indicates the climate suitability irrespective of geographic location. More difficult to interpret and to apply concrete adaptation effort for would be those values in the middle of the range. In general, this score is an indication that as many models assess the area may be a refugium as may be an area of concern. Thus, the uncertainty over the required adaptation effort in areas showing as light grey is high. One approach in these shaded areas is to compare them with the maps showing species richness remaining (see Supporting Information) to determine what the overall estimate percentage is based on half the models. Are the areas projected to retain between 50% and 75% of the species, or is it only 25%–
Climate Resilience and Sustainability, 2026	110fl5
Biodiversity					27.5	39.8	59.8	70.8	
	Plants				19.3	30.8	47.7	57.2	
	Animals				41.7	56.1	74.3	81.6	
		Arthropoda			44.6	60.2	79.5	86.6	
		Arachnida			33.1	48.7	74.6	85.4	
			Spiders		34.2	49.7	76.4	87.4	
		Insecta			45.3	60.6	79.8	86.7	
			Bees		25.5	41.3	61.8	70.9	
			Beetles		50.3	67.0	85.1	90.5	
			True Bugs		41.5	54.4	70.2	78.1	
			Flies		41.7	56.1	76.4	87.0	
			Lepidoptera		48.5	63.4	83.6	89.1	
				Butterflies	15.1	22.1	50.5	66.8	
				Moths	52.9	67.7	87.2	92.3	
			Dragonflies		54.3	70.7	87.0	94.9	
			Pollinators		53.2	68.2	81.8	86.2	
	Chordata				12.0	16.5	24.0	31.2	
		Amphibia			11.8	17.2	19.6	28.0	
		Aves			11.3	15.1	21.0	26.9	
		Mammals			17.0	24.2	44.0	58.5	
		Reptiles			4.4	10.1	15.7	24.0	
TABLE 9 I Percentage of species in different taxonomic groups projected to be at risk of local extinction (averaged over the entire PA) owing to changes in climate alone under alternative scenarios of future change in GWLs of between 1.5°C and 4°C relative to the pre-industrial levels. Yellow shading indicates areas projected to become climatically unsuitable for > 25% of the species studied (by group), darker yellow shading indicates areas projected to become climatically unsuitable for > 50% of the species studied and red shading indicates areas projected to become climatically unsuitable for > 75% of the species studied. NA means there is insufficient data for that group in that area.
Taxa	1.5°C	2°C	3°C	4°C
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TABLE 10 Percentage of area remaining as climatic refugia (i.e., remaining climatically suitable for > 75% of the species) for different taxonomic groups under alternative GWLs, expressed as future change in global annual mean surface air temperature of between 1.5◦ C and 4◦C relative to preindustrial levels (top row) at 1 km resolution. The row showing > 0 means at least 1 and fewer than 11 global circulation models (GCMs) project the area is a refugium at that level of global warming, > 10 means 11 or more GCM models agree the area is a refugium. Dark green indicates > 75% of the PA remains a refugium, mid-green that 50%–75% of the PA is a refugium, light green that 25%–50% of the PA is a refugium and white that < 25% of the PA is a refugium for the taxa shown.
Taxa	1.5◦C	2◦C	3◦C	4◦C	
	>0	> 10	>0	> 10	>0	> 10	>0	> 10	
Biodiversity		100.0	0.0	14.6	0.0	0.0	0.0	0.0	0.0	
	Plants	100.0	100.0	100.0	0.0	50.0	0.0	0.0	0.0	
Note: This is an illustrative portion of a larger table, showing biodiversity as a whole and plants as just one of the sub-groupings.

FIGURE 1 Adaptation effort for biodiversity for Biebrza. Colours range from dark brown to dark green. The numbers refer to the number of climate models in agreement (+ is refugia [green]; - is area of concern [brown]—see below). The greener the shading, the more likely that business-as-usual conservation may be suitable to build resilience, and those that are dark brown indicate that it would be difficult to maintain the majority of the species occurring there (with the possible exception of in micro-refugia < 1km2 in size). These are areas where facilitating change may be a more appropriate resilience strategy.

50% of the species? In both cases, the area would ‘score’ as being neither a refugium nor an area of concern, but the adaptation options, and ecological considerations, would be very different. Those areas projected to retain > 50% of the species would be expected to retain more of their ecosystem services than those retaining < 50% of the species (Parmesan et al. 2022). For those projected to retain > 50% of the species, the required adaptation effort would still be high, and maintaining species’ composition as they currently are would be difficult. One approach would be to concentrate efforts on identifying micro-refugia—those areas < 1 km2 in size that may retain micro-climates longer than the surrounding whole.
The maps for adaptation effort for biodiversity in Biebrza National Park show that even at low levels of warming, most of the park falls within the category of increasing need for adaptation actions. At 1.5◦ C warming, most of the park is in the middle of the adaptation effort (light green). While there is some uncertainty among the climate models (i.e., none are in the dark green +18–21 range), BAU conservation with increased attention to water management and preparedness to deal with drought and potential fires may be adequate (see below). Once warming levels exceed 2◦C, then adaptation options will be increasingly challenging, as many species are projected to be in areas becoming climatically unsuitable. Thus, the area would begin to be in the facilitating change range. Given the importance of the area to retaining carbon stored in the peat, keeping the area wet will be of high importance. Given projected increases in drought frequency and length, then infrastructure may be needed to make sure water is available to maintain wetness in the key wet areas overlaying peat. This will also provide additional adaptation benefits to the birds for which this area is important. Another issue that would be expected to increase over time is fire risk. There have been several large fires in the area in recent years, including in 2020 and 2025. This runs the risk not only of the fires burning into the peat, releasing carbon, but also of soil compaction when large firefighting vehicles drive over the wetland areas to fight the fire. The compacted soils then have reduced capability of acting as a sponge for the water flowing over the wetland, potentially leading to more rapid runoff and additional impacts on the habitat.
5		Conclusion
Much of the water running through the marshes in the park comes from winter precipitation. However, projected increases in maximum winter temperatures suggest the potential for less retention of snow on the landscape. While the severity of dry years has been increasing, projected changes in precipitation are for minor increases in the winter and decreases in the summer. However, the projected changes in drought would indicate that increases in temperature (through potential evapotranspiration) would offset these increases in precipitation. As Biebrza is most known for its marshes, and maintaining wet soils is important to maintain the carbon in the peat from being released, then the projected increase in severe drought duration has the potential to have major impacts on this PA.
The climate suitability of at least 25% of most of the invertebrates and plants studied is projected to be exceeded at a GWL of 2◦C, with most invertebrate taxa studied showing these projected impacts at 1.5◦ C. While climate suitability for vertebrates fares better (3◦ C, except for mammals), the projected potential loss of both food (invertebrate prey) and habitat would suggest that there could be indirect impacts at even lower GWLs.
As GWL increases, adaptation options would decrease, and adaptation efforts would proportionally increase. The maps of adaptation effort show that increased adaptation effort would be needed with a GWL of 1.5◦C and that as warming levels increase, then adaptation efforts would potentially need to, for many areas, shift more to facilitating change. However, given the specific types of habitats this area is important for (e.g., peat), facilitating change to a different habitat mixture may not be the best option (with peat soils also a limiting factor).
Discussions with Biebrza Wildlife Trust suggest that a major current impact on some biodiversity in the marshes has been a change in mowing regimes. Historically, mowing in many parts of the marshes was by hand. Given that parts of the marshes would sometimes be too wet for hand mowing, and that hand mowing itself can lead to differing heights of mown grass, hand mowing would lead to a more heterogenous habitat structure beneficial for biodiversity. However, in recent years the mowing regime has shifted to mechanised mowing. One reason for this was an effort to improve conditions for Ruff (Calidris pugnax). Until the 1960s, Ruff bred in large numbers in the park (https://bbpn.gov. pl/biebrza-national-park) but now are only found sporadically in the area. ‘To encourage ruffs to build nests in the park, overgrown swamp meadows are mowed.’ While mowing has been undertaken, Ruff are still not found in the park in large numbers, with none breeding in most years. Breeding Ruff typically only occurs in years with optimal climatic conditions, even with the mowing that has been undertaken. A review of the data from the Wallace Initiative (Price, unpublished data) shows that the likelihood of occurrence for Ruff declines substantially between current (1961–1990) and a GWL of 1.5◦ C, suggesting the possibility that the failed recovery of Ruff might be due, in part, to climatic shifts tied to observed levels of warming. This is supported by the observation that some Ruff do breed when climatic conditions are perfect. While the shift to mechanised mowing has not led to substantial improvement in Ruff numbers, it has led to a loss of habitat heterogeneity, impacting other species, and can lead to soil compaction. The soil compaction then contributes to quicker water run-off and the potential difficulty of re-wetting the peat soils.
The easiest adaptation is the adaptation that does not need to be done. The climate and biodiversity projections indicate that adaptation will be very difficult with GWL much above 2◦C. So, the best adaptation is mitigation. At lower warming levels, there are adaptation options that could be pursued. These include a return to more historical types of habitat management in an effort to create habitat heterogeneity and reduce soil compaction. Such adaptation will also help to sequester carbon in the soil and help prevent a loss of carbon to the atmosphere, thus contributing also to mitigation. These actions in the park therefore comprise a synergy between adaptation and mitigation.
Biebrza National Park has a large number of bogs, marshes and wet grasslands. It is these habitats that are critical for many of the key species that are, or were, found there. Therefore, maintaining water levels is critical, especially given the projected increase in the duration of severe drought with increasing levels of warming. The EU-Horizon SpongeScapes project seeks to address these issues in general and has a case study on the Upper Biebrza in particular. ‘Sponge measures aim to absorb, store, and slowly release water through landscapes. Sponge measures mitigate floods and droughts and strengthen ecosystems against climate change, biodiversity loss, and pollution (https://www. spongescapes.eu/background).’
SpongeScapes has produced a policy brief (SpongeScapes 2024) outlining some resilience approaches. Sponge functioning
depends on intercepting rainfall, slowing run-off and temporarily storing excess water. Enhancing sponge capacity is thought to have the ability to reduce the impacts of drought and lower the risk of fire. The key is to maintain water in the area long enough for it to be able to filter into the soils. This can be accomplished through improved habitat management as well as the potential addition of measures to slow the run-off (especially in dry periods) such as natural dikes. These measures would also potentially improve the resilience of the area with increasing GWLs, at least to 2◦C. The measures proposed from the SpongeScapes project, in addition to a return to more traditional mowing regimes, would be a good overall adaptation strategy for this PA. However, in the absence of greater mitigation efforts, even these may not be sufficient.
It is also possible to use these reports to assist in larger scale conservation planning. For example, in many of the reports, the maps show areas outside of the PA as well. This potentially could be used to determine whether increasing the size of the PA, or developing a buffer around the PA, might be a useful conservation strategy, with the maps in the reports potentially providing guidance as to which areas are projected to be less exposed to climate change. Taking an even larger view, the maps in the country-level reports provide the same maps as in the PA-specific reports. These maps could then potentially be used to identify areas less exposed to climate change and possibly better places to site new PAs or to implement landscape-level conservation.
Acknowledgements
This work draws on nearly a decade of collaborative research between the Tyndall Centre for Climate Change Research, University of East Anglia, United Kingdom, and the eResearch Centre at James Cook University, Australia. We wish to thank those who played a major role in producing the original underlying models, data and webpages used in the Wallace Initiative—J. VanDerWal, I. Atkinson, D. Baird and P. Lawrey. We would also like to acknowledge the High-Performance Computing Team at the University of East Anglia for assistance in allowing us to make all of the reports and to make them publicly available. Portions of this work have been funded from the MacArthur Foundation funding to World Wildlife Fund-US and IUCN (Ecosystems and Livelihoods Adaptation Network), WWF-UK (Wildlife in a Warming World), UK Natural Environment Research Council (NERC) grant number NE/P014992/1 (IMPALA) and the European Union’s Horizon 2020 research and innovation programmes under grant agreement No. 101003687 (PROVIDE); the climate projections were produced using the ClimGen system funded by NERC (QUEST-GSI; NE/E001831/1) and EU (HELIX; 603864), and we thank Ian Harris and Craig Wallace for producing the underlying data. We acknowledge the climate modelling groups, the Program for Climate Model Diagnosis and Intercomparison and the World Climate Research Programme’s Working Group on Coupled Modelling for producing and making available the CMIP5 model output.
Data Availability Statement
All of the reports are available at https://wallaceparcs.uea.ac.uk/.
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Supporting Information
Additional supporting information can be found online in the Supporting Information section.

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Supplementary Materials: cli270031-sup-0001-SuppMat.pdf