Scientific study, which deals with the impact of different climate change mitigation strategies on biodiversitySpecifically, it examines impact of forest and bioenergy strategies on the ranges of 14,234 vertebrate speciesThe study uses extensive datasets on habitat, climate and species occurrence to model how these strategies affect available habitat and climatic conditions for different species.

Methodology

1. Data collection and preparation:
   • Obtaining data on biodiversity, climate, land use and other supporting data.
   • Using species occurrence data from the Global Biodiversity Information Facility (GBIF) and IUCN.
   • Data cleaning and processing, including removing records outside the expected range and filtering fossil records.
   • Creating two sets of occurrence records for birds: "breeding" and "non-breeding".
   • Obtaining global climate variables from the Coupled Model Intercomparison Project (CMPIP6) and IPCC.
   • Using the IUCN Global Habitat Map, which modifies the 2015 Copernicus landcover data using biodiversity records.
   • Projection of changes in habitat composition by 2050 based on fractional covers of 32 plant functional types.
   • Obtaining maps of current carbon stocks in plant biomass and soil.
   • Using maps of maximum tree cover percentage, global biomes, and sedimentary basins.
2. Creating range maps (AOH) for each species:
   • Modeling the bioclimatic envelope of each species using MaxEnt species distribution models (SDMs).
   • Combining bioclimatic envelope with habitat data and species affinities to habitats to create AOH maps.
   • Consideration of different species spread scenarios, including zero, limited and ideal spread.
3. Modeling the impacts of habitat conversion caused by LBMS:
   • Assessing the impacts of reforestation and afforestation and bioenergy crops.
   • Identifying pixels that are biophysically capable of supporting trees through natural growth.
   • Excluding pixels where carbon gains would be offset by changes in albedo.
   • Distinguishing between reforestation and afforestation based on historical habitats.
   • Modeling the impacts of bioenergy crops with an emphasis on grasslands.
   • Updating AOH maps for each species after applying afforestation and bioenergy strategies.
4. Calculating the impacts of LBMS on climate mitigation:
   • Calculating the amount of atmospheric carbon removed by afforestation and carbon sequestration from bioenergy crops.
   • Taking into account carbon emissions associated with fertilizing bioenergy crops.
   • Calculation of the carbon benefit of replacing fossil fuels with bioenergy.
   • Modeling carbon sequestration through carbon capture and storage (CCS) in geological reservoirs.
   • Converting carbon stocks into changes in average global warming levels and regional changes in bioclimatic variables.
   • Calculation of changes in AOH of each species depending on changes in bioclimatic variables.
5. Aggregation of results:
   • Presenting results at the species level, including logarithmic ratio of AOH changes.
   • Calculation of spatially specific changes in AOH, averaged across all species.
   • Identifying which LBMS is better for biodiversity in a given pixel.

Main findings

• Afforestation it has a positive impact for some species, while it has a negative impact for others.
• Bioenergy crops they have the potential to mitigate climate change, but can have a negative impact on biodiversity.
• The impact of forestry and bioenergy strategies varies by species and region.
• Climate change can significantly affect species ranges regardless of LBMS implementation.
• The study highlights the importance taking into account the various influences of LBMS (habitat conversion and climate mitigation) and their mutual influence.

Sources of uncertainty

• Using a single SDM model (MaxEnt) and the same predictor variables for all species.
• Uncertainty in maps of global forest growth potential, carbon sequestration, and albedo.
• Simplified modeling of climate impact on species.
• Uncertainty arising from modelling species distribution and adaptation.
• Lack of details about how species use different habitats.

Conclusion

The study provides a comprehensive overview of the impacts of forest and bioenergy strategies on biodiversityThe results show that it is important take into account complex influences LBMS for different species and regions, rather than focusing only on global indicators. It is necessary carefully consider trade-offs between climate mitigation and biodiversity protection when deciding on the implementation of LBMS. Further research should focus on improving models and taking into account other factors influencing the impact of LBMS on biodiversity. Spring

Glossary of key terms

• AOH (Area of Habitat): Habitat area represents the area that is suitable for a given species in terms of climate and habitat types.
• Bioclimatic envelope: The range of climatic conditions that a given species is able to tolerate, expressed as the relative degree of suitability of individual sites.
• LBMS (Land-Based Mitigation Strategy): A land-based mitigation strategy, method, or procedure for using land to reduce greenhouse gas emissions or remove carbon from the atmosphere.
• Reforestation: Forest restoration in places where forests historically existed.
• Aforestation: Planting forests in places that were historically non-forested.
• MaxEnt: A species distribution model based on the principle of maximum entropy, used to model the distribution of species based on their occurrence and environmental variables.
• GBIF: Global Biodiversity Information Facility, a global database of species occurrence data.
• SDM (Species Distribution Model): Species distribution model, makes predictions about the spatial distribution of species.
• Bilinear interpolation: A method for estimating values between two known data, used to resample data.
• Kriging: An interpolation method for estimating spatial values based on the position and value of surrounding points.
• Pseudo-absences: Data on locations where a given species is not expected to occur, which is used to train species distribution models.
• Sedimentary basins: Geological structures suitable for long-term carbon storage.
• Biome: A large ecosystem with characteristic vegetation and climate.
• Albedo: The rate at which the Earth's surface reflects sunlight, can affect warming and cooling.
• SSP2-RCP4.5: Common Socio-Economic Pathway 2 (SSP2) combined with Representative Concentration Pathway 4.5 (RCP4.5), one of the reference scenarios for future climate development.
• Lignocellulosic plants: Plants whose main components are cellulose, lignin and hemicellulose. They are used for bioenergy production.