Impact model: BioScen1.5-SDM-GAM

The BioScen1.5-SDM-GAM models are species distribution models of 15496 terrestrial vertebrate species (2964 amphibians, 8493 birds and 4039 mammals) using Generalised Additive Models (GAMs) as model algorithm. BioScen1.5-SDM-GAM are statistical models, which were calibrated using a 30-year average (1980-2009) of the EWEMBI observed climate input data set and IUCN and BirdLife range maps of each vertebrate species. The models are then projected to simulated historical, current and future 30-year average climate data from ISIMIP2b, resulting in probabilities of occurrence and summed probabilities of occurrence for the three different vertebrate taxa.

Sector
Terrestrial biodiversity
Region
global
Contact Person

Information for the model BioScen1.5-SDM-GAM is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Basic information
Model output license: CC BY 4.0
Reference Paper: Main Reference: Hof C, Voskamp A, Biber M, Böhning-Gaese K, Engelhardt E, Niamir A, Willis S, Hickler T et al. Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. Proceedings of the National Academy of Sciences,115,13294-13299,2018
Reference Paper: Other References:
Output Data
Experiments: II, III
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2018-11-23
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Input data sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Observed Atmospheric Climate Data Sets Used: EWEMBI
Other Data Sets Used: Land-sea mask
Climate Variables: tasmax, tasmin, pr
Additional Information About Input Variables: 30-year monthly means of minimum temperature (tasmin), maximum temperature (tasmax) and total precipitation (pr) were calculated and used to derive 19 bioclimatic variables (see Hijmans et al. 2005). The best combination of 4 variables was selected for each taxon (amphibians, birds, mammals) by comparing the performance of all uncorrelated combinations of 4 variables. As a result, Bio4 (temperature seasonality), Bio5 (max temperature of warmest month), Bio12 (annual precipitation) and Bio15 (precipitation seasonality), were used for modelling birds and mammals and Bio4, Bio5, Bio18 (precipitation of warmest quarter) and Bio19 (precipitation of coldest quarter) were used for modelling amphibians.
Additional Input Data Sets: Present biodiversity data for amphibians, terrestrial mammals and terrestrial birds were obtained from expert range maps provided by the International Union for Conservation of Nature (2011) and BirdLife International (2014) and gridded to a resolution of 0.5°x0.5°.
Model specifications
Model Algorithm: GAM
Explanatory Variables: Bio4 (temperature seasonality), Bio5 (max temperature of warmest month), Bio12 (annual precipitation) and Bio15 (precipitation seasonality) were used for modelling birds and mammals. Bio4, Bio5, Bio18 (precipitation of warmest quarter) and Bio19 (precipitation of coldest quarter) were used for modelling amphibians.
Response Variable: absence/presence of species
Additional Information About Response Variable: Range maps only give us information about the presence of a species, absence data was created by using a distance kernel and random selection of at least 1000 points.
Distribution Of Response Variable: Binomial
Parameters: fx=FALSE, k=-1, bs="tp", gamma=1.4
Additional Information About Parameters: Model performance was evaluated internally using cross-validation. Only models with an AUC of > 0.7 were kept as results.
Software Function: gam()
Software Package: mgcv
Software Program: R
Model Output: probability of occurrence
Additional Information About Model Output: The model output is the probability of occurrence of a species, which can vary between 0 and 1 (see for example amphibian-prob output files). Note: Probability of occurrence is projected to the present and all neighbouring realms of a species and so sort of represents the unlimited dispersal of a species in the future. The output (probability of occurrence) of all species is then stacked (added up) to a summed probability of occurrence (a proxy of species richness) for (i) all species, (ii) all endemic species and (iii) all threatened species of the three modelled vertebrate taxa (see for example amphibian-sum-prob output files). Summed probability of occurrence is split into different dispersal scenarios (no dispersal, d/2, d, 2*d, full dispersal). Full dispersal represents the sum of the probability of occurrence output files. No dispersal assumes that species can only be present where they are actually present according to the IUCN and BirdLife range maps. The other three dispersal scenarios consider species-specific dispersal buffers added to the present range, where d is the largest diameter of the original range of the species.
Note of caution
Note of caution about applicability of model use due to modeling concept: Projecting models to pre-industrial and extended future time periods was only performed to allow for comparability with the other modeling sectors and should be used with caution. Historical projections are limited to the species that were modeled and do not include species which went extinct over the last decades or centuries (which we did not model). Historical richness is thus very unlikely to be a realistic estimate of past richness as it is probably highly underestimated. Future and especially extended future projections should also be used with caution, as species interactions and other processes are not considered when running individual models even though they do influence species distributions and may change in the future, altering the future distribution of a species. Furthermore, the distribution data used to calibrate the models are range maps drawn by experts based on current and historical distribution records as well as additional information and expert knowledge. Thus, they do not represent the distribution at a specific point in time, but a rough outline of a species current distribution. Therefore, precise projections of individual years are not sensible for this modelling approach.
Basic information
Model output license: CC BY 4.0
Reference Paper: Main Reference: Hof C, Voskamp A, Biber M, Böhning-Gaese K, Engelhardt E, Niamir A, Willis S, Hickler T et al. Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. Proceedings of the National Academy of Sciences,115,13294-13299,2018
Reference Paper: Other References:
Output Data
Experiments: historical
Climate Drivers: EWEMBI
Date: 2018-11-23
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Input data sets used
Observed Atmospheric Climate Data Sets Used: EWEMBI
Other Data Sets Used: Land-sea mask
Climate Variables: tasmax, tasmin, pr
Additional Information About Input Variables: 30-year monthly means of minimum temperature (tasmin), maximum temperature (tasmax) and total precipitation (pr) were calculated and used to derive 19 bioclimatic variables (see Hijmans et al. 2005). The best combination of 4 variables was selected for each taxon (amphibians, birds, mammals) by comparing the performance of all uncorrelated combinations of 4 variables. As a result, Bio4 (temperature seasonality), Bio5 (max temperature of warmest month), Bio12 (annual precipitation) and Bio15 (precipitation seasonality), were used for modelling birds and mammals and Bio4, Bio5, Bio18 (precipitation of warmest quarter) and Bio19 (precipitation of coldest quarter) were used for modelling amphibians.
Model specifications
Model Algorithm: GAM
Explanatory Variables: Bio4 (temperature seasonality), Bio5 (max temperature of warmest month), Bio12 (annual precipitation) and Bio15 (precipitation seasonality) were used for modelling birds and mammals. Bio4, Bio5, Bio18 (precipitation of warmest quarter) and Bio19 (precipitation of coldest quarter) were used for modelling amphibians.
Response Variable: absence/presence of species
Additional Information About Response Variable: Range maps only give us information about the presence of a species, absence data was created by using a distance kernel and random selection of at least 1000 points.
Distribution Of Response Variable: Binomial
Parameters: fx=FALSE, k=-1, bs="tp", gamma=1.4
Additional Information About Parameters: Model performance was evaluated internally using cross-validation. Only models with an AUC of > 0.7 were kept as results.
Software Function: gam()
Software Package: mgcv
Software Program: R
Model Output: probability of occurrence
Additional Information About Model Output: The model output is the probability of occurrence of a species, which can vary between 0 and 1 (see for example amphibian-prob output files). Note: Probability of occurrence is projected to the present and all neighbouring realms of a species and so sort of represents the unlimited dispersal of a species in the future. The output (probability of occurrence) of all species is then stacked (added up) to a summed probability of occurrence (a proxy of species richness) for (i) all species, (ii) all endemic species and (iii) all threatened species of the three modelled vertebrate taxa (see for example amphibian-sum-prob output files). Summed probability of occurrence is split into different dispersal scenarios (no dispersal, d/2, d, 2*d, full dispersal). Full dispersal represents the sum of the probability of occurrence output files. No dispersal assumes that species can only be present where they are actually present according to the IUCN and BirdLife range maps. The other three dispersal scenarios consider species-specific dispersal buffers added to the present range, where d is the largest diameter of the original range of the species.
Note of caution
Note of caution about applicability of model use due to modeling concept: Projecting models to pre-industrial and extended future time periods was only performed to allow for comparability with the other modeling sectors and should be used with caution. Historical projections are limited to the species that were modeled and do not include species which went extinct over the last decades or centuries (which we did not model). Historical richness is thus very unlikely to be a realistic estimate of past richness as it is probably highly underestimated. Future and especially extended future projections should also be used with caution, as species interactions and other processes are not considered when running individual models even though they do influence species distributions and may change in the future, altering the future distribution of a species. Furthermore, the distribution data used to calibrate the models are range maps drawn by experts based on current and historical distribution records as well as additional information and expert knowledge. Thus, they do not represent the distribution at a specific point in time, but a rough outline of a species current distribution. Therefore, precise projections of individual years are not sensible for this modelling approach.