Ecography. Supplementary material

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1 Ecography ECOG Ranc, N., Santini, L., Rondinini, C., Boitani, L., Poitevin, F., Angerbjörn, A. and Maiorano, L Performance tradeoffs in target-group bias correction for species distribution models. Ecography doi: /ecog Supplementary material

2 APPENDIX 1. Study area boundaries. Figure A1. Map of the Mediterranean basin and study area (red).

3 APPENDIX 2. Data providers. Table B1. Main data providers and datasets with corresponding records number and geographic coverage. Category Data provider Dataset Number of records Geographic coverage Academia Published data 3082 Study area Researcher dataset Study area SFEPM/Groupes Chiroptères/ONF French Bat Atlas 8235 France Atlas Spanish Mammal Atlas Spain Lazio Mammal Atlas Lazio, Italy Sicily Atlas 2754 Sicily, Italy African Mammal Databank 695 Morocco (Africa) Data sharing facilities GBIF Observado 3392 Study area (Worldwide) Study area (Worldwide) Silene 7536 S France Ecology consulting EcoMed 1921 S France

4 Government Organization Israel National Park Service Israel Museum Crete Museum 3044 Crete, Greece Tel Aviv Museum 3722 Israel LPO PACA Faune PACA 2792 SE France LPO Ardèche Faune Ardèche 2398 SE France Non Governmental Organization LPO Drôme Faune Drôme 9404 SE France GCC 4178 SE France GCP Corsica, France ALEPE/COG/LPO34-11/GOR Meridionalis 3006 SW France SFEPM: Spanish Mammal Atlas: Palomo J. (2008). Atlas y libro rojo de los mamiferos terrestres de España. ICONA (Organismo Autonomo Parques Nacionales). ISBN-10: Lazio Mammal Atlas: a6979d0a6d59 Sicily Mammal Atlas: Collana Studi e Ricerche dell ARPA Sicilia (2008). Atalante della Biodiversità della Sicilia: Vertebrati terrestri. ISBN African Mammal Databank: Boitani, L., Sinibaldi, I., Corsi, F., De Biase, A., Carranza, I. D., Ravagli, M., Reggiani, G., Rondinini, C. & Trapanese, P. (2008). Distribution of medium- to large-sized African mammals based on habitat suitability models. Biodivers. Conserv. 17, GBIF: Observado: Silene: EcoMed: Israel National Park Service: Crete Museum: Tel Aviv Museum: available through GBIF Faune PACA: Faune Ardèche: Faune Drôme: Groupe Chiroptères de Corse (GCC): Groupe Chiroptères de Provence (GCP): Meridionalis:

5 APPENDIX 3. Species-specific covariates and results. Table C1. Alphabetic list of all 92 range-based virtual species with corresponding species-specific covariates, mean SDM performance and three associated response variables. Abbreviations: TG = target-group (Bats, L.M. = Large Mammals, S.M. = Small Mammals), N = presence sample size, ROA = relative occurrence area, rnroa = ratio presence sample size to relative occurrence area, NB = niche breadth, Y1 = bias ratio, Y2 = improvement through TE-correction (TE = true effort) and Y3 = improvement through TG-correction. ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Barbastella barbastellus Bats Eptesicus serotinus Bats Miniopterus schreibersii Bats

6 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Myotis bechsteinii Bats Myotis blythii Bats Myotis capaccinii Bats Myotis daubentonii Bats Myotis emarginatus Bats Myotis myotis Bats Myotis mystacinus Bats Myotis nattereri Bats Myotis punicus Bats Nyctalus lasiopterus Bats

7 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Nyctalus leisleri Bats Nyctalus noctula Bats Pipistrellus kuhlii Bats Pipistrellus nathusii Bats Pipistrellus pipistrellus Bats Pipistrellus pygmaeus Bats Pipistrellus savii Bats Plecotus auritus Bats Plecotus austriacus Bats Rhinolophus euryale Bats

8 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Rhinolophus ferrumequinum Bats Rhinolophus hipposideros Bats Rhinolophus mehelyi Bats Tadarida teniotis Bats Canis lupus L.M Capra pyrenaica L.M Capreolus capreolus L.M Castor fiber L.M Cervus elaphus L.M

9 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Erinaceus europaeus L.M Felis silvestris L.M Hystrix cristata L.M Lepus corsicanus L.M Lepus europaeus L.M Lepus granatensis L.M Lepus timidus L.M Lutra lutra L.M Lynx pardinus L.M Marmota marmota L.M

10 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Martes foina L.M Martes martes L.M Meles meles L.M Mustela erminea L.M Mustela lutreola L.M Mustela nivalis L.M Mustela putorius L.M Oryctolagus cuniculus L.M Rupicapra rupicapra L.M Sciurus vulgaris L.M

11 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Sus scrofa L.M Ursus arctos L.M Vulpes vulpes L.M Apodemus flavicollis S.M Apodemus sylvaticus S.M Arvicola amphibius S.M Arvicola sapidus S.M Chionomys nivalis S.M Crocidura leucodon S.M

12 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Crocidura russula S.M Crocidura sicula S.M Crocidura suaveolens S.M Eliomys quercinus S.M Glis glis S.M Micromys minutus S.M Microtus agrestis S.M Microtus arvalis S.M Microtus cabrerae S.M Microtus duodecimcostatus S.M

13 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Microtus gerbei S.M Microtus lusitanicus S.M Microtus multiplex S.M Microtus savii S.M Mus spretus S.M Muscardinus avellanarius S.M Myodes glareolus S.M Neomys anomalus S.M Neomys fodiens S.M Rattus norvegicus S.M

14 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Sorex antinorii S.M Sorex araneus S.M Sorex coronatus S.M Sorex granarius S.M Sorex minutus S.M Sorex samniticus S.M Suncus etruscus S.M Talpa caeca S.M Talpa europaea S.M Talpa occidentalis S.M

15 ECOLOGICAL CORRELATES SDM RESULTS DEPENDENT VARIABLES Scientific name TG N ROA rnroa NB Unbiased Biased TE-Corrected TG-Corrected Y1 Y2 Y3 Talpa romana S.M

16 APPENDIX 4. Simulating virtual species true distribution. Range-based virtual species distributions D1). These were obtained through a SDM approach based on existing species ranges (Fig. SPECIES RANGE PRESENCE POINTS ENV. VARIABLES ENV. VARIABLES SDM Algorithm ENV. VARIABLES RANDOM BACKGROUND RANGE-BASED VIRTUAL DISTRIBUTION Figure D1. Schematic representation of the species distribution model (SDM) approach used to simulate range-based virtual species distributions. Species geographic ranges were used to generate spatially-contained presence points. These were contrasted against random background points distributed throughout the study area. Presence points and random background were used, in addition to a set of environmental variables, as input in the SDM calibration. The SDM continuous output represents the range-based virtual species distribution. In the present figure we used Myotis capaccinii.

17 True virtual species distributions These were simulated by combining bell-shaped species response functions to the six environmental covariates used in the analyses. We generated a wide- and narrow-ranging species by altering the bell-shaped parameters mean and standard deviation. In both cases, we set the mean for each predictor variable to the average value of this predictor across the Mediterranean basin (i.e. the study area). We set the standard deviation of each predictor to 1 and 4 times its standard deviation within the study area for the narrow- and wide-ranging species, respectively. We then converted the environmental suitability to a probability of presence by applying a linear conversion of slope 1. These true virtual species were obtained using the function generatespfromfun from the R package virtual species (Leroy et al. 2015). Leroy, B. et al virtualspecies, an R package to generate virtual species distributions. - Ecography. 39:

18 APPENDIX 5. Post hoc comparisons. Table E1. Post hoc comparisons for the different SDM types. Hypotheses Estimate Std. Error z value Pr(> z ) TE-Corrected - biased == <1e-04 TG-Corrected - biased == <1e-04 unbiased - biased == <1e-04 TG-Corrected - TE-Corrected == unbiased - TE-Corrected == unbiased - TG-Corrected == <1e-03 Table E2. Post hoc comparisons for the different target-groups. Hypotheses Estimate Std. Error z value Pr(> z ) Large_mammals - Bats == <1e-04 Small_mammals - Bats == Small_mammals - Large_mammals == <1e-04

19 APPENDIX 6. Mixed-effect model coefficients and variable importance Figure F1. Importance of several potential predictors on (a) bias ratio, (b) improvement through TE-correction (TE = true effort), (c) Improvement through TG-correction. Importance is estimated by measuring the relative increase in model s mean square error (MSE) when a given independent variable is randomly permuted. Abbreviations: TG = target-group, N = presence sample size, ROA = relative occurrence area, rnroa = ratio presence sample size to relative occurrence area, NB = niche breadth.

20 Figure F2. Partial dependence plots of main effects and interactions on bias ratio. Abbreviations: TG = target-group, N = presence sample size, ROA = relative occurrence area, rnroa = ratio presence sample size to relative occurrence area, NB = niche breadth.

21 Figure F3. Partial dependence plots of main effects and interactions on the improvement through TE-correction (TE = true effort). Abbreviations: TG = target-group, N = presence sample size, ROA = relative occurrence area, rnroa = ratio presence sample size to relative occurrence area, NB = niche breadth.

22 Figure F4. Partial dependence plots of main effects and interactions on the improvement through TG-correction. Abbreviations: TG = target-group, N = presence sample size, ROA = relative occurrence area, rnroa = ratio presence sample size to relative occurrence area, NB = niche breadth.