The beaver facilitates species richness and abundance of terrestrial and semi-aquatic mammals

Beavers are ecosystem engineers which are capable to facilitate many groups of organisms. However, their facilitation of mammals has been little studied. We applied two methods, camera trapping and snow track survey to investigate the facilitation of a mammalian community by the ecosystem engineering of the American beaver (Castor canadensis) in a boreal setting. We found that both mammalian species richness (83% increase) and occurrence (12% increase) were significantly higher in beaver patches than in the controls. Of individual species, the moose (Alces alces) used beaver patches more during both the ice-free season and winter. The Eurasian otter (Lutra lutra), the pine marten (Martes martes) and the least weasel (Mustela nivalis) made more use of beaver sites during the winter. Our study highlights the role of ecosystem engineers in promoting species richness and abundance, especially in areas of relatively low productivity. Wetlands and their species have been in drastic decline during the past century, and promoting facilitative ecosystem engineering by beaver is feasible in habitat conservation or restoration. Beaver engineering may be especially valuable in landscapes artificially deficient in wetlands. (c) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer reviewe

First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus):a collaborative approach

Additional file 8: Table S7. Allelic patterns in 2000–2010 (pop1 - pre-arctos) and 2011–2017 (pop2 - arctos & post arctos). Na number of different alleles, Na Freq. ≥5% number of alleles with a frequency ≥ 5%, Ne number of effective alleles, I Shannon Information Index, No. Private Alleles number of private alleles, Ho observed heterozygosity and He expected heterozygosity

Additional file 5 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 5: Table S5. List of 194 selected samples representing 115 individual bear genotypes out of 125 (ISPRA BIO-CGE). When available, samples from invasive (blood and tissues) and systematic sampling (hairs) were preferred to non-invasive and opportunistic ones, the more recent to the older samples, hairs to feces. Since samples from 10 genotypes (Italian national reference biobank, ISPRA BIO-CGE) were not available, their genotypes were not updated with CXX20 and REN144A06 loc

Additional file 7 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 7: Fig. S1. Relationship between theoretical probability of identity and number of loci assayed using four heterozygosity levels. (a) randomly sampled individuals and (b) sibs. From [37]

Additional file 1 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 1: Table S1. List of invasive samples used to verify matches among genotypes and calculate scores’ calibration. List of the 6 selected samples representing 6 genetically different individuals sharing the lowest number of alleles, therefore resuming the observed variability of the whole data set. These samples were analyzed by Lab2 during the Life Arctos Project [52] and by Lab3 during the present study, in order to verify matches among genotypes and calculate scores’ calibration between labs

Additional file 3 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 3: Table S3. PCR primer sequences used at Lab2, at Lab3 and in this study for the Apennine brown bear individual identification

Additional file 4 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 4: Table S4. Putative identical genotypes that differ between labs. Genotypes ram0587 and HS374 were identified by Lab2, genotypes Gen 108 and Gen 105 were identified by Lab3. a missing data, b mismatched loci

Additional file 8 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 8: Table S7. Allelic patterns in 2000–2010 (pop1 - pre-arctos) and 2011–2017 (pop2 - arctos & post arctos). Na number of different alleles, Na Freq. ≥5% number of alleles with a frequency ≥ 5%, Ne number of effective alleles, I Shannon Information Index, No. Private Alleles number of private alleles, Ho observed heterozygosity and He expected heterozygosity

Additional file 2 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 2: Table S2. Four different STR markers sets compared in this study. Both labs use the Amelogenin gene (AMG) to assess gender. a [52], b [14, 41, 50]

Additional file 6 of First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Additional file 6: Table S6. Sampling trend of Apennine brown bear individuals over times. Genotypes were subdivided into two groups based on the years of sampling: 2000–2010 pre-arctos bears, 2011–2017 arctos and post arctos bears. Individuals sampled in both periods were eliminated from the analysis