Our study highlights the importance of using multiple variables as indicators to integrate biodiversity conservation criteria into management planning of the Mediterranean forests.

All structural variables are relevant and linked to at least one taxonomic group, but no single structural variable is linked to many taxa, so none can be used alone as proxy indicator of forest biodiversity.

We found that forest subgroups are more related to these variables than its corresponding full groups, but each taxa responds differently to variations in these variables.

We related 10 forest structural variables with 6 taxonomic groups and 4 taxonomic subgroups, evaluating its usefulness as indirect indicators of biodiversity to incorporate them in forest management planning.

In conclusion…

GMR provided the most accurate estimates and allowed estimating the population sex-ratio

✔️ Keys of success of GMR: The narrowly targeted fieldwork design + The high molecular identification rate
⚠️Monitoring rare species with HDS can be risky to obtain a sufficient sample size in single-year studies

Methods taking into account imperfect detection #hierarchicalmodels
Hierarchical Distance Sampling (HDS): Multispecies HDS (general survey design) & Sandgrouse HDS (survey design specific to Pin-tailed sandgrouse)
Genetic Mark-Recapture: Faeces collection when the population is aggregated and closed

Our study highlights the importance of targeting conservation action on the reproductive core. It also identifies potential expansion areas, where efforts to promote #coexistence will be key to ensure population recovery

📈 Model projections suggest future population stability under different scenarios of female loss and potential for population spread with increased reproduction events at the periphery

Adult males lead the expansion front, while females and juveniles stay at the core ♂️♀️

Bear density is positively affected by the area where the population originated 🐻 💢

➡️ Slow population expansion is likely caused by female philopatry

We used non-invasive data from the long-term monitoring program of the Pyrenean Brown bear population 🧬

Integrating the age-structured model of #NathanHostetter into an OPSCR model we obtained density estimates for ♀️♂️ at different age categories 🐻 #NIMBLE🚀