Enrico Merli
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Enrico Merli has a total of 26 co-authors. Here are the top co-authors by number of shared publications:
- Stefano Grignolio
Department of Life Science and Biotechnology, University of Ferrara, via Borsari 46, Ferrara, 44121, Italy
3 shared publications Alberto Meriggi
University of Pavia
1 shared publication- Andrea Marcon
Department of Veterinary Medicine, University of Sassari, Sassari, Italy
1 shared publication - Andrea Viviano
Consiglio Nazionale delle Ricerche, Istituto per la BioEconomia (CNR-IBE), Firenze, Italy
1 shared publication - Duccio Berzi
Department of Veterinary Medicine, University of Sassari, Sassari, Italy
1 shared publication - Elena Bassi
Department of Veterinary Medicine, University of Sassari
1 shared publication
Latest Publications
- 6 February 2024
- Published by Springer Nature in Mammalian Biology
- Vol. 104 (2) , 215-220
Sika deer Cervus nippon out of the blue: a cryptic invasion in Italy
- 6 February 2024
- Published by Springer Nature in Mammalian Biology
- Vol. 104 (2) , 215-220
Sika deer Cervus nippon out of the blue: a cryptic invasion in Italy
- Open Access
Estimating Wolf Population Size and Dynamics by Field Monitoring and Demographic Models: Implications for Management and Conservation
We estimated the current size and dynamics of the wolf population in Tuscany and investigated the trends and demographic drivers of population changes. Estimates were obtained by two different approaches: (i) mixed-technique field monitoring (from 2014 to 2016) that found the minimum observed pack number and estimated population size, and (ii) an individual-based model (run by Vortex software v. 10.3.8.0) with demographic inputs derived from a local intensive study area and historic data on population size. Field monitoring showed a minimum population size of 558 wolves (SE = 12.005) in 2016, with a density of 2.74 individuals/100 km2. The population model described an increasing trend with an average annual rate of increase λ = 1.075 (SE = 0.014), an estimated population size of about 882 individuals (SE = 9.397) in 2016, and a density of 4.29 wolves/100 km2. Previously published estimates of wolf population were as low as 56.2% compared to our field monitoring estimation and 34.6% in comparison to our model estimation. We conducted sensitivity tests to analyze the key parameters driving population changes based on juvenile and adult mortality rates, female breeding success, and litter size. Mortality rates played a major role in determining intrinsic growth rate changes, with adult mortality accounting for 62.5% of the total variance explained by the four parameters. Juvenile mortality was responsible for 35.8% of the variance, while female breeding success and litter size had weak or negligible effects. We concluded that reliable estimates of population abundance and a deeper understanding of the role of different demographic parameters in determining population dynamics are crucial to define and carry out appropriate conservation and management strategies to address human–wildlife conflicts.
Open AccessEstimating Wolf Population Size and Dynamics by Field Monitoring and Demographic Models: Implications for Management and Conservation
We estimated the current size and dynamics of the wolf population in Tuscany and investigated the trends and demographic drivers of population changes. Estimates were obtained by two different approaches: (i) mixed-technique field monitoring (from 2014 to 2016) that found the minimum observed pack number and estimated population size, and (ii) an individual-based model (run by Vortex software v. 10.3.8.0) with demographic inputs derived from a local intensive study area and historic data on population size. Field monitoring showed a minimum population size of 558 wolves (SE = 12.005) in 2016, with a density of 2.74 individuals/100 km2. The population model described an increasing trend with an average annual rate of increase λ = 1.075 (SE = 0.014), an estimated population size of about 882 individuals (SE = 9.397) in 2016, and a density of 4.29 wolves/100 km2. Previously published estimates of wolf population were as low as 56.2% compared to our field monitoring estimation and 34.6% in comparison to our model estimation. We conducted sensitivity tests to analyze the key parameters driving population changes based on juvenile and adult mortality rates, female breeding success, and litter size. Mortality rates played a major role in determining intrinsic growth rate changes, with adult mortality accounting for 62.5% of the total variance explained by the four parameters. Juvenile mortality was responsible for 35.8% of the variance, while female breeding success and litter size had weak or negligible effects. We concluded that reliable estimates of population abundance and a deeper understanding of the role of different demographic parameters in determining population dynamics are crucial to define and carry out appropriate conservation and management strategies to address human–wildlife conflicts.
- 14 October 2022
- Published by Elsevier in Animal Behaviour
- Vol. 194, 79-88
Behavioural syndromes going wild: individual risk-taking behaviours of free-ranging wild boar
- 14 October 2022
- Published by Elsevier in Animal Behaviour
- Vol. 194, 79-88
Behavioural syndromes going wild: individual risk-taking behaviours of free-ranging wild boar
- 25 May 2017
- Published by Wiley in Journal of Zoology
- Vol. 303 (2) , 155-164
Wild boar under fire: the effect of spatial behaviour, habitat use and social class on hunting mortality
The wild boar (Sus scrofa) is an ungulate of major management concern, often controlled only by indiscriminate, recreational hunting, which represents its main cause of death. Several studies have dealt with the effects of hunting on wild boar populations, pointing out controversial changes in spatial behavior and habitat use related to it. However, little information on the relationship between spatial behavior, habitat use and hunting mortality is available. In this study, 105 wild boars were monitored by means of radio‐tracking. Their cause of death was assessed, thus confirming the major role of hunting. For 89 of them, spatial behavior and habitat use as well as social class were related to hunting mortality. Spatial behavior, expressed as home range size, average daily distance among fixes and familiarity with the hunting area, resulted in having no strong effect on hunting mortality. Conversely, the relationship between the wild boar and the habitat, expressed in the proportion of forests in the home range, landscape complexity and habitat diversity, had a major role in predicting hunting mortality. In particular, the best model including social class and refuges pointed out a positive effect of landscape complexity on hunting mortality, while animals having a higher proportion of forests in their home range (assessed when this did not overlap the protected area) were less likely to be culled. Adult males and subadults of both sexes were more liable to be hunted than family groups. On the whole, behavioral and ecological differences in hunting vulnerability should be taken into account in management plans in order to optimize population management.
- 25 May 2017
- Published by Wiley in Journal of Zoology
- Vol. 303 (2) , 155-164
Wild boar under fire: the effect of spatial behaviour, habitat use and social class on hunting mortality
The wild boar (Sus scrofa) is an ungulate of major management concern, often controlled only by indiscriminate, recreational hunting, which represents its main cause of death. Several studies have dealt with the effects of hunting on wild boar populations, pointing out controversial changes in spatial behavior and habitat use related to it. However, little information on the relationship between spatial behavior, habitat use and hunting mortality is available. In this study, 105 wild boars were monitored by means of radio‐tracking. Their cause of death was assessed, thus confirming the major role of hunting. For 89 of them, spatial behavior and habitat use as well as social class were related to hunting mortality. Spatial behavior, expressed as home range size, average daily distance among fixes and familiarity with the hunting area, resulted in having no strong effect on hunting mortality. Conversely, the relationship between the wild boar and the habitat, expressed in the proportion of forests in the home range, landscape complexity and habitat diversity, had a major role in predicting hunting mortality. In particular, the best model including social class and refuges pointed out a positive effect of landscape complexity on hunting mortality, while animals having a higher proportion of forests in their home range (assessed when this did not overlap the protected area) were less likely to be culled. Adult males and subadults of both sexes were more liable to be hunted than family groups. On the whole, behavioral and ecological differences in hunting vulnerability should be taken into account in management plans in order to optimize population management.
- 1 December 2006
- Published by Springer Nature in Mammal Research
- Vol. 51 (4) , 383-394
Using harvest data to predict habitat-population relationship of the wild boarSus scrofa in Northern Italy
- 1 December 2006
- Published by Springer Nature in Mammal Research
- Vol. 51 (4) , 383-394
Using harvest data to predict habitat-population relationship of the wild boarSus scrofa in Northern Italy