scholarly journals Citizen science contribution to national wolf population monitoring: what have we learned?

2020 ◽  
Vol 66 (3) ◽  
Author(s):  
N. Ražen ◽  
Ž. Kuralt ◽  
U. Fležar ◽  
M. Bartol ◽  
R. Černe ◽  
...  
BMC Ecology ◽  
2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Hanna Granroth-Wilding ◽  
Craig Primmer ◽  
Meri Lindqvist ◽  
Jenni Poutanen ◽  
Olaf Thalmann ◽  
...  

Author(s):  
Gretchen H Roffler ◽  
Jason N Waite ◽  
Rodney W Flynn ◽  
Kristian R Larson

Wolves (Canis lupus) in Southeast Alaska have been proposed for listing under the U.S. Endangered Species Act first in 1993, and more recently in 2011. Reports of declining wolf populations sparked concern, in addition to high rates of logging and broad-scale succession patterns predicted to negatively impact Sitka black-tailed deer (Odocoileus hemionus sitkensis), the primary ungulate prey species of wolves in Southeast Alaska. Given the recurring interest of wolf viability in the region, and in order to manage wolves and their prey sustainably, it is imperative to obtain regular and reliable population estimates. However, monitoring wolves in temperate rainforests is challenging because the landscape obscures visibility and lowers success of traditional methods such as aerial surveys and radio collar mark-recapture. We used hair snares to collect DNA samples and spatially-explicit capture-recapture (SECR) models to estimate fall wolf density during 2012–2015 on northcentral Prince of Wales Island (POW), Alaska. We incorporated covariates including sex, behavioral responses, and site-specific changes in effectiveness of detection probability by fitting hybrid mixture models to the data. We also incorporated into our models landscape variables including forest habitats in various management conditions and succession stages to relate to wolf habitat selection. We concurrently implemented a traditional approach for comparison to the DNA-based SECR method using radiocollared wolf data to estimate population abundance with minimum counts and the size of wolf packs and pack territories. The results of the DNA-based SECR method proved to be more reliable, efficient, cost-effective, and robust than the traditional method, which was sensitive to violations of model assumptions. Our efforts to improve SECR density estimate precision by increasing the hair sampling intensity and area resulted in more wolf hair detections and redetections, and increased the number of unique wolves redetected. Based on multiple lines of evidence, we report a decline in wolf population abundance over the past 2 decades in northcentral POW. We conclude that DNA-based SECR is an effective tool for regular population monitoring, as is required in situations of elevated concern for the persistence of a population, and may simultaneously provide information on heterogeneous landscape use, an important wildlife management consideration in fragmented forests.


2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ivana Matić ◽  
Iva Gorše ◽  
Milomir Stefanović ◽  
Mihajla Djan ◽  
Nevena Veličković

Genetic monitoring, as one of the main aspects in long-term population monitoring, has a crucial role in establishing an effective management strategy and biological conservation of wildlife. Determination and monitoring of genetic variability as well as identification of management units, represents the best estimator of natural population potential for adaptation and survival. Here we present a comparative overview of data obtained through genetic monitoring of three wildlife species in Serbia - brown hares, wild boars and grey wolves. First determination of genetic variability in brown hares from Serbia recommended an optimal three-year monitoring period for this species and continual genetic monitoring has revealed maintenance of moderate genetic diversity over a twenty-year period. Furthermore, it is suggested that future genetic monitoring should encompass more informative molecular markers, such as those linked to adaptive traits. Microsatellite molecular markers have provided much of the required information about the wild boar population. The wild boar is one of the most important game species and is crucial to estimate adequate management measures in order to preserve genetic variability, but also, to prevent possible territorial expansion of the species. Panel of 18 microsatellite loci proved to be very informative when it comes to the grey wolf population. Serbian wolf population is relatively stable for now, but it is very important to maintain appropriate genetic monitoring to preserve this valuable reservoir of genetic variability. The results, obtained through genetic monitoring of these three species in Serbia, support integration of genetic information with other traditional methods for hunting management strategy in order to provide a long-term sustainability.


2021 ◽  
Author(s):  
Philipp H Boersch-Supan ◽  
Robert A Robinson

Accurate and robust population trend assessments are key to successful biodiversity conservation. Citizen science surveys have provided good evidence of biodiversity declines whilst engaging people with them. Citizen scientists are also collecting opportunistic biodiversity records at unprecedented scales, vastly outnumbering records gathered through structured surveys. Opportunistic records exhibit spatio-temporal biases and heterogeneity in observer effort and skill, but their quantity offers a rich source of information. Data integration, the combination of multiple information sources in a common analytical framework, can potentially improve inferences about populations compared to analysing either in isolation. We combine count data from a structured citizen science survey and detection-nondetection data from an opportunistic citizen science programme. Population trends were modelled using dynamic N-mixture models to integrate both data sources. We applied this approach to two different inferential challenges arising from sparse data: (i) the estimation of population trends for an area smaller than a structured survey stratum, and (ii) the estimation of national population trends for a rare but widespread species. In both cases, data integration yielded population trajectories similar to those estimated from structured survey data alone but had higher precision when the density of opportunistic records was high. In some cases this allowed inferences about population trends where indices derived from single data sources were too uncertain to assess change. However, there were differences in the trend magnitude between the integrated and the standard survey model. We show that data integration of large-scale structured and unstructured data is feasible and offers potential to improve national and regional wildlife trend estimates, although a need to independently validate trends remains. Smaller gains are achieved in areas where uptake of opportunistic recording is low. The integration of opportunistic records from volunteer-selected locations alone may therefore not adequately address monitoring gaps for management and policy applications. To achieve the latter, scheme organisers should consider providing incentives for achieving representative coverage of target areas in both structured and unstructured recording schemes.


2016 ◽  
Author(s):  
Gretchen H Roffler ◽  
Jason N Waite ◽  
Rodney W Flynn ◽  
Kristian R Larson

Wolves (Canis lupus) in Southeast Alaska have been proposed for listing under the U.S. Endangered Species Act first in 1993, and more recently in 2011. Reports of declining wolf populations sparked concern, in addition to high rates of logging and broad-scale succession patterns predicted to negatively impact Sitka black-tailed deer (Odocoileus hemionus sitkensis), the primary ungulate prey species of wolves in Southeast Alaska. Given the recurring interest of wolf viability in the region, and in order to manage wolves and their prey sustainably, it is imperative to obtain regular and reliable population estimates. However, monitoring wolves in temperate rainforests is challenging because the landscape obscures visibility and lowers success of traditional methods such as aerial surveys and radio collar mark-recapture. We used hair snares to collect DNA samples and spatially-explicit capture-recapture (SECR) models to estimate fall wolf density during 2012–2015 on northcentral Prince of Wales Island (POW), Alaska. We incorporated covariates including sex, behavioral responses, and site-specific changes in effectiveness of detection probability by fitting hybrid mixture models to the data. We also incorporated into our models landscape variables including forest habitats in various management conditions and succession stages to relate to wolf habitat selection. We concurrently implemented a traditional approach for comparison to the DNA-based SECR method using radiocollared wolf data to estimate population abundance with minimum counts and the size of wolf packs and pack territories. The results of the DNA-based SECR method proved to be more reliable, efficient, cost-effective, and robust than the traditional method, which was sensitive to violations of model assumptions. Our efforts to improve SECR density estimate precision by increasing the hair sampling intensity and area resulted in more wolf hair detections and redetections, and increased the number of unique wolves redetected. Based on multiple lines of evidence, we report a decline in wolf population abundance over the past 2 decades in northcentral POW. We conclude that DNA-based SECR is an effective tool for regular population monitoring, as is required in situations of elevated concern for the persistence of a population, and may simultaneously provide information on heterogeneous landscape use, an important wildlife management consideration in fragmented forests.


2015 ◽  
Vol 77 (08/09) ◽  
Author(s):  
L Del Savio ◽  
A Buyx ◽  
B Prainsack
Keyword(s):  

2019 ◽  
Vol 41 (6) ◽  
pp. 963-1000
Author(s):  
Minsu Park ◽  
Younghee Noh
Keyword(s):  

2019 ◽  
Vol 39 (2) ◽  
pp. 169 ◽  
Author(s):  
Holly L. Bernardo ◽  
Pati Vitt ◽  
Rachel Goad ◽  
Susanne Masi ◽  
Tiffany M. Knight

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