Longitudinal stability of detection dog behavioral assessment: A follow-up study of long-term working success

IF 2.2 2区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Applied Animal Behaviour Science Pub Date : 2023-10-10 DOI:10.1016/j.applanim.2023.106082
Lucia Lazarowski , Bart Rogers , Jordan G. Smith , Sarah Krichbaum , Paul Waggoner
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Abstract

Detection dogs are a critical asset to homeland security, law enforcement, conservation efforts, and more. However, due to the challenges faced in operational working roles and the behavioral requirements to succeed in such roles, many dogs bred and trained for detection tasks are ultimately deemed unsuitable. Therefore, reliable methods for selecting dogs with the highest chances of success are needed. Studies aiming to identify the behavioral traits associated with success and methods for assessment have focused on short-term outcomes related to completion of training or selection for working roles. However, intervening factors may emerge leading to attrition once in service. The goal of the current study was to determine the longitudinal stability of performance on a detection dog behavioral suitability test as well as its predictive validity in determining working success. We followed a cohort of candidate detection dogs (n = 61) to determine changes in number of dogs successfully completing training and still working two years later, as well as determine the long-term predictive validity of the behavioral test. We also repeated the test in a subset of dogs (n = 34) to determine the longitudinal stability of performance on the test from puppy to mature adult. We found that training completion success rates were similar to current working success rates (i.e., minimal attrition). Additionally, performance on aspects of the puppy test such as emotional reactivity conducted at 3 months was predictive of current status (p = .008, OR =.246), and significantly correlated with performance at 3 years (r = .330, p = .028). These findings demonstrate longitudinal stability in behavioral traits considered important for detection dog success, which will be valuable for improving behavioral assessment and selection measures.

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检测犬行为评估的纵向稳定性:长期工作成功的随访研究
侦查犬是国土安全、执法、保护工作等方面的重要资产。然而,由于在操作工作角色中面临的挑战以及成功担任此类角色的行为要求,许多为检测任务而饲养和训练的狗最终被认为不合适。因此,需要可靠的方法来选择成功几率最高的狗。旨在确定与成功相关的行为特征和评估方法的研究集中在与完成培训或选择工作角色相关的短期结果上。然而,一旦服役,可能会出现导致人员流失的干预因素。当前研究的目标是确定检测犬行为适应性测试的纵向稳定性及其在确定工作成功方面的预测有效性。我们跟踪了一组候选检测犬(n=61),以确定成功完成训练并在两年后仍在工作的犬数量的变化,并确定行为测试的长期预测有效性。我们还在一组狗(n=34)中重复了这项测试,以确定从幼犬到成年犬的测试纵向稳定性。我们发现,培训完成率与当前的工作成功率相似(即,最小损耗)。此外,小狗测试各方面的表现,如在3个月时进行的情绪反应,可以预测目前的状态(p=0.008,OR=.246),并与3年时的表现显著相关(r=.330,p=0.028)。这些发现表明,行为特征的纵向稳定性被认为是检测狗成功的重要因素,这对于改进行为评估和选择措施将是有价值的。
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来源期刊
Applied Animal Behaviour Science
Applied Animal Behaviour Science 农林科学-行为科学
CiteScore
4.40
自引率
21.70%
发文量
191
审稿时长
18.1 weeks
期刊介绍: This journal publishes relevant information on the behaviour of domesticated and utilized animals. Topics covered include: -Behaviour of farm, zoo and laboratory animals in relation to animal management and welfare -Behaviour of companion animals in relation to behavioural problems, for example, in relation to the training of dogs for different purposes, in relation to behavioural problems -Studies of the behaviour of wild animals when these studies are relevant from an applied perspective, for example in relation to wildlife management, pest management or nature conservation -Methodological studies within relevant fields The principal subjects are farm, companion and laboratory animals, including, of course, poultry. The journal also deals with the following animal subjects: -Those involved in any farming system, e.g. deer, rabbits and fur-bearing animals -Those in ANY form of confinement, e.g. zoos, safari parks and other forms of display -Feral animals, and any animal species which impinge on farming operations, e.g. as causes of loss or damage -Species used for hunting, recreation etc. may also be considered as acceptable subjects in some instances -Laboratory animals, if the material relates to their behavioural requirements
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