Three Decades of Satisfied Israeli Farmers: Barn Owls (Tyto alba) as Biological Pest Control of Rodents

Peleg, Ori; Nir, Sigalit; Leshem, Yossi; Meyrom, Koby; Aviel, Shauli; Charter, Motti; Roulin, Alexandre; Izhak, Ido

doi:10.5070/v42811039

Cited by 7 publications

(8 citation statements)

References 23 publications

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“…and is mostly made up of cattle fodder (wheat, sweet corn, alfalfa, clover, vetch and oats), grain crops and seeds (wheat and sweet corn), spices and herbs (oregano, hyssop, basil, and dill), olive orchards and small villages. Nest boxes were added in the area as part of using barn owls as biological pest control agents against rodents (Meyrom et al 2009;Peleg et al 2018).…”

Section: Study Sitementioning

confidence: 99%

“…Third, barn owls are common in agricultural regions due to an abundance of rodents, which, puts them at heightened risk to secondary poisoning from rodenticide use. Additionally, they have a large variation in their reproductive success (Martínez and López 1999;Charter et al 2015;Roulin 2020), and they are opportunistic feeders specializing in small mammals and their diets often vary according to region (Gubanyi et al 1992;Klok and De Roos 2007) thereby affecting their reproductive success (Klok and De Roos 2007;Charter et al 2018) Barn owls are frequently used in nest box schemes both in conservation (Petty et al 1994) and as biological pest control agents of rodents in agriculture (Meyrom et al 2009;Kross et al 2016;Wendt and Johnson 2017;Peleg et al 2018). Despite the usefulness of nest boxes, they may potentially act as "ecological traps" (Klein et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Movement ecology, breeding, diet, and roosting behavior of barn owls (Tyto alba) in a transboundary conflict region

et al. 2021

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Transboundary frontiers often contain wildlife habitats that are fractured by geopolitical borders, which could have adverse effects on the wildlife that inhabit those areas. We examined the movement, breeding, roosting and diet of 15 GPS-tagged barn owls (Tyto alba) along the Israeli-Jordanian and Israeli-Palestinian Authority borders. Our results showed that 80% of the barn owls hunted across the border. On average, the owls engaged in 5.4 hunting trips per night, 16% of which were cross-border excursions, and they crossed the borders as frequently as expected randomly, highlighting the importance of wildlife protection on all sides. Hunting movement, rather than cross-border activity, had an effect on the annual reproductive success and diet composition. Speci cally, female owls that spent more time at the nest and engaged in longer distance hunting trips had higher reproductive success. 80% of the females roosted outside of the nest box at a median distance of 908 m (range = 199-4,112 m). Only 13.3% of the owls (2/15 owls) roosted across the border. These results increase our understanding of the movement, breeding, and roosting behavior of a non-migratory avian raptor. However, a serious lack of cooperation and communication between bordering countries hampers our ability to understand the full effects of differing environmental policies on a species that knows no borders.

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Section: Study Sitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Movement ecology, breeding, diet, and roosting behavior of barn owls (Tyto alba) in a transboundary conflict region

et al. 2021

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“…Data collection was carried out in 2018, using wild T. alba individuals in the Hula Valley (33°6′N, 35°37′E), Israel as part of a Barn Owl Monitoring Scheme (Peleg et al, 2018 ). During 2015–2017, the western barn owl monitoring scheme routinely collected cadavers.…”

Section: Methodsmentioning

confidence: 99%

Anatomy of bristles on the nares and rictus of western barn owls (Tyto alba)

2022

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Nocturnal owls (Strigiformes), such as barn owls (Tyto spp.), can locate prey in complete darkness due to their enhanced auditory localisation ability and binocular vision (Gill, 2007;Martin, 2017;Stevens, 2007). However, they also possess facial bristles, which are simple, hair-like feathers that occur on the upper mandible and surround the bill of many species, including around the nares and rictal regions. Küster (1905) previously described the rictal bristle anatomy of four species of owls (Bubo bubo, Asio flammeus, Athene noctua and Strix aluco) and Cunningham et al. (2011) in one species (Ninox novaeseelandiae). Küster (1905) found that the bristle follicles were connected by muscle and connective tissue, and associated with nerves and mechanoreceptors (Küster, 1905;Stettenheim, 1973). Küster (1905) and Cunningham et al. (2011) both noticed Herbst corpuscles surrounding the rictal bristle follicles. Herbst corpuscles

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“…Although integrated pest control methods for controlling voles have been suggested that combine irrigation to flood fields and the introduction of natural predators [37,38], these methods have not solved the problem completely, and rodenticide is still used. One of the major problems with controlling voles is that farmers do not have methods for determining the abundance and locations of voles in their fields.…”

Section: Motivationmentioning

confidence: 99%

How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows

et al. 2021

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Small mammals, and particularly rodents, are common inhabitants of farmlands, where they play key roles in the ecosystem, but when overabundant, they can be major pests, able to reduce crop production and farmers’ incomes, with tangible effects on the achievement of Sustainable Development Goals no 2 (SDG2, Zero Hunger) of the United Nations. Farmers do not currently have a standardized, accurate method of detecting the presence, abundance, and locations of rodents in their fields, and hence do not have environmentally efficient methods of rodent control able to promote sustainable agriculture oriented to reduce the environmental impacts of cultivation. New developments in unmanned aerial system (UAS) platforms and sensor technology facilitate cost-effective data collection through simultaneous multimodal data collection approaches at very high spatial resolutions in environmental and agricultural contexts. Object detection from remote-sensing images has been an active research topic over the last decade. With recent increases in computational resources and data availability, deep learning-based object detection methods are beginning to play an important role in advancing remote-sensing commercial and scientific applications. However, the performance of current detectors on various UAS-based datasets, including multimodal spatial and physical datasets, remains limited in terms of small object detection. In particular, the ability to quickly detect small objects from a large observed scene (at field scale) is still an open question. In this paper, we compare the efficiencies of applying one- and two-stage detector models to a single UAS-based image and a processed (via Pix4D mapper photogrammetric program) UAS-based orthophoto product to detect rodent burrows, for agriculture/environmental applications as to support farmer activities in the achievements of SDG2. Our results indicate that the use of multimodal data from low-cost UASs within a self-training YOLOv3 model can provide relatively accurate and robust detection for small objects (mAP of 0.86 and an F1-score of 93.39%), and can deliver valuable insights for field management with high spatial precision able to reduce the environmental costs of crop production in the direction of precision agriculture management.

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Three Decades of Satisfied Israeli Farmers: Barn Owls (Tyto alba) as Biological Pest Control of Rodents

Cited by 7 publications

References 23 publications

Movement ecology, breeding, diet, and roosting behavior of barn owls (Tyto alba) in a transboundary conflict region

Movement ecology, breeding, diet, and roosting behavior of barn owls (Tyto alba) in a transboundary conflict region

Anatomy of bristles on the nares and rictus of western barn owls (Tyto alba)

How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows

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