The use of socio-economy in species distribution modelling: Features of rural societies improve predictions of barn owl occurrence

Żmihorski, Michał; Kowalski, M.; Cichocki, Jan; Rubacha, Sławomir; Kotowska, Dorota; Krupiński, Dominik; Rosin, Zuzanna M.; Šálek, Martin; Pärt, Tomas

doi:10.1016/j.scitotenv.2020.140407

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…To assess the effect of urban land cover on occupancy and productivity, we measured the proportion of all developed areas (including developed open space and developed low, medium, and high intensities) within a 1‐km radius using the National Land Cover Database 2016 (Dewitz 2020). We also quantified land cover composition, which has been reported to correlate with barn owl occupancy and productivity (Leech et al 2009, Wright 2018, Żmihorski et al 2020), including proportions of shrub or scrub, grassland, pasture or hay fields, and cultivated crops. We used a 1‐km radius for calculating road and land cover variables based on previous studies of barn owl reproduction (Frey et al 2011, Hindmarch et al 2014, Wendt and Johnson 2017), and because it approximated a common barn owl home range (Taylor 1994, Arlettaz et al 2010).…”

Section: Methodsmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

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Roads reduce breeding occupancy and productivity in barn owls

Busby,

Gordon,

Belthoff

2024

J Wildl Manag

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Urbanization and expanding road networks threaten some avian populations through habitat loss and degradation. Barn owls (Tyto alba) have been particularly affected through roadway mortality, loss of grassland, and conversion or destruction of nesting sites. To combat declines and bolster reproduction, and as part of integrated pest management to reduce crop damage, some land managers provide supplemental owl nesting sites via the installation of nest boxes. If nearby habitat and land cover characteristics are not considered when placing boxes, such as major roads or other anthropogenic features, owls could be attracted to locations that could hinder populations further. We investigated the effects of roads and urban areas on barn owl breeding occupancy and productivity to provide information to help guide the placement of nest boxes. We monitored >300 nest boxes over the 2020 and 2021 breeding seasons in southwestern Idaho, USA, where substantial roadway mortality of barn owls occurs. Barn owls occupied >60% of nest boxes, but the likelihood of breeding occupancy decreased with increasing proximity of nest boxes to roads. Boxes 500 m from roads had a predicted probability of occupancy of over 0.9, which was nearly double that of boxes within 100 m. Proximity to roads also was associated with reduced productivity such that boxes within 100 m of roads were predicted to produce approximately 1 fewer fledgling than those 500 m away. There was no evidence that the proportion of urban area surrounding nest boxes substantially influenced breeding occupancy or productivity. Thus, land managers can consider placing nest boxes for barn owls farther from roads when possible as a potential tactic to increase the probability of breeding occupancy and to foster higher productivity.

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Section: Methodsmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

Roads reduce breeding occupancy and productivity in barn owls

Busby,

Gordon,

Belthoff

2024

J Wildl Manag

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“…The maps provide information on where to focus limited resources in mitigating impacts of the free-roaming dogs, not only on the area where the dogs were located, but in other areas with available environmental variables employed by the model. For many species, socioeconomic data was used as an environmental variable, particularly for areas where settlements or human influence has a significant effect on the distribution of the species (Gallardo & Aldridge 2013;Kapitza et al 2021;Żmihorski et al 2020). Model results include measures of variable importance and significance for fine-tuning and evaluating model performance to determine whether further iterations are required.…”

Section: Species Distribution Modelling For Tongatapu Islandmentioning

confidence: 99%

Distribution characteristics of the free-roaming dog population of the western area of Tongatapu, Kingdom of Tonga: Developing a GIS for biosecurity and animal welfare projects

Aguilar,

de Felice

2023

PiBs

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The free-roaming dog population of Tongatapu, Kingdom of Tonga, is a ubiquitous, well-known and persistent feature of the island. Information on the quantity, condition and spatial distribution characteristics of these dogs is needed for projects aimed at improving biosecurity conditions in different impacted sectors of society, while addressing the management of an animal associated with humans. Health and safety of the human population, agriculture and biodiversity are directly affected by the presence of free-roaming dogs in Tongan communities. A survey conducted over western Tongatapu recorded locations of free-roaming dogs sighted when driving along the roads of 12 towns and villages. Locations were recorded using an iPad and the mobile application Collector for ArcGIS. A total of 1152 dogs was recorded, mostly within villages and residential roads. Patterns of spatial distribution of dog locations were described using kernel density, hotspot analysis, and cluster and outlier analysis tools available in ArcGIS Pro. Using the species distribution modelling algorithms Maxent and random forest (RF), the suitability of the entire island for free-roaming dogs was determined using as input the dog locations and environmental variables consisting of population, land use and road types. Results of both algorithms show that roads and residential areas consistently show the highest probability of free-roaming dog presence. The RF algorithm also produced maps that show predictions of the values of other variables collected during the survey, including body-condition scores, age group and sex types over the entire island. Overall, the maps produced and the database created contribute a baseline biosecurity knowledge-base supporting rational decision-making and governance for the island.

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“…vegetation cover; Barbet‐Massin & Jetz, 2014; Hof et al., 2012; Wisz et al., 2013). It is less common to model species using variables that reflect human and socio‐cultural influences (Żmihorski et al., 2020), yet in the modern world the distributions of many species are at least in part determined by humans (Boivin et al., 2016). Modelling the distribution of ancient trees, which have strong human and historical links to the landscape, presents a unique opportunity in our study to explore the potential of including anthropogenic and historical predictors in SDMs to provide meaningful and accurate predictions of species locations.…”

Section: Introductionmentioning

confidence: 99%

Historical maps confirm the accuracy of zero‐inflated model predictions of ancient tree abundance in English wood‐pastures

Nolan

Reader

Gilbert

et al. 2021

Journal of Applied Ecology

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1. Ancient trees have important ecological, historical and social connections, and are a key source of dead and decaying wood, a globally declining resource. Woodpastures, which combine livestock grazing, open spaces and scattered trees, are significant reservoirs of ancient trees, yet information about their true abundance within wood-pastures is limited. England has extensive databases of both ancient trees and wood-pasture habitat, providing a unique opportunity for the first largescale, national case study to address this knowledge gap.2. We investigated the relationship between the abundance of ancient trees in a large sample of English wood-pastures (5,571) and various unique environmental, historical and anthropogenic predictors, to identify wood-pastures with high numbers of undiscovered ancient trees. A major challenge in many modelling studies is obtaining independent data for model verification: here we introduce a novel model verification step using series of historic maps with detailed records of trees to validate our model predictions. This desk-based method enables rapid verification of model predictions using completely independent data across a large geographical area, without the need for, or limitations associated with, extensive field surveys.3. Historic map verification estimates correlated well with model predictions of tree abundance. Model predictions suggest there are ~101,400 undiscovered ancient trees in all wood-pastures in England, around 10 times the total current number of ancient tree records. Important predictors of ancient tree abundance included wood-pasture area, distance to several features including cities, commons, historic Royal forests and Tudor deer parks, and different types of soil and land classes.4. Synthesis and applications. Historical maps and statistical models can be used in combination to produce accurate predictions of ancient tree abundance in woodpastures, and inform future targeted surveys of wood-pasture habitat, with a focus on those deemed to have undiscovered ancient trees. This study provides support for improvements to conservation policy and protection measures for ancient trees and wood-pastures.

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The use of socio-economy in species distribution modelling: Features of rural societies improve predictions of barn owl occurrence

Cited by 10 publications

References 35 publications

Roads reduce breeding occupancy and productivity in barn owls

Roads reduce breeding occupancy and productivity in barn owls

Distribution characteristics of the free-roaming dog population of the western area of Tongatapu, Kingdom of Tonga: Developing a GIS for biosecurity and animal welfare projects

Historical maps confirm the accuracy of zero‐inflated model predictions of ancient tree abundance in English wood‐pastures

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