DOI: 10.5061/dryad.wwpzgmsvb
title: Data from: Human-wildlife coexistence needs more evidence-based interventions to reduce the losses of crops, livestock, and fishery catches

description: Evidence-based interventions designed to reduce wildlife-caused losses are
 essential for human-wildlife coexistence. The lack of systematic
 summarization of research effort and evidence makes it challenging for
 researchers, managers, and policymakers to prioritize interventions for
 evaluation and implementation.  Here, we compiled experimental
 case studies of nonlethal technical interventions designed to reduce the
 losses of crops, livestock, and fishery catches caused by terrestrial
 carnivores, elephants, farmland birds, and marine fauna worldwide. Then,
 we summarized the research effort and the performance of interventions by
 their sensory stimuli and target animals. We found that: (i) 54 out of 88
 interventions included in this study had statistically effective evidence,
 where only 39% (21/54) were evaluated with more than three experiments
 (ii) physical-, sound-, chemical-, and light (or visual) -based
 interventions were the most in numbers and their performance varied
 greatly; (iii) farmland birds, seabirds, and cetaceans were the most
 studied animal groups while there are only a few experiments for
 elephants; and (iv) the interventions for marine fauna generally had no
 impact on the target catch of fisheries. Syntheses and applications: Our
 results indicated that collective effort is needed to further evaluate
 interventions using various sensory stimuli and launch incentive programs
 to motivate the implementation of interventions, particularly related to
 marine fauna conservation. Our synthesis could be helpful for stakeholders
 to tackle the negative human-wildlife interactions outlined as Target 4 of
 the Kunming-Montreal Global Biodiversity Framework.
description: Compilation of the list of case studies We
 collected case studies from the six syntheses (van Eeden<em> et
 al.</em> 2018b; Khorozyan 2022; Lorand<em> et al.</em>
 2022; Montgomery<em> et al.</em> 2022; Huang<em> et
 al.</em> 2023; Huang<em> et al.</em> 2024), which were
 based on systematic search schemes and provided the most comprehensive
 lists of case studies to our knowledge. For terrestrial carnivores, van
 Eeden<em> et al.</em> (2018b) compiled 114 case studies based
 on four systematic reviews (Miller<em> et al.</em> 2016;
 Treves, Krofel & McManus 2016; Eklund<em> et al.</em>
 2017; van Eeden<em> et al.</em> 2018a); Lorand<em> et
 al.</em> (2022) compiled 103 studies; and Khorozyan (2022) compiled
 244 studies to summarize the study designs. For elephants,
 Montgomery<em> et al.</em> (2022) compiled 95 studies. For
 farmland birds, Huang<em> et al.</em> (2023) compiled 48
 studies. For marine fauna, Huang<em> et al.</em> (2024)
 compiled 119 studies. After removing the duplicated studies, 594 case
 studies were left for the initial list (Fig, S1).
 Inclusion criteria These six syntheses had
 different inclusion criteria about target animal groups, types of
 interventions, study designs, reported data, and statistical approaches
 (Table S1). According to our research objectives, we set six criteria to
 make the research effort comparable. If a case study did not meet one of
 the following criteria, we would exclude it from further review.
 <strong>Language</strong>. The studies had to be
 published in the working languages of the reviewing team (i.e., English
 and Chinese). <strong>Problem</strong>. The
 involved animals should be wild and free-ranging animals. The affected
 properties should be crops, livestock, and fishery catches. Notably, while
 terrestrial animals are often intentionally controlled, marine megafauna
 usually is entangled or hooked unintentionally when they feed on baits and
 captured fishes, leading to injuries or drowning and further damage to
 fishing gear (Wilson<em> et al.</em> 2014; Gilman<em> et
 al.</em> 2023). <strong>Nonlethal technical
 intervention</strong>. Interventions must be nonlethal technical
 measures and adjusting fishing or farming time were excluded.
 <strong>Comparisons</strong>. While the random set-up
 of a strict experiment is often impossible for case studies in this
 research field, we required the set-up of experimental comparison to be
 somehow controlled to reduce various confounding biases in the
 wild. <strong>Outcome</strong>. The effects
 of interventions on properties should be measured. The measurements
 include weight of crop yield, area or weight of crop damage, number of
 livestock lost, and weight or number of captured fish. We excluded
 experiments based on indirect behavioral indicators, such as the number of
 visitations, contact rate (e.g., contact rate between seabirds and gears
 near baited hooks), and respondents’ perceived effectiveness. For
 translocations, the change of losses of the pre-release site and the
 post-release site should be measured.
 <strong>Statistical test.</strong> Significance
 testing of the outcome between the treatments and controls should be
 reported or is possible to calculate from the reported data.
 In general, to include more types of interventions, we relaxed
 the inclusion criteria on case studies about the reported data compared to
 previous meta-analysis (van Eeden<em> et al.</em> 2018a;
 Huang<em> et al.</em> 2023). The meta-analysis approaches are
 often used to evaluate the overall impact of an intervention based on
 quantitative effect size (e.g., Hedge’s <em>d</em> and
 <em>g</em>) calculated from detailed data (e.g., the mean,
 standard deviation, and sample size) obtained from multiple case studies
 and may detect statistically significant effects of interventions, even
 when synthesizing insignificant case studies of low statistical power
 (Koricheva & Gurevitch 2013; Borenstein<em> et
 al.</em> 2021). However, case studies that do not report the
 required data for calculating the effect size were usually excluded, such
 as the studies for fyke net modification for pinniped (Königson<em>
 et al.</em> 2007) and umbrella-and-stones system for seabirds
 (Brown<em> et al.</em> 2010). Besides, many interventions were
 evaluated only by a single experiment (or one effect size), such as using
 decoy fields (Hagy, Linz & Bleier 2008), seed coat (Decker, Avery
 & Way 1990), and caffeine (Avery<em> et al.</em> 2005)
 for farmland birds, were often excluded from the meta-analysis
 (Huang<em> et al.</em> 2023). Meanwhile, we tightened the
 inclusion criteria to that of previous narrative reviews on the study
 designs and outcome measurement. With our different
 criteria from previous syntheses, we revisited the inclusion process of
 Huang et al. (2023) and Huang<em> et al.</em> (2024) and
 identified an additional 13 and 8 studies, respectively, which did not
 meet the previous inclusion criteria of the meta-analysis but met the
 criteria of this study. Therefore, there were 615 case studies in the
 final list and 605 of them are available online. The inclusion process of
 this study was documented in the supplementary material (Fig.
 S1). Data extraction and compilation
 For each case study, we extracted information on the types of
 interventions and their outcomes. An intervention was recognized mainly
 based on the combination of the target animal groups and device or gear
 its used. We classified intervention outcomes as either statistically
 effective, insignificant, or counter-productive according to the original
 case studies. Statistically effective means that an intervention showed
 statistical significance in reducing the loss or increasing the yield of
 properties; counter-productive means that an intervention showed
 statistical significance in increasing the loss or reducing the yield of
 properties; and statistically insignificant means that an intervention had
 no statistically significant impact on the properties but this does not
 necessarily mean the effect of an intervention is absent due to
 statistical powers (Borenstein<em> et al.</em> 2021). For
 studies that reported the effects of interventions on multiple livestock
 (e.g., sheep, goat, and cattle) or target fishes, we extracted the overall
 statistical test, if available. We treated the level of 0.05 as
 statistically significant. We compiled the data about
 the environment (i.e., the terrestrial or marine), animal groups, sensory
 stimuli, and geographical information (e.g., countries and major fishing
 areas). According to management and research practices, we classified the
 animals into terrestrial carnivores, elephants, farmland birds, seabirds,
 cetaceans, pinnipeds, and elasmobranchs. The sensory stimuli could be
 light (or visual, e.g., flashing light and movable predator models), sound
 (e.g., acoustic pingers), chemical (e.g., anthraquinone), electricity
 (e.g., electric wire), magnetic (e.g., permanent magnet), multi-sensory
 stimuli (multimodality, hereafter, e.g., alternative crops or baits), and
 physical approach (e.g., fortified fences and line weightings to avoid
 depredations), and combinations of the former stimuli (e.g., toriline as
 the light-physical and guardian animals as the multimodality-physical)
 (Fig. 1 and Table S2). We assigned each study location (if available) to
 the countries’ territory, Economic Exclusive Zone of a specific country,
 and the major fishing area as defined by the Food and Agriculture
 Organization. We classified feeding stations or decoy
 fields as physical approaches that lead wildlife away. We classified the
 bird perch and aerial-applied herbicide to habitat as two indirect
 interventions because they relied on the presence of aggressive birds and
 the removal of weeds at roost habitats. We only included statistically
 effective interventions when a study aims to examine the outcome of
 multiple similar interventions without making any assumptions about which
 one would work. Examples of such interventions included the colors of
 fruit-protective bags (Mutisya<em> et al.</em> 2016;
 Wen<em> et al.</em> 2016)
description: # Human-wildlife coexistence needs more evidence-based interventions to
 reduce the losses of crops, livestock, and fishery catches
 [https://doi.org/10.5061/dryad.wwpzgmsvb](https://doi.org/10.5061/dryad.wwpzgmsvb) ## Description of the data and file structure The dataset include three files: 1_List_of_included_literature.xlsx, 2_Experimental_information_of_interventions.xlsx, and 3_Analysis_code.html. ### 1\_List\_of\_included\_literature.xlsx  #### This table is a list of 615 papers initially included in this study.  Source: case studies were collected from six syntheses. StudyID: unique id of a case study. Title: title of a case study. PublishYear: year of a case study published. Accessible: whether a paper is accessible online. ### 2\_Experimental\_information\_of\_interventions.xlsx #### This table is the information of interventions and their outcome in reducing wildlife-related losses. StudyID: unique id of a case study. Country1: countries’ territory of study locations. Country2: countries’ territory of study locations. Country3: countries’ territory of study locations. FAO1:  the major marine area as defined by the Food and Agriculture Organization of study locations. FAO2: the major marine area as defined by the Food and Agriculture Organization of study locations. FAO3: the major marine area as defined by the Food and Agriculture Organization of study locations. Environment: terrestrial or marine. Taxa: biological classification of involved animals. Stimuli: type of behavior stimuli of an intervention. Intervention: type of a mitigation measure. Title: title of a case study. InterventionDetail: more detailed description or classification of a intervention. Outcome3: significantly reduce the loss, significantly increase the loss, and insignificant in reducing the loss. Outcome2: significantly reduce the loss or not. PublishYear: year of a case study published. Period: assign the PublishYear in ten-year period. ### 3\_Analysis\_code.html #### This file is a rmarkdown file of the whole analysis process in R environment.