Wildlife conservation on private land: a social-ecological systems study

Wildlife conservation on private land: a social-ecological systems study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Wildlife conservation on private land: a social-ecological systems study Matthew Taylor, Barry Brook, Christopher Johnson, Siobhan de Little This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3916808/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2024 Read the published version in Environmental Management → Version 1 posted 7 You are reading this latest preprint version Abstract As human activity accelerates the global crisis facing wildlife populations, private land conservation provides an example of wildlife management challenges in social-ecological systems. This study reports on the research phase of ‘WildTracker’ - a co-created citizen science project, involving 160 landholders across three Tasmanian regions. This was a transdisciplinary collaboration between an environmental organisation, university researchers, and local landholders. Focusing on mammal and bird species, the project integrated diverse data types and technologies: social surveys, quantitative ecology, motion sensor cameras, acoustic recorders, and advanced machine-learning analytics. An iterative analytical methodology encompassed Pearson and point-biserial correlation for interrelationships, Non-Metric Multidimensional Scaling (NMDS) for clustering, and Random Forest machine learning for variable importance and prediction. Taken together, these analyses revealed complex relationships between wildlife populations and a suite of ecological, socio-economic, and land management variables. Both site-scale habitat characteristics and landscape-scale vegetation patterns were useful predictors of mammal and bird activity, but these relationships were different for mammals and birds. Four focal mammal species showed variation in their response to ecological and land management drivers. Unexpectedly, threatened species, such as the eastern quoll ( Dasyurus viverinus) , favoured locations where habitat was substantially modified by human activities. The research provides actionable insights for landowners, and highlights the importance of ‘messy’, ecologically heterogeneous, mixed agricultural landscapes for wildlife conservation. The identification of thresholds in habitat fragmentation reinforced the importance of collaboration across private landscapes. Participatory research models such as WildTracker can complement efforts to address the wicked problem of wildlife conservation in the Anthropocene. Wildlife conservation social-ecological systems private land transdisciplinary research citizen science Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction Context and importance of wildlife conservation in the Anthropocene Globally, human land use, especially on private lands, has precipitated a major decline in biodiversity. Vertebrate populations declined by 68% between 1970 and 2018, and habitat conversion on private lands was a significant driver of these declines (Almond et al. 2022). A significant portion of Australia’s unique biodiversity is found on private lands. The continent's rich biodiversity, in which over 80% of its species being endemic, is both threatened and protected by actions on private properties (Fitzsimons 2015; Legge et al. 2023). Private lands play a crucial role in global biodiversity conservation (Knight 1999), where they harbor rich and unique ecosystems. Private land ownership was historically focused on productive parts of the landscape. As such, private properties today support especially abundant and diverse wildlife communities (Rayner et al. 2014; Jenkins et al. 2015; Clancy et al. 2020), including threatened species, and offer conservation opportunities distinct from public reserves (Ivanova and Cook 2020; Bingham et al. 2021). Private-land conservation faces unique challenges, such as aligning landowner interests with broader ecological goals and overcoming knowledge gaps. Wildlife populations do not conform to human-designated boundaries, making their management on private lands inherently challenging (Pulsford et al. 2013a). The distribution, movement, and life-history strategies of species necessitate conservation approaches that are spatially explicit, accounting for both the site-scale habitat requirements and landscape-level processes that require migration corridors, habitat connectivity, and ecological fluxes across multiple properties (Mackey et al. 2013). Private lands also offer unique opportunities for conservation. With appropriate management, these lands can serve as vital refuges and ‘stepping stones’ for wildlife between protected areas and reserves, mitigating some of the impacts of habitat fragmentation (Figgis 2004; Fitzsimons 2004; Kamal et al. 2015; Chapman et al. 2023). Conservation covenant and stewardship programs, alongside innovative approaches like citizen science and participatory action research, can complement public reserve systems and contribute to multi-tenure conservation networks (Pulsford et al. 2013b; Kamal et al. 2014; Taylor et al. 2023a). Private land conservation strategies engage landholders directly in conservation efforts, fostering knowledge sharing and collaborative management practices. This grassroots involvement is vital for effective stewardship of private lands, contributing significantly to global biodiversity conservation efforts. The social ecological systems framework Wildlife conservation on private lands is complex because it lies at the intersection of ecology, economics, and human values, making it a quintessential example of a "wicked problem" (Rittel and Webber 1973). Wicked problems are characterized by the lack of clear definitions, solutions, or objective measures of success, and they typically encompass various intertwined and often conflicting human and ecological dimensions. Social-ecological perspectives are vital in addressing such wicked problems, as they emphasize the interconnectedness of human and environmental systems (Mertens 2015; Akamani et al. 2016). This approach recognises that conservation outcomes are influenced not just by ecological factors, but also by social, economic, and cultural dynamics. On private lands, where decisions of individual landowner can have major effects on conservation efforts, gaining a better understanding of these interdependencies in the context of wildlife management is crucial. Adopting a social-ecological perspective allows for more holistic and effective strategies, as it integrates diverse stakeholder values, knowledge systems, and ecological processes, leading to more sustainable and community-supported conservation outcomes (Angelstam et al. 2013; Hummel et al. 2017; Hull et al. 2023). The application of this approach to wildlife management is a potential pathway to better understanding and addressing wicked problems that have to date largely defied resolution, despite significant research and management effort globally. Transdisciplinary research, which transcends disciplinary boundaries and incorporates knowledge from both scientific and non-scientific sources, is increasingly recognised as a valuable approach for social-ecological research (Axelsson 2012). By involving multiple stakeholders, including local landowners, ecologists, policymakers, and the broader community, transdisciplinary research fosters holistic understandings and collaborative strategies for effective conservation management (Marchini et al. 2021). Citizen science offers a powerful tool to bridge knowledge gaps, harnessing the collective power of the community in monitoring and understanding the environment (Bonney et al. 2009; Crain et al. 2014; Strasser et al. 2019). It enables researchers to gather data at scales previously unattainable, while participants benefit from enhanced environmental awareness and a sense of stewardship. Co-created knowledge can also be used to inform and thereby improve landholders’ management of their land (Toomey and Domroese 2013; Taylor et al. 2023b).More than just a data collection tool, citizen science fosters collaborations that can inform sustainable land-management practices and empower local communities to take active roles in conservation efforts, ultimately contributing to more robust environmental outcomes (Dickinson et al. 2010; Conrad and Hilchey 2011; Tulloch et al. 2013). Research gap and objectives of the study Although the role of private land in wildlife conservation has been repeatedly acknowledged (refs), comprehensive social-ecological studies that integrate socio-economic, ecological, and land management variables at various spatial scales are lacking. Tasmania is a large (68,000 km2) temperate island off the south coast of Australia. Tasmania's diverse range of ecosystems and species, including many that are threatened, makes it a microcosm for understanding broader global patterns in wildlife conservation on ecologically heterogeneous, human-dominated private landscapes. The region's diverse land uses and mix of private and public lands, coupled with active community involvement in land management, makes Tasmania a relevant case study for that resonates with global social-ecological research into wildlife management. In this study, we adopted a collaborative transdisciplinary approach, prioritising co-design with participants over traditional hypothesis development. Therefore, our preliminary research objectives were deliberately general to allow for input from landholders, practitioners, and researchers. The preliminary research objectives were as follows: Explore the relationships between wildlife populations and a variety of site and landscape variables on private lands. Employ a transdisciplinary approach, integrating ecological data with socio-economic and land management factors. Use appropriately sophisticated analytical methods to robustly discern patterns and drivers affecting wildlife on private properties. Offer practical insights for landowners and conservationists, aiming to promote collaborative, landscape-scale conservation efforts. This approach, detailed in our methodology section, was essential in fostering a participatory research environment. The objectives served as a guiding framework, setting the scope without constraining specific investigative paths. The co-design process culminated in the formulation of a series of interrelated hypotheses, embodied in models that reflect the dynamics of the social-ecological system. These hypotheses were subsequently tested through a comprehensive suite of social and ecological data-collection methods, ensuring a robust and inclusive exploration of the system's complexities. Focal wildlife groups and taxa This study concentrated on native mammals and bird diversity, with a detailed focus on four mammal species. The overarching objective was to compare the biotic responses to landscape and site-scale socio-ecological drivers, a phenomenon under-explored in existing literature. While previous comparative studies have indicated that mammals and birds are similarly impacted by intensive land use, they also show nuanced differences in their reactions to landscapes that are less-intensively modified (Burel et al. 1998; Felton et al. 2010; Santangeli et al. 2022). Moreover, mammals and birds, being charismatic and readily identifiable, are of particular community interest, and established survey procedures are suitable for deployment by citizen scientists. The selection of the four focal species (eastern barred bandicoot ( Perameles gunnii ), eastern bettong ( Bettongia gaimardi ), eastern quoll ( Dasyurus viverrinus ) and long-nosed potoroo ( Potorous tridactylus )) for detailed analysis of social-ecological drivers was guided by the outputs of the stakeholder workshop. The species selected are all in the ‘critical weight range’ of high extinction risk in Australia (Johnson and Isaac 2009), and are especially susceptible to predation by feral cats. The species were expected to show both commonalities and contrasts in their response to habitat condition at site and landscape scales, thereby providing valuable insights into the complex dynamics of habitat-species interactions and socio-ecological drivers. Methods Framework This research adopted a transdisciplinary methodology grounded in the principles of citizen science and participatory action research. Social-ecological systems are intricate, with intertwined human and natural components that influence one another in complex ways. In order to understand these systems a combination of quantitative and qualitative methods was used, drawing from both the natural and social sciences. The research process involved private landholders in five stages, including scoping, problem framing, data collection, data analysis, and feedback and reporting. Conservation practitioners and researchers were also involved during the scoping and problem framing stages of the research. The data collection and analysis procedures are illustrated in Fig. 1 . Stakeholder involvement in scoping and problem framing are essential elements of participatory research that ensure relevance by incorporating local ideas and knowledge systems into both research planning and implementation. The following methods section will focus on stages 3–5 of the research process, as the scoping and problem framing stages of the research are covered in detail in previously published papers (Taylor et al. 2023a , b ). A social-ecological systems conceptual model (Fig. 2 ) was developed through a stakeholder workshop. The model identified the focal social-ecological factors and guided the data-collection phase of the research, including selection of ecological, landscape-context, socio-economic and land-management variables. Study Location The research was undertaken in southeast Tasmania, Australia, an area characterised by rich biodiversity, productive agricultural lands, and dynamic human-nature interactions. Tasmania is a large maritime island off the south coast of Australia, with a cool temperate climate. Tasmania has been continuously occupied by Aboriginal people for at least 40,000 years (Jones et al. 2019 ). Following European colonisation, agriculture development has transformed much of the Tasmanian landscape. Tasmania has diverse wildlife communities, including species of mammals that remain reasonably common and widespread (with some localised declines) but are rare or extinct in their former ranges on mainland Australia. Large areas of land area under private ownership but with a high retained cover of native (variously modified) vegetation, and potentially high biodiversity value. Regional differences in agricultural practices and the extent of native vegetation clearance have influenced the distribution of native species and habitats. The mosaic of private land allotments, many small in extent, are subject to a wide variety of management styles that create a socio-ecologically heterogeneous landscape. The study focused on three distinct regions: the Huon Valley, Bruny Island, and the Derwent Valley (Fig. 3 ). These regions were selected in order to compare and contrast the influence of biogeographic and socio-economic characteristics on species distributions and habitat condition. Recruitment of Participants Private landholders with properties of at least 1 hectare in size were recruited via the networks of the Tasmanian Land Conservancy and through advertising on community noticeboards, traditional and social media. The project was titled ‘WildTracker’ and has since evolved from the pilot phase reported herein into an ongoing citizen-science program hosted by the Tasmanian Land Conservancy. A total of 160 landholders participated in the research. All landholders attended a training workshop, which outlined the research objectives and provided hands-on training in techniques for collecting data on mammals, birds and habitat, including the use of motion-sensor cameras, sound recorders, and photo-point monitoring. Some landholders also participated in other elements of the research, including through interviews, a problem-framing workshop, socio-economic and land management survey, and classification of wildlife images and habitat photos. Recruitment and participation in the research were in accordance with Human Ethics Permit H0016014, issued by the University of Tasmania. Sampling Strategy and Site Selection Landholders were provided with a property map that showed the distribution of broad vegetation or habitat types. They were instructed to establish a long-term monitoring site in one or more habitat types, depending on the size and ecological characteristics of the property. Sites were located at least 500m apart to mitigate spatial autocorrelation of the species distribution data. Landholders were provided with instructions on how to choose a suitable site for mammal and bird observations using the supplied equipment but were also encouraged to use knowledge of their property and local wildlife to guide the exact placement of the site. Further details of survey procedures for ecological indicators are provided in Table 1 . Field collection of ecological data Ecological data were collected by landholders on mammals, birds, and habitat condition. These site-scale indicators were selected because they are of high conservation significance, were identified as important natural values by landholders, and survey technologies and procedures are available that facilitate a citizen-science approach to both data collection and analysis. Equipment was provided by the Tasmanian Land Conservancy in four rounds, with a limited supply of equipment rotated between landholders over a 16-week period in the Tasmanian spring and summer. Landholders were provided with a field manual containing instructions on field survey techniques, and support could be accessed from the research team via phone or email. Field data were collected in accordance with Animal Ethics Permit A0015788, issued by the University of Tasmania. Table 1 Site indicators, survey method and procedures for collection of ecological data Site Indicator Survey method Procedure Mammals Landholder survey – wildlife camera Camera type: ScoutGuard SG560K Deployment period: 21 days Trigger setting: multi-shot (3 photos) Lapse period: 30 seconds Lure: Scent attractant Deployment location: on track (vehicle/walking/ animal) Birds Landholder survey – sound recorder Recorder type: Zoom H2N or smartphone Recording period: 20 minutes Recording time: +\- 60 minutes of dawn Recoding location: same as for wildlife camera Habitat condition Landholder survey – photo monitoring Camera type: personal digital camera or smartphone Photo orientation: north, south, east, west Landscape indicator calculation Spatial analyses were used to characterise the landscape context characteristics of each site, including vegetation cover, land productivity, and water availability. These indicators were identified in the stakeholder workshop as most important for determining distribution and abundance of native wildlife. Publicly available vegetation, agricultural and hydrological datasets were accessed from the Land Information Systems Tasmania database (Department of Natural Resources and Environment Tasmania 2023 ). Landscape indicators were analysed using geoprocessing functions in ArcGIS Pro software. Details of landscape indicators, source datasets and calculation procedures are detailed in Table 2 . Table 2 Landscape indicators, source datasets and calculation procedures Landscape Indicator Geospatial source data Procedure Native vegetation type (9 categories) TASVEG 4.0 – Vegetation Group Spatial join geoprocessing function Native vegetation extent (percentage) TASVEG 4.0 – Modified Vegetation Category Buffer, Clip and Calculate Geometry geoprocessing functions (100m, 250m, 500m, 1km, 2km, 5km) Riparian distance (metres) LIST Hydrology LIST Hydrography Near geoprocessing function Land productivity (7 categories) Land capability Spatial join geoprocessing function Socio-Economic and Land Management Survey Socio-economic and land management data were collected via a survey sent to the 160 landholders participating in the WildTracker program. The survey questions were based on factors identified as most important during the stakeholder workshop (Table 3 ). The survey was also sent to 1066 neighbouring landholders within 500 metres of a data collection site, because the workshop identified neighbouring land management as a potentially important driver of wildlife conservation outcomes. In total 454 responses to the survey were received (response rate 57%). Table 3 Landholder survey categories, sub-categories, and question types. Indicator Category Sub-category Question type Property information Property size Ownership time Proportion of income earned from property Residential status Natural resources Environmental values Area in hectares or acres Years of ownership 5 categories Resident/absentee 5 categories plus ‘other’ 6 categories plus ‘other Land management Property type Primary land manager Land management objectives Land management activities (current) Land management activities (historic) Land management time Land management expenditure Environmental / NRM program participation 5 categories plus ‘other’ 4 categories plus ‘other’ 6 categories plus ‘other’ 15 categories plus ‘other’ 15 categories plus ‘other’ 4 categories plus ‘other’ Dollars per month 6 categories plus ‘other’ Landholder information Age Gender Country of birth Weekly household income Education Occupation 8 categories Open ended response Open ended response 8 categories 5 categories plus ‘other’ Open ended response Environmental values New Ecological Paradigm Scale (Dunlap and Van Liere 1978 ) Sense of place 15 questions, Likert scale rating Open ended response Local community Community organisation participation Common interests Community relationships 9 categories plus ‘other 3 categories 3 categories Local ecological knowledge Sources of knowledge Trustworthiness of knowledge sources Types of knowledge 8 categories plus ‘other’ 7 categories, Likert scale rating 12 questions, Likert scale Preliminary Ecological Analysis Landholders collected raw data on mammals, birds, and habitat condition in the form of photos and sound recordings. Mammal and habitat images were classified by landholders and a team of volunteers, hosted by the Tasmanian Land Conservancy. Training sessions were provided by the research team and a guidebook was prepared to aid mammal identifications. Volunteer-classified images were validated by the research team through a hierarchical review process that focused on identifying unclassified images, then checking rare species, before finally checking a subset of commonly recorded species. In total volunteers classified 35,431 fauna detections from 160 camera deployments. An activity index for each species was calculated for each site as the proportion of days that the species was detected during a camera deployment. Total richness of mammals and feral animals was also calculated for each site and aggregated across deployments for properties with multiple survey sites. As a cross-validation procedure, the images were also classified using a recently developed deep-learning algorithm that had been trained on a dataset of over one million tagged images from parallel University of Tasmania fauna research projects (Brook et al. 2023 ). Cross validation of species detected at least 50 times showed a mean discrepancy between classification datasets of 11.7%. Rarer species were proportionally more likely to be misclassified by both human and AI recognisers, highlighting the importance of cross-validation measures in wildlife citizen-science projects. Sound recordings were reviewed by a skilled volunteer ornithologist and presence, or absence of bird species was recorded for each site. The sound recordings were validated by review of a proportion of recordings by a trained ornithologist. The acoustic dataset yielded 1165 observations from 84 sites. Habitat photos were reviewed by volunteers and were visually classified according to structural characteristics (density of three vegetation strata), and the proportion of native to introduced plants. classification system is shown in detail in Table 4 . Table 4 The classification system used to analyse photo-point images of habitat Vegetation structure Nativeness Strata Density Understorey Mid-storey Canopy Low density 70% Mostly exotic species ( 70% native) Property reports and feedback/feedforward workshops Property reports were compiled by Tasmanian Land Conservancy volunteers and provided to each landholder participating in WildTracker. The reports contained summary and descriptive statistics, such as habitat condition, the number of native species, the number of feral species, the relative abundance of each species, and expected species that were not detected. The expected species list was determined from species distribution maps for Tasmanian fauna (Rounsevell et al. 1991 ). A property map and management recommendations were also included in each report. Feedback/feedforward workshops were held in each participating region, in order to present the preliminary findings of the survey and identify future opportunities for improving the processes and areas of focus of the WildTracker program. Summary and descriptive statistics were calculated for each region in order to compare and contrast the findings of the fauna and habitat surveys at a regional scale. There were clear differences in the patterns of distribution and abundance of many species, provoking a discussion about potential causes, both natural and anthropogenic. The preliminary survey findings were also compiled into a report that was circulated to WildTracker participants (Taylor, 2017 ). Detailed Analysis of Ecological Components of the Social-Ecological System Detailed analysis of social-ecological datasets followed an iterative process, guided by the focal relationships identified as most important by the stakeholder-developed social-ecological system conceptual model. Analysis focused on the ecological and land management components of our social-ecological systems model. Social factors were considered as secondary drivers that effect primary drivers like vegetation cover and fragmentation. Calculations were done using the R statistical package (R Core Team 2023 ). Table 5 Description of analytical procedures for detailed social-ecological analysis Analysis Description Correlation plots To assess relationships, correlation plots were used to identify linear correlations in ecological data. We used the Pearson correlation coefficient to measure continuous dependent variables (e.g., species richness, species activity index) and independent variables (e.g., cat abundance, landscape characteristics, landowner survey data). For analysing relationships between continuous dependent variables and binomial categorical independent variables (e.g., region, land cover type, management practices), we used the point-biserial correlation method. Non-metric multidimensional scaling (NMDS) For cluster analysis, we did Non-Metric Multidimensional Scaling (NMDS) analysis using the metaMDS function from the vegan package in R. NMDS is a distance-based ordination method that transforms the data matrix into a dissimilarity matrix, forming the basis for ordination. For this analysis, dissimilarity matrices were calculated for native mammal and bird activity, with rows representing sites and columns representing species. The NMDS procedure is iterative, starting with defining original positions in multidimensional space and creating an initial configuration in reduced dimensions (usually 2D). The initial configuration is refined through regression against observed distances, with stress as a measure of disagreement. Stress levels guide the quality of dimensionality reduction, with lower values indicating better representation. Final NMDS plots were generated for both groups, with points coloured by various landscape, site, and landowner survey variables to identify potential groupings. Random Forest machine learning For prediction, we used Random Forests machine learning via the randomForest function from the caret package in R (Kuhn 2008 ). Random Forests, an ensemble of decision trees, employs recursive partitioning on subsets of training data and features. This approach introduces randomness, enhancing diversity and mitigating overfitting. In determining appropriate data splits, the trees contribute to an average prediction, generally yielding more accurate and robust results than individual decision trees. The analysis focused on identifying important variables in datasets including native mammal richness, native bird richness, and the abundance of both rare (eastern quoll, eastern bettong, eastern barred bandicoot, long nosed potoroo) and common species (Bennetts wallaby, bare-nosed wombat, grey currawong). For each independent tree, the dataset was divided into training (70%) and testing (30%) sets. Model tuning was via the tuneRF function to optimize the number of trees ( ntrees ) and the number of variables tried at each split ( mtry ). The fit of the models was assessed using variance explained, and variable importance plots along with partial dependency plots to examine the relationships between predictor (landscape, site, and landowner variables) and dependent variables (species richness or abundance). Given the limited number of sites contributing landowner variables, these were analysed separately from other landscape and site variables, ensuring a focused and relevant examination of their impact. Results The integration of diverse datasets including landholder-contributed data presented analytical challenges. Under the WildTracker participatory citizen-science model, the research collated extensive data from 160 properties and 285 sites. This approach, while robust in data collection, encountered gaps and inconsistencies across sites, a common issue in social-ecological and citizen science research (Dickinson et al. 2012 ; Guerrero et al. 2015 ). In this study, these issues were manifested in a high number of moderately significant predictor variables. This emphasised the need for more uniform data collection methods and highlighted the intricacies of balancing the depth and breadth of data in complex systems. Overcoming these difficulties are crucial to understanding the dynamics affecting mammal and bird assemblages in privately managed landscapes, and they underscore the trade-offs inherent in such comprehensive social-ecological research endeavours. Summary and descriptive statistics A total of 52 species were identified by the wildlife camera survey. This included 40 native species and 12 introduced species. Of the native species, 18 were mammals and 22 were birds. Of the introduced species, 9 were mammals and 3 were birds. Significant regional differences in the relative activity of native mammals (Fig. 4 ) and introduced mammals (Fig. 5 ). The most common native mammal species are generalist herbivores such as the Tasmanian pademelon ( Thylogale billardierii ), Bennetts wallaby ( Macropus rufogriseus ) and brushtail possum ( Trichosurus vulpecula ), which were frequently detected across all study regions. Threatened species such as the eastern quoll ( Dasyurus viverinus ), Tasmanian devil ( Sarcophilus harrisii ) and eastern barred bandicoot ( Perameles gunnii ) were less frequently detected. The Huon valley remains a relative stronghold for these species. Some species listed as least concern, such as the bare-nosed wombat ( Vombatus ursinus ), long-nosed potoroo ( Potorous tridactylus ) and southern brown bandicoot ( Isoodon obesulus ) were among the least frequently detected species. Note that Bruny Island has a naturally lower diversity of native mammal species: species such as the Tasmanian devil and spotted tailed quoll were absent from the island at the time of European colonisation. Data on both domestic and feral animals are presented here in order to show the relative abundance of these species in comparison to native fauna. Sheep and cattle are the most abundant domestic livestock species in southeast Tasmania. Sheep were most frequently detected on Bruny Island where there are extensive grazing properties on the northern part of the island. Cats were second most frequently detected introduced species, despite their typically low density and cryptic nature. They are detected at a similar frequency across all three study regions. Fallow deer were detected most frequently on Bruny Island and is noteworthy in that they are a recently established population following a documented escape into the wild. A total of 64 species of birds were identified by the acoustic survey. This included 54 native species, seven of which were Tasmanian endemic species, and ten introduced species. Three threatened species were identified: the swift parrot (Lathamus discolor) was identified at 15 sites, the blue-winged parrot ( Neophema chrysostoma ) was detected at five sites, and grey goshawk ( Accipiter novaehollandiae ) was identified at one site. The most frequently detected species was the forest raven ( Corvus tasmanicus ), which was detected at 92% of sites. The ten most frequently detected species were all native and were detected at > 50% of sites. The most frequently detected introduced species was the common blackbird ( Turdus merula ), which was detected at 49% of sites. Three introduced species of management concern were detected: the rainbow lorikeet, superb lyrebird and Eurasian starling. The rainbow lorikeet and superb lyrebird are native to the Australian mainland but were introduced to Tasmania. These species potentially impact native species via competition for nesting and foraging resources, and habitat alteration (lyrebird). Rainbow lorikeet and Eurasian starling Species richness per site was greatest in the Derwent Valley (Fig. 6 ), but the Huon Vally recorded the highest diversity of bird species (Fig. 7 ). Socio-Ecological Drivers of Wildlife Conservation – Land Management and Ecological Components Correlation plots of mammal and bird richness with site and landscape variables showed low to moderate correlations across a wide range of variables. The focal species showed intercorrelation amongst those species, and stronger correlations between them and site factors than landscape factors. Common species (Bennetts wallaby, common wombat, brushtail possum, grey currawong) all showed week relationships with predictor variables, indicating their ubiquity in the landscape (low habitat selectivity). Correlations were also observed between land management predictors and site and landscape scale vegetation predictors. These variables are the nexus between ecological and social components of the social-ecological system. Correlations between social-ecological predictor variables and mammal richness, bird richness, and focal species activity index are shown in Fig. 8 . Non-metric multidimensional scaling of mammal and bird observation data showed no obvious differentiation of fauna into distinctive communities across the study area when visualised against the majority of predictor variables. The only clear grouping separated the Bruny Island mammal assemblage from the other study regions (Fig. 9 ), which is to be expected given that it is an island with a naturally depauperate fauna. This community separation was not observed for birds, which are able to transit the narrow channel between the island and mainland Tasmania. Random forest (RF) analysis confirmed the importance of relationships between predictors and dependent variables identified through correlation plots. Dependency plots show non-linear relationships and evidence of ecological thresholds, especially for the native-vegetation extent landscape variables. The activity level of cats was found to predict both mammal richness and the activity of focal mammal species. The same relationship was also found for introduced animal richness. RF analysis also identified additional predictor variables of importance for native mammals relating to the extent of native vegetation within a radius of a monitoring site. Mammal richness declined when native vegetation cover within 1km of a site decreased beyond 80%, and when native vegetation within 100m of a site decreased beyond 50% (Fig. 11 ). Eastern barred bandicoot activity decreased sharply with distance from a stream or waterbody, and when native vegetation within 2km of a site decreased below 50% (Fig. 12 ). Eastern bettong activity increased with increasing shrub cover, and decreased when native vegetation within 1km of a site was below 70% (Fig. 13 ). Eastern quoll activity decreased substantially when native vegetation extent within 1km of a site was below 50% and increased with increasing shrub cover (Fig. 14 ). Long-nosed potoroo activity decreased when native vegetation within 100m of a site decreased below 50% and increased within an increasing proportion of native understorey vegetation (Fig. 15 ). The importance of predictor variables is covered comprehensively in the Supplementary Materials. Discussion Herein we have identified the key social-ecological drivers influencing wildlife populations and highlights the importance of ‘messy’, ecologically heterogeneous, human-dominated landscapes for wildlife conservation. The study's approach, centred on co-design and active stakeholder participation, led to a broad analysis of the social-ecological system, moving away from a conventional hypothesis-driven model. This expansive analysis identified several distinct and significant relationships within the system. These insights, emerging from a thorough and inclusive research process, provide key understandings into the complexities of wildlife conservation on private lands. The discussion below is structured according to the guiding conceptual model of social-ecological systems. It focuses on major findings related to land management, landscape-scale drivers, site-scale drivers, and invasive species affecting wildlife species richness and activity patterns. A common finding across theses social-ecological components, is that native wildlife can tolerate or thrive in the highly modified habitats that have been created by people in rural landscapes. Additionally, the important role of citizen scientists in wildlife monitoring and management is examined, and we advocate for a greater role for private landholders in landscape scale wildlife monitoring and management. Land management Land management serves as a critical nexus in the interaction among humans, wildlife, and habitats within our social-ecological model. Human activities have profoundly modified private landscapes creating ecologically heterogeneous environments. Conventional conservation theory would indicate that the impact of this modification on native wildlife would be deleterious. However, we found a mix of positive and negative associations with different categories of land use. Utilising survey data from landholders, our study investigated the dynamics of land management and its implications for wildlife conservation. We observed a complex impact of land management on wildlife populations, revealing interactions among property size, land use, and wildlife dynamics. Notably, not all examined factors were important to wildlife, and the relationships presented here are relatively weak. Our analysis categorises land management factors into primary, active factors like grazing, invasive species management, and restoration, and secondary factors including property type, time spent on land management, property size, and income from land. The most significant predictor of wildlife outcomes related to grazing, which correlated with negative outcomes for all fauna indicators. Grazing emerged as the most significant predictor of negative wildlife outcomes, correlating with larger properties and higher farm income. It impacts fauna populations by degrading understorey habitat quality and extent (Kirkpatrick et al. 2005) and is linked to broader landscape scale drivers such as native vegetation clearance and fragmentation (Eichenwald et al. 2020 ). Grazing management was associated with larger properties and greater farm income. Note that our study didn’t quantify grazing intensity, and research suggests that some grazing strategies are conducive to wildlife and habitat conservation (Leonard and Kirkpatrick 2004 ). In contrast, properties dedicated to conservation purposes, while associated with less time spent on land management, exhibited higher diversity of mammals and birds, and were positively correlated with our four focal mammal species. This emphasises the important role of private conservation lands in complementing and connecting public reserve systems and nature conservation initiatives (Ivanova and Cook 2020 ; Bingham et al. 2021 ). Interestingly, bird diversity was negatively correlated with the presence of active or historic native vegetation restoration on a property, an effect not observed in mammals. Restoration was negatively correlated with vegetation extent and site-scale habitat condition variables. Revegetation typically occurs in highly cleared and fragmented landscapes (Davidson et al. 2021 ), and there has been a historic tendency in restoration initiatives to plant primarily canopy tree species rather than diverse understorey that provides structural complexity (Lindenmayer et al. 2018 ; Jones et al. 2021 ). This can favour aggressive forest and woodland birds if there is an absence of suitable cover for smaller species such as honeyeaters (Munro et al. 2007 ; Bennett et al. 2022 ). The variation in responses between bird and mammal species underscores the need for targeted conservation strategies that address the unique needs of different faunal groups. The historical context of land management is also critical, as past practices may continue to influence present ecological conditions (Race et al. 2012 ). The participation of landholders in the research process as survey respondents and data collectors provides valuable insights but may introduce a self-selection bias, a factor that must be considered when interpreting these results (Pateman et al. 2021 ). Landholders with a disinterested or antagonistic attitude to wildlife are unlikely to have engaged in our conservation-centric project. This study highlights the diverse and sometimes counterintuitive effects of land management practices on wildlife conservation. The diversity of management approaches evident in private landscapes determines landscape-scale patterns in the extent, configuration, and condition of habitats for wildlife and is therefore a fundamental social-ecological driver of wildlife conservation outcomes. Landscape-scale social-ecological drivers: thresholds of habitat loss and fragmentation Agricultural and residential development have profoundly modified native ecosystems at landscapes scales (Mackey et al. 2013 ; Magioli et al. 2016 ). The most damaging impact has been the conversion and degradation of habitat (Legge et al. 2023 ). Despite these impacts, this study shows that wildlife is resilient and can persist in landscapes that have been significantly modified by human activities. A key finding of this research is the distinction between the impacts when viewed at the site versus landscape scales. While site-specific factors influence immediate habitats, landscape-scale considerations are pivotal in shaping broader ecological networks and corridors. These larger-scale factors significantly affect faunal movement, genetic diversity, and long-term species viability, emphasising the need for a strategic landscape-scale approach to wildlife conservation (Downes et al. 1997 ; Mackey et al. 2013 ; Davidson et al. 2021 ). One of the principal landscape-scale factors influencing mammal and bird diversity is the intactness of native vegetation within a radius of a site. Our findings confirm that both mammal and bird assemblages can tolerate a relatively high degree of fragmentation (Fischer and Lindenmayer 2006 ), with this tolerance extending from hundreds of meters to kilometres from a detected location. However, there are critical thresholds for native vegetation loss, beyond which species richness at the landscape level diminishes (Saunders et al. 1991 ; Fischer and Lindenmayer 2007 ). We found that at certain degree of native vegetation loss, a decline in species richness at a site becomes evident. This pattern was observed for the focal mammal species, with the distance and the percentage threshold of intact habitat varying between species. For instance, the activity of the long-nosed potoroo showed a sharp decline when native vegetation within 100 meters of a site dropped below 50%. This aligns with other studies indicating the species' preference for intact forest areas (Norton et al. 2010 ). Conversely, the activity of eastern quolls and eastern barred bettongs declined significantly at sites where there was a loss of more than 50% of native vegetation within a 2km radius. Species such as the eastern quoll and eastern barred bandicoot are more tolerant of landscape scale disturbance compared to the long-nosed potoroo. The contrast in conservation status, with both the quoll and bandicoot being threatened while the potoroo is not (although it is patchily distributed), underscores the importance of managing site-scale factors in conjunction with landscape-scale vegetation configuration. This finding supports findings from quantitative and expert elicitation analyses of dispersion in Australian species (Jones and Davidson 2016 ; Lechner et al. 2017 ). Such an understanding of species-specific thresholds can inform evidence-based landscape scale conservation planning, tailoring strategies to the unique ecological needs of each species (Noss 2008 ; Lechner et al. 2017 ; Proft et al. 2018 ; Gardiner et al. 2019 ) and thereby helping wildlife to persist in modified and heterogeneous private landscapes. Site scale socio-ecological drivers: the importance of productive, mixed-use lands Our study found that many wildlife species are able to persist and even thrive in highly modified habitats at the site scale. Some groups and taxa even displayed a preference for modified habitats, highlighting the importance of productive landscapes where mixed agricultural and residential land uses dominate. Mammals were more diverse in areas of high land productivity, regardless of the composition of the vegetation, and were positively correlated with the Modified Land vegetation category. The eastern quoll and eastern barred bandicoot (both threatened species) showed a preference for modified land and valley locations in proximity to streams or waterbodies. At face value this finding contradicts an established literature that consistently demonstrates that conversion of habitat leads to decline in native wildlife populations (Johnson et al. 2017 ; Almond et al. 2022 ; Legge et al. 2023 ). Productive landscapes are the focus of agricultural development and human settlement, which has resulted in the loss of significant proportion of native vegetation. However, there is a growing literature that recognises the values of mixed agricultural and peri-urban landscapes for faunal conservation (Burel et al. 1998 ; Dotta and Verdade 2011 ; Ehlers Smith et al. 2018 ; Semenchuk et al. 2022 ). Productive landscapes provide the highest and the most consistent supply of natural resources and historically supported the richest native fauna communities prior to extensive agricultural activities. Many native faunal species are not dependent on native plants for food and are able to coexist alongside people and agriculture, as long as the basic requirements of foraging resources and sheltering habitat are met (Burel et al. 1998 ; Rodewald 2003 ; Dertien and Baldwin 2022 ). Furthermore, introduced plants have been found to provide important habitat (e.g., food, shelter, cover from predation) in the absence of native alternatives (Marris 2013 ; Ranyard et al. 2018 ). This finding supports more nuanced approach to conservation that decouples the conservation of fauna from the conservation of native habitats, in favour of a focus on managing specific pressures that threaten native fauna in human landscape on private land. Contrasting the pattern observed in mammals, two of our focal species, the eastern bettong, and the long-nosed potoroo, exhibited distinct preferences for native habitat. The eastern bettong favoured intact native vegetation with substantial ground cover, while the long-nosed potoroo's site-scale habitat preferences were less specific, though it did show a preference for areas with ground cover and a closed canopy. Avian diversity also showed a stronger correlation with mixed and undisturbed remnant vegetation, being highest in sites with intact native understorey, and negatively correlated with sites with primarily introduced vegetation. Bird assemblages were more diverse in locations with a higher density of native shrubs. Diverse native habitats provide a greater variety of foraging niches and shelter from larger aggressive birds such as forest raven and noisy minor (Catterall et al. 1997 ; Lindenmayer et al. 2018 ; Bennett et al. 2022 ; Hingee et al. 2022 ), and our research confirms pervious research on the importance of native understorey habitat for these species. These findings underscore the necessity of a multifaceted approach to fauna conservation and management, one that is attuned to the diverse needs of local species. Effective management hinges on robust monitoring data; without knowledge of the species present on a property or in a specific area, it becomes challenging to devise land management strategies that cater to all species. A ‘diversified strategy’ in conservation management is likely to yield the most resilient outcomes. Site-scale socio-ecological drivers: The feral cat is a mid-sized invasive predator that is widespread in Australia. Cats prey on a wide range of taxa, from small rodents, reptiles, and amphibians to small and mid-sized marsupials (Doherty et al. 2017 ). They are recognised as significant invasive species both globally and especially on offshore islands (Medina et al. 2011 ; Dickman et al. 2019 ; Legge et al. 2020 ). A notable finding of our study was that feral cats were more prevalent in modified landscapes, in areas of higher land productivity and sites with the greatest diversity and activity of native mammals and birds. This aligns with findings from other researchers, such as Hamer et al ( 2021 ) who observed that both cats and the native spotted-tailed quoll were abundant in high-productivity areas, which support plentiful prey populations. Notably, mammal and bird richness, as well as the activity of all four focal mammal species, showed positive correlations with high cat activity in our study. This finding seems counterintuitive, because numerous studies have documented the adverse impact of cats on fauna, including their capacity to cause local extinctions and their role in the extinction of many of Australia's 34 mammal species since European colonisation (Legge et al. 2020 ). However, our study also found that the activity of the four focal species was positively correlated with the presence of medium to high-density ground layer vegetation, while being negatively correlated with areas lacking dense ground vegetation, and that bird diversity was similarly linked to the presence of an intact native shrub layer and medium-density ground vegetation. These findings underscore the significance of sheltering habitats in protecting 'critical weight range' mammals and other native fauna from predation, supporting the emerging perspective that complex understory habitats enable small to mid-sized Australian mammals to coexist with high densities of feral cats (Cunningham et al. 2019 ; Radford et al. 2021 ). Given the high cost and logistical challenges associated with feral cat control, managing land to maintain understory vegetation emerges as a pragmatic and implementable strategy for conserving native wildlife in Australia and beyond (Lazenby et al. 2021 ). In modified Tasmanian private landscapes, exotic plants species such as gorse ( Ulex europaeus ) may have an important ecological role to play in wildlife conservation (Ranyard et al. 2018 ), further supporting our key finding that ‘messy’ heterogeneous landscapes and properties are important for many wildlife species. A role for citizen scientists in wildlife monitoring and threatened species assessment? The WildTracker research collaboration was primarily aimed at enhancing the capacity of landholders in wildlife management on their properties, fostering a network that includes landholders, researchers, and conservation practitioners. This approach not only aimed at co-designing locally relevant data gathering tools but also at enabling landholders to address specific, meaningful questions related to local wildlife management issues. The significant number of observations of threatened species by WildTracker participants, corresponded to strong community interest in those species, and demonstrates the potential benefits of utilising citizen science in both social-ecological research and wildlife monitoring. Despite presenting significant logistical, training and data-integrity challenges, the unrealised potential of citizen science in biodiversity research, particularly in filling the data gaps that hinder effective conservation efforts has been emphasised by a substantive literature (Locke et al. 2019 ; Dissanayake et al. 2019 ; Fischer et al. 2021 ). Our findings corroborate this, showing that citizen scientists were adept at identifying both threatened mammal and bird species across various locations, contributing essential data that might otherwise be unavailable. A total of four threatened mammals and three threatened bird species were identified across numerous locations in all three regions. The data presented in this paper is from one year of data, but ongoing participation in WildTracker is now starting to yield information on trends in wildlife populations. This approach, especially in regions like Tasmania where ecological monitoring is under-resourced, presents a promising avenue for enhancing biodiversity tracking. Many jurisdictions, including Tasmania, suffer from a lack of investment in ecological monitoring. The need for additional resourcing of monitoring is also highlighted by the finding that many species categorised as least concern were found in far lower frequency than some endangered species. There is a case for assessment of least-concern species such as the southern brown bandicoot, a species considered common and widespread, but which was only identified at a small proportion of sites by this study. Although potentially still locally common on intact public lands, our data suggests a significant decline on private lands. The listing of the eastern quoll as endangered further illustrates this point: this iconic species jumped from least-concern to endangered, only because of a targeted research project that documented a significant decline of the species (Fancourt et al. 2013 ; Fancourt 2016 ). This underscores the importance of continuous and comprehensive monitoring strategies, a task where citizen science can play a pivotal role (Mckinley et al. 2017 ). Lack of data is a serious impediment to effective conservation because the prioritisation of environmental investments and policy are often based on the listing status of species. The role of citizen scientists in identifying the range and trajectory of threatened and more common species can help fill this gap, especially in private landscapes of which they are the custodians. Conclusion Our study demonstrates the importance of private lands for wildlife conservation, particularly productive environments where there a mix of land uses including agriculture and human settlement. These landscapes are characterised by their ‘messiness’, ecologically heterogeneous at both the site and landscape scale. The diversity of land management across these areas creates a complex mosaic that supports a high diversity of wildlife and offers more stable resources such as food and water. This underscores the need for a new model that recognises the value of modified landscapes in wildlife conservation, akin to the concept of 'rambunctious gardens' (Marris 2013), where ecologically varied agricultural and peri-urban areas serve as sanctuaries for both people and wildlife. Our research shows how the interaction between ecological, socio-economic, and land management factors shape wildlife conservation on private landscapes in Tasmania and has relevance to global wildlife conservation efforts. Understanding these dynamics is crucial for developing effective conservation strategies that encompass all aspects of socio-ecological systems, including mammals, birds, their habitats, and private landholder (Hull et al. 2023). Innovative solutions that acknowledge the importance of modified and novel ecosystems are critical to reversing the decline of native wildlife populations on private lands. Participatory initiatives such as WildTracker can play a significant role, empowering local communities with ecological data, knowledge, and management tools. By integrating insights from WildTracker workshops and interviews, we generated specific hypotheses, validated through analytical methods. This blended empirical data with local observations, enhancing our understanding of socio-ecological dynamics. It identified relationships between fauna assemblages and land management practices at both site and landscape scales, emphasising the need to consider local and broader ecological processes in conservation strategies. The significance of spatial scale in wildlife conservation on private lands cannot be overstated. While individual property-level habitat management is important, it often falls short for wide-ranging species that require broader landscape-level conservation actions (Mackey et al. 2013; Lindenmayer et al. 2016). Consequently, successful conservation strategies on private lands require a synergistic approach: one that not only caters to specific local habitat requirements but also promotes collaborative initiatives at the landscape level among property owners. Employing a socio-ecological methodology, which integrates diverse disciplinary perspectives and stakeholder insights, ensures that conservation practices are both contextually appropriate at the local level and practical to implement, thereby addressing the varied requirements of wildlife species across private landscapes. Declarations Acknowledgements The research team wishes to acknowledge the invaluable contribution of our research partner the Tasmanian Land Conservancy, which provided major financial, logistical, and technical assistance with this project. Dr Michael Lockwood was the initial supervisor of this research project and contributed substantially to research design. Associate Professor Aidan Davison and Dr Andrew Harwood are part of the research team and have contributed substantial social-science knowledge to the design of the project. We would also like to thank the many participating landholders who contributed their time, ideas and enthusiasm that made this research possible. Finally, we’d like to thank NRM South and the Land for Wildlife Program, which provided assistance with recruitment of participants. Acknowledgement of Country We acknowledge the Tasmanian Aboriginal people, as custodians of the land where this project was undertaken and pay respect to Elders, past, present and emerging. Lutruwita (Tasmania) has been home to the palawa people for thousands of years, and there is much that western ecological science can learn from their traditional knowledge about living in balance with nature. Aboriginal and Torres Strait Islander sovereignty was never ceded, this was and always will be Aboriginal land. Competing interests and funding The authors declare no competing interests in the conduct of this research. This paper has not been published nor submitted for publication elsewhere. The authors received research funding and financial support from the Tasmanian Land Conservancy (MT) and the University of Tasmania (MT, BB, CJ). Ethics and Consent This project has received human and animal ethics permits from the University of Tasmania’s Human Research and Animal Ethics Research Committees (refs. H0016014 and A0015788). All research participants have provided written consent to participate and for their data to be used for this publication. Authorship statement All authors whose names appear on the submission: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; drafted the work or revised it critically for important intellectual content; approved the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Data Availability De-identified interview data, survey data and quantitative ecological data may be made available upon request. Author Contribution All authors whose names appear on the submission:1. made substantial contributions to the conception or design of the work (MT, BB, CJ); or the acquisition (MT), analysis (MT, SDL), or interpretation of data (MT);2. drafted the work (MT) or revised it critically for important intellectual content (BB, CJ, SDL);3. approved the version to be published (ALL); and4. agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved (ALL). References Akamani K, Holzmueller EJ, Groninger JW (2016) Managing wicked environmental problems as complex social-ecological systems: the promise of adaptive governance. In: Melesse A, Wossenu A (eds) Landscape dynamics, soils and hydrological processes in varied climates. 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Front Ecol Evol 9:. https://doi.org/10.3389/fevo.2021.739817 Ranyard CE, Kirkpatrick JB, Johnson CN, et al (2018) An exotic woody weed in a pastoral landscape provides habitat for many native species, but has no apparent threatened species conservation significance. Ecological Management and Restoration 19:212–221. https://doi.org/10.1111/emr.12338 Rayner L, Lindenmayer DB, Wood JT, et al (2014) Are protected areas maintaining bird diversity? Ecography 37:43–53. https://doi.org/10.1111/j.1600-0587.2013.00388.x Rittel HWJ, Webber MM (1973) Dilemmas in a General Theory of Planning. Policy Sci 4:161 Rodewald AD (2003) The Importance of Land Uses within the Landscape Matrix. Wildl Soc Bull 31:586–592 Rounsevell D, Taylor R, Hocking G (1991) Distribution Records of Native Terrestrial Mammals in Tasmania. Wildlife Research 18:699–717 Santangeli A, Mammola S, Lehikoinen A, et al (2022) The effects of protected areas on the ecological niches of birds and mammals. Sci Rep 12:. https://doi.org/10.1038/s41598-022-15949-2 Saunders DA, Hobbs RJ, Margules CR (1991) Biological consequences of ecosystem fragmentation: A review. Biol Conserv 5:18–32. https://doi.org/10.1016/0006-3207(92)90725-3 Semenchuk P, Plutzar C, Kastner T, et al (2022) Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity. Nat Commun 13:. https://doi.org/10.1038/s41467-022-28245-4 Strasser BJ, Baudry J, Mahr D, et al (2019) Citizen Science? Rethinking Science and Public Participation. Science & Technology Studies 32:52–76 Taylor M (2017) WildTracker: trialling a community based wildlife monitoring initiative in southeast Tasmania. Hobart Taylor M, Davison A, Harwood A (2023a) Local Ecological Learning: Creating Place-based Knowledge through Collaborative Wildlife Research on Private Lands. Environ Manage. https://doi.org/10.1007/s00267-023-01907-9 Taylor M, Davison A, Harwood A (2023b) Bridging Knowledge Creation and Conservation Practice through Participatory Action Research on Private Lands. Citiz Sci 8:6. https://doi.org/10.5334/cstp.428 Toomey AH, Domroese MC (2013) Can citizen science lead to positive conservation attitudes and behaviors? Human Ecology Review 20:50–62 Tulloch A, Possingham HP, Joseph LN, et al (2013) Realising the full potential of citizen science monitoring programs. Biol Conserv 165:128–138. https://doi.org/10.1016/j.biocon.2013.05.025 Additional Declarations No competing interests reported. Supplementary Files WildlifeConservationonPrivateLandSupplementaryMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 23 Mar, 2024 Read the published version in Environmental Management → Version 1 posted Editorial decision: Revision requested 25 Feb, 2024 Reviews received at journal 23 Feb, 2024 Reviewers agreed at journal 02 Feb, 2024 Reviewers invited by journal 01 Feb, 2024 Editor assigned by journal 01 Feb, 2024 Submission checks completed at journal 01 Feb, 2024 First submitted to journal 01 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3916808","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270877619,"identity":"bd1ac6c4-b194-4bda-9d35-811210a1c973","order_by":0,"name":"Matthew Taylor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3RMQrCMBSA4VcCcXnSNUXxBkJAEMHBqwgOLhU6iYMUQaiL9iy6OEcCurTOHXVxcnAqOCgmom7WugnmH0IS8pFAAEym34zroQxAxGOjnYOoswhA2yC+JMjzkeqIzMlpKNEuRWnpNPTBLrgczsF7UhfUA7GW6IS9JVMTcKZHbs0yiX4PlcijoiJUAE/ULcWP5KoJHpi4+tBSxLp8IqvgTihbBQQ4cznJvEVST8RhF50prTXiUCKLDp4sbzPIZrzYDdJmxUayTwapX7EnncX+2H9PgID+jfFrjXoQGeCZn+OMyWQy/W03kSBP6m7H2F0AAAAASUVORK5CYII=","orcid":"","institution":"University of Tasmania","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Taylor","suffix":""},{"id":270877620,"identity":"e817ef82-7068-42de-a8b2-b1b48d5277b3","order_by":1,"name":"Barry Brook","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Barry","middleName":"","lastName":"Brook","suffix":""},{"id":270877621,"identity":"ddf425b0-ae37-4875-ac8f-e0f13254fc81","order_by":2,"name":"Christopher Johnson","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Johnson","suffix":""},{"id":270877622,"identity":"ae5e7cbf-6b5d-4535-a73c-aaab0f813e04","order_by":3,"name":"Siobhan de Little","email":"","orcid":"","institution":"Ecotec Environmental","correspondingAuthor":false,"prefix":"","firstName":"Siobhan","middleName":"","lastName":"de Little","suffix":""}],"badges":[],"createdAt":"2024-02-01 09:17:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3916808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3916808/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00267-024-01962-w","type":"published","date":"2024-03-23T15:01:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50655383,"identity":"e3038469-9418-44b6-838f-7476f6b00545","added_by":"auto","created_at":"2024-02-05 10:09:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99522,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the five-stage social-ecological research methodology. Blue shaded elements indicate landholder participation. Note: the original intention was to present final research findings to landholders at feedback workshops to promote discussion. However, logistical complications delayed final analyses and findings were presented to landholders via correspondence after the workshops.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/72ae19157149a230c57e8479.png"},{"id":50655381,"identity":"ae461382-6344-4633-ac58-8fc82933b305","added_by":"auto","created_at":"2024-02-05 10:09:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81240,"visible":true,"origin":"","legend":"\u003cp\u003eSocial-ecological systems model of wildlife conservation on private lands. Developed by landholders, conservation practitioners and researchers, this model guided the data collection and analysis processes. Note that fire and climate drivers were not incorporated into data aggregation or analysis. The co-design workshop process is described in detail in Taylor et al (2023b).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/a020e3a32686c7e544e55f9b.png"},{"id":50655380,"identity":"9e38c6fd-01d2-4700-b532-a7cf104236fc","added_by":"auto","created_at":"2024-02-05 10:09:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141924,"visible":true,"origin":"","legend":"\u003cp\u003eStudy regions in southeast Tasmania and approximate location of the properties of participating\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/5657b24efcd4688c7faa8aab.png"},{"id":50655379,"identity":"2b04fa11-b134-4e29-8fb7-cae4d19d9198","added_by":"auto","created_at":"2024-02-05 10:09:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98581,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of detection per region of eleven target native marsupial species, ranked from most frequently detected species to least frequently detected species.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/53cef6c83a954ad4c7b713f3.png"},{"id":50655391,"identity":"fc30eb9c-3082-43b1-8a06-bb0ac04e7b49","added_by":"auto","created_at":"2024-02-05 10:09:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94904,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of detection per region of introduced mammal species, ranked from most frequently detected species to least frequently detected species.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/e5a684142a1d05d4e91248fb.png"},{"id":50656247,"identity":"f5700fe3-17f1-42b1-a430-3ce4c303a3c3","added_by":"auto","created_at":"2024-02-05 10:17:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":43222,"visible":true,"origin":"","legend":"\u003cp\u003eMean number of bird species per site by region with Standard Errors. This bar chart shows the average number of bird species observed per site in each region. Error bars indicate the standard error of the mean, providing a measure of the variability in the data across different sites within each region.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/2581ded94c4a092005bc1e7f.png"},{"id":50656596,"identity":"ee6f12dc-a815-4e0b-b42a-2dee34e65f65","added_by":"auto","created_at":"2024-02-05 10:25:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":82791,"visible":true,"origin":"","legend":"\u003cp\u003eTotal number of bird species recorded by each region. The bars represent the cumulative count of different species observed.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/84cdc5ac62e477f5554b1709.png"},{"id":50656244,"identity":"9e2a3c59-7e20-4808-ae62-fc59ea6dc01b","added_by":"auto","created_at":"2024-02-05 10:17:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":237075,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of correlation plot results for mammal richness, bird richness, focal species against a subset of predictor variables for which a moderate (0.2) to strong (1.0) correlation was observed. Correlations between land management and vegetation predictors are also shown. Blue marks indicate a positive correlation and red marks indicate a negative correlation.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/a93c55c8855777f4b76816dc.png"},{"id":50656243,"identity":"f357b25b-b382-4ead-89bd-43cfa061100a","added_by":"auto","created_at":"2024-02-05 10:17:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":57539,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS plot of native mammals with sites colour coded by region. Bruny Island shows a moderate differentiation from the other two study regions, although many of its sites were not separable.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/3bb12a7c6f92109343761870.png"},{"id":50655394,"identity":"f45c205d-8dd1-4f73-a773-004f96e164ee","added_by":"auto","created_at":"2024-02-05 10:09:50","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":51248,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS plot of native birds with sites colour coded by region. Regions show no obvious differentiation.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/6fd36be5ed4a2ab7a4794d45.png"},{"id":50655388,"identity":"c0f4c0ff-2258-4e26-9862-767189957d1a","added_by":"auto","created_at":"2024-02-05 10:09:50","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":48657,"visible":true,"origin":"","legend":"\u003cp\u003eMammal richness dependency plots for the three most significant social-ecological predictor variables from RF analyses: cat activity (cat), native vegetation extent within 1km of a site (native_1km), and native vegetation extent within 100m of a site (natv_100m).\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/d62d6b19a4495cfee574aec3.png"},{"id":50656250,"identity":"cfc6154b-d971-4a2c-ac35-8b5278bc63b7","added_by":"auto","created_at":"2024-02-05 10:17:50","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":47407,"visible":true,"origin":"","legend":"\u003cp\u003eEastern barred bandicoot dependency plots for the three most significant social-ecological predictor variable from RF analyses: cat activity (cat), distance from stream or waterway (riparian), and native vegetation extent within 2km of a site (nativ_2km).\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/bfea0e0aaaab81031e7b6e62.png"},{"id":50656597,"identity":"bba05302-70d2-4ed1-b74f-11a983ff4a09","added_by":"auto","created_at":"2024-02-05 10:25:50","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":43997,"visible":true,"origin":"","legend":"\u003cp\u003eEastern bettong dependency plots for the three most significant social-ecological predictor variable from RF analyses: cat activity (cat), shrub cover (Shrbs.mdm) and native vegetation extent within a 1km of a site (nativ_1km).\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/c3f1f67e4a584e9ee66b40ca.png"},{"id":50656246,"identity":"d6df1d36-6cf8-41d0-8303-ea11724adee4","added_by":"auto","created_at":"2024-02-05 10:17:50","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":49524,"visible":true,"origin":"","legend":"\u003cp\u003eEastern quoll dependency plots for the three most significant social-ecological predictor variable from RF analyses: cat activity (cat), native vegetation extent within 2km of a site (nativ_2km), and shrub density (Shrbs.mdm)\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/1bb381e194b82b0bfda95a69.png"},{"id":50655392,"identity":"39fe4875-9517-4516-a54b-2fbef45f097f","added_by":"auto","created_at":"2024-02-05 10:09:50","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":48458,"visible":true,"origin":"","legend":"\u003cp\u003eLong-nosed potoroo dependency plots for the three most significant social-ecological predictor variable from RF analyses: cat activity (cat), native vegetation extent within 100m of a site (natv_100m), and proportion of native vegetation at a site (Nativ.ntv).\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/3790ea2843f407445f81d984.png"},{"id":53404046,"identity":"42046db5-caff-4f6c-b944-6e0019352a96","added_by":"auto","created_at":"2024-03-25 15:16:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1658813,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/ef63f456-d632-4c2f-a665-c833ea39ee89.pdf"},{"id":50656249,"identity":"2c40db48-7987-423a-b386-124ae324364f","added_by":"auto","created_at":"2024-02-05 10:17:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":294721,"visible":true,"origin":"","legend":"","description":"","filename":"WildlifeConservationonPrivateLandSupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3916808/v1/2bcfbdaf441f856225d5042a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wildlife conservation on private land: a social-ecological systems study","fulltext":[{"header":"Introduction","content":"\u003ch3\u003eContext and importance of wildlife conservation in the Anthropocene\u003c/h3\u003e\n\u003cp\u003eGlobally, human land use, especially on private lands, has precipitated a major decline in biodiversity. Vertebrate populations declined by 68% between 1970 and 2018, and habitat conversion on private lands was a significant driver of these declines (Almond et al. 2022). A significant portion of Australia’s unique biodiversity is found on private lands. The continent's rich biodiversity, in which over 80% of its species being endemic, is both threatened and protected by actions on private properties (Fitzsimons 2015; Legge et al. 2023). Private lands play a crucial role in global biodiversity conservation (Knight 1999), where they harbor rich and unique ecosystems. Private land ownership was historically focused on productive parts of the landscape. As such, private properties today support especially abundant and diverse wildlife communities (Rayner et al. 2014; Jenkins et al. 2015; Clancy et al. 2020), including threatened species, and offer conservation opportunities distinct from public reserves\u0026nbsp;(Ivanova and Cook 2020; Bingham et al. 2021).\u003c/p\u003e\n\u003cp\u003ePrivate-land conservation faces unique challenges, such as aligning landowner interests with broader ecological goals and overcoming knowledge gaps. Wildlife populations do not conform to human-designated boundaries, making their management on private lands inherently challenging (Pulsford et al. 2013a). The distribution, movement, and life-history strategies of species necessitate conservation approaches that are spatially explicit, accounting for both the site-scale habitat requirements and landscape-level processes that require migration corridors, habitat connectivity, and ecological fluxes across multiple properties (Mackey et al. 2013). Private lands also offer unique opportunities for conservation. With appropriate management, these lands can serve as vital refuges and ‘stepping stones’ for wildlife between protected areas and reserves, mitigating some of the impacts of habitat fragmentation (Figgis 2004; Fitzsimons 2004; Kamal et al. 2015; Chapman et al. 2023). Conservation covenant and stewardship programs, alongside innovative approaches like citizen science and participatory action research, can complement public reserve systems and contribute to multi-tenure conservation networks (Pulsford et al. 2013b; Kamal et al. 2014; Taylor et al. 2023a). Private land conservation strategies engage landholders directly in conservation efforts, fostering knowledge sharing and collaborative management practices. This grassroots involvement is vital for effective stewardship of private lands, contributing significantly to global biodiversity conservation efforts.\u003c/p\u003e\n\u003ch3\u003eThe social ecological systems framework\u003c/h3\u003e\n\u003cp\u003eWildlife conservation on private lands is complex because it lies at the intersection of ecology, economics, and human values, making it a quintessential example of a \"wicked problem\"\u0026nbsp;(Rittel and Webber 1973). Wicked problems are characterized by the lack of clear definitions, solutions, or objective measures of success, and they typically encompass various intertwined and often conflicting human and ecological dimensions. Social-ecological perspectives are vital in addressing such wicked problems, as they emphasize the interconnectedness of human and environmental systems (Mertens 2015; Akamani et al. 2016). This approach recognises that conservation outcomes are influenced not just by ecological factors, but also by social, economic, and cultural dynamics. On private lands, where decisions of individual landowner can have major effects on conservation efforts, gaining a better understanding of these interdependencies in the context of wildlife management is crucial. Adopting a social-ecological perspective allows for more holistic and effective strategies, as it integrates diverse stakeholder values, knowledge systems, and ecological processes, leading to more sustainable and community-supported conservation outcomes (Angelstam et al. 2013; Hummel et al. 2017; Hull et al. 2023). The application of this approach to wildlife management is a potential pathway to better understanding and addressing wicked problems that have to date largely defied resolution, despite significant research and management effort globally.\u003c/p\u003e\n\u003cp\u003eTransdisciplinary research, which transcends disciplinary boundaries and incorporates knowledge from both scientific and non-scientific sources, is increasingly recognised as a valuable approach for social-ecological research (Axelsson 2012). By involving multiple stakeholders, including local landowners, ecologists, policymakers, and the broader community, transdisciplinary research fosters holistic understandings and collaborative strategies for effective conservation management (Marchini et al. 2021). Citizen science offers a powerful tool to bridge knowledge gaps, harnessing the collective power of the community in monitoring and understanding the environment (Bonney et al. 2009; Crain et al. 2014; Strasser et al. 2019). It enables researchers to gather data at scales previously unattainable, while participants benefit from enhanced environmental awareness and a sense of stewardship. Co-created knowledge can also be used to inform and thereby improve landholders’ management of their land\u0026nbsp;(Toomey and Domroese 2013; Taylor et al. 2023b).More than just a data collection tool, citizen science fosters collaborations that can inform sustainable land-management practices and empower local communities to take active roles in conservation efforts, ultimately contributing to more robust environmental outcomes\u0026nbsp;(Dickinson et al. 2010; Conrad and Hilchey 2011; Tulloch et al. 2013).\u003c/p\u003e\n\u003ch3\u003eResearch gap and objectives of the study\u003c/h3\u003e\n\u003cp\u003eAlthough the role of private land in wildlife conservation has been repeatedly acknowledged (refs), comprehensive social-ecological studies that integrate socio-economic, ecological, and land management variables at various spatial scales are lacking. Tasmania is a large (68,000 km2) temperate island off the south coast of Australia. Tasmania's diverse range of ecosystems and species, including many that are threatened, makes it a microcosm for understanding broader global patterns in wildlife conservation on ecologically heterogeneous, human-dominated private landscapes. The region's diverse land uses and mix of private and public lands, coupled with active community involvement in land management, makes Tasmania a relevant case study for that resonates with global social-ecological research into wildlife management.\u003c/p\u003e\n\u003cp\u003eIn this study, we adopted a collaborative transdisciplinary approach, prioritising co-design with participants over traditional hypothesis development. Therefore, our preliminary research objectives were deliberately general to allow for input from landholders, practitioners, and researchers. The preliminary research objectives were as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eExplore the relationships between wildlife populations and a variety of site and landscape variables on private lands.\u003c/li\u003e\n \u003cli\u003eEmploy a transdisciplinary approach, integrating ecological data with socio-economic and land management factors.\u003c/li\u003e\n \u003cli\u003eUse appropriately sophisticated analytical methods to robustly discern patterns and drivers affecting wildlife on private properties.\u003c/li\u003e\n \u003cli\u003eOffer practical insights for landowners and conservationists, aiming to promote collaborative, landscape-scale conservation efforts.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis approach, detailed in our methodology section, was essential in fostering a participatory research environment. The objectives served as a guiding framework, setting the scope without constraining specific investigative paths. The co-design process culminated in the formulation of a series of interrelated hypotheses, embodied in models that reflect the dynamics of the social-ecological system. These hypotheses were subsequently tested through a comprehensive suite of social and ecological data-collection methods, ensuring a robust and inclusive exploration of the system's complexities.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFocal wildlife groups and taxa\u003c/h3\u003e\n\u003cp\u003eThis study concentrated on native mammals and bird diversity, with a detailed focus on four mammal species. The overarching objective was to compare the biotic responses to landscape and site-scale socio-ecological drivers, a phenomenon under-explored in existing literature. While previous comparative studies have indicated that mammals and birds are similarly impacted by intensive land use, they also show nuanced differences in their reactions to landscapes that are less-intensively modified (Burel et al. 1998; Felton et al. 2010; Santangeli et al. 2022). Moreover, mammals and birds, being charismatic and readily identifiable, are of particular community interest, and established survey procedures are suitable for deployment by citizen scientists. The selection of the four focal species (eastern barred bandicoot (\u003cem\u003ePerameles gunnii\u003c/em\u003e), eastern bettong (\u003cem\u003eBettongia gaimardi\u003c/em\u003e), eastern quoll (\u003cem\u003eDasyurus viverrinus\u003c/em\u003e) and long-nosed potoroo (\u003cem\u003ePotorous tridactylus\u003c/em\u003e)) for detailed analysis of social-ecological drivers was guided by the outputs of the stakeholder workshop. The species selected are all in the ‘critical weight range’ of high extinction risk in Australia (Johnson and Isaac 2009), and are especially susceptible to predation by feral cats. The species were expected to show both commonalities and contrasts in their response to habitat condition at site and landscape scales, thereby providing valuable insights into the complex dynamics of habitat-species interactions and socio-ecological drivers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eFramework\u003c/h2\u003e\n \u003cp\u003eThis research adopted a transdisciplinary methodology grounded in the principles of citizen science and participatory action research. Social-ecological systems are intricate, with intertwined human and natural components that influence one another in complex ways. In order to understand these systems a combination of quantitative and qualitative methods was used, drawing from both the natural and social sciences. The research process involved private landholders in five stages, including scoping, problem framing, data collection, data analysis, and feedback and reporting. Conservation practitioners and researchers were also involved during the scoping and problem framing stages of the research. The data collection and analysis procedures are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Stakeholder involvement in scoping and problem framing are essential elements of participatory research that ensure relevance by incorporating local ideas and knowledge systems into both research planning and implementation. The following \u003cspan class=\"InternalRef\"\u003emethods\u003c/span\u003e section will focus on stages 3\u0026ndash;5 of the research process, as the scoping and problem framing stages of the research are covered in detail in previously published papers (Taylor et al. \u003cspan class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e). A social-ecological systems conceptual model (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) was developed through a stakeholder workshop. The model identified the focal social-ecological factors and guided the data-collection phase of the research, including selection of ecological, landscape-context, socio-economic and land-management variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Location\u003c/h2\u003e\n \u003cp\u003eThe research was undertaken in southeast Tasmania, Australia, an area characterised by rich biodiversity, productive agricultural lands, and dynamic human-nature interactions. Tasmania is a large maritime island off the south coast of Australia, with a cool temperate climate. Tasmania has been continuously occupied by Aboriginal people for at least 40,000 years (Jones et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Following European colonisation, agriculture development has transformed much of the Tasmanian landscape. Tasmania has diverse wildlife communities, including species of mammals that remain reasonably common and widespread (with some localised declines) but are rare or extinct in their former ranges on mainland Australia. Large areas of land area under private ownership but with a high retained cover of native (variously modified) vegetation, and potentially high biodiversity value. Regional differences in agricultural practices and the extent of native vegetation clearance have influenced the distribution of native species and habitats. The mosaic of private land allotments, many small in extent, are subject to a wide variety of management styles that create a socio-ecologically heterogeneous landscape. The study focused on three distinct regions: the Huon Valley, Bruny Island, and the Derwent Valley (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These regions were selected in order to compare and contrast the influence of biogeographic and socio-economic characteristics on species distributions and habitat condition.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eRecruitment of Participants\u003c/h2\u003e\n \u003cp\u003ePrivate landholders with properties of at least 1 hectare in size were recruited via the networks of the Tasmanian Land Conservancy and through advertising on community noticeboards, traditional and social media. The project was titled \u0026lsquo;WildTracker\u0026rsquo; and has since evolved from the pilot phase reported herein into an ongoing citizen-science program hosted by the Tasmanian Land Conservancy. A total of 160 landholders participated in the research. All landholders attended a training workshop, which outlined the research objectives and provided hands-on training in techniques for collecting data on mammals, birds and habitat, including the use of motion-sensor cameras, sound recorders, and photo-point monitoring. Some landholders also participated in other elements of the research, including through interviews, a problem-framing workshop, socio-economic and land management survey, and classification of wildlife images and habitat photos. Recruitment and participation in the research were in accordance with Human Ethics Permit H0016014, issued by the University of Tasmania.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eSampling Strategy and Site Selection\u003c/h2\u003e\n \u003cp\u003eLandholders were provided with a property map that showed the distribution of broad vegetation or habitat types. They were instructed to establish a long-term monitoring site in one or more habitat types, depending on the size and ecological characteristics of the property. Sites were located at least 500m apart to mitigate spatial autocorrelation of the species distribution data. Landholders were provided with instructions on how to choose a suitable site for mammal and bird observations using the supplied equipment but were also encouraged to use knowledge of their property and local wildlife to guide the exact placement of the site. Further details of survey procedures for ecological indicators are provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eField collection of ecological data\u003c/h2\u003e\n \u003cp\u003eEcological data were collected by landholders on mammals, birds, and habitat condition. These site-scale indicators were selected because they are of high conservation significance, were identified as important natural values by landholders, and survey technologies and procedures are available that facilitate a citizen-science approach to both data collection and analysis. Equipment was provided by the Tasmanian Land Conservancy in four rounds, with a limited supply of equipment rotated between landholders over a 16-week period in the Tasmanian spring and summer. Landholders were provided with a field manual containing instructions on field survey techniques, and support could be accessed from the research team via phone or email. Field data were collected in accordance with Animal Ethics Permit A0015788, issued by the University of Tasmania.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSite indicators, survey method and procedures for collection of ecological data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite Indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurvey method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProcedure\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMammals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandholder survey \u0026ndash; wildlife camera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCamera type: ScoutGuard SG560K\u003c/p\u003e\n \u003cp\u003eDeployment period: 21 days\u003c/p\u003e\n \u003cp\u003eTrigger setting: multi-shot (3 photos)\u003c/p\u003e\n \u003cp\u003eLapse period: 30 seconds\u003c/p\u003e\n \u003cp\u003eLure: Scent attractant\u003c/p\u003e\n \u003cp\u003eDeployment location: on track (vehicle/walking/ animal)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBirds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandholder survey \u0026ndash; sound recorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecorder type: Zoom H2N or smartphone\u003c/p\u003e\n \u003cp\u003eRecording period: 20 minutes\u003c/p\u003e\n \u003cp\u003eRecording time: +\\- 60 minutes of dawn\u003c/p\u003e\n \u003cp\u003eRecoding location: same as for wildlife camera\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHabitat condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandholder survey \u0026ndash; photo monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCamera type: personal digital camera or smartphone\u003c/p\u003e\n \u003cp\u003ePhoto orientation: north, south, east, west\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eLandscape indicator calculation\u003c/h2\u003e\n \u003cp\u003eSpatial analyses were used to characterise the landscape context characteristics of each site, including vegetation cover, land productivity, and water availability. These indicators were identified in the stakeholder workshop as most important for determining distribution and abundance of native wildlife. Publicly available vegetation, agricultural and hydrological datasets were accessed from the Land Information Systems Tasmania database (Department of Natural Resources and Environment Tasmania \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Landscape indicators were analysed using geoprocessing functions in ArcGIS Pro software. Details of landscape indicators, source datasets and calculation procedures are detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLandscape indicators, source datasets and calculation procedures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLandscape Indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeospatial source data\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProcedure\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative vegetation type\u003c/p\u003e\n \u003cp\u003e(9 categories)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTASVEG 4.0 \u0026ndash; Vegetation Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSpatial join\u003c/em\u003e geoprocessing function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative vegetation extent\u003c/p\u003e\n \u003cp\u003e(percentage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTASVEG 4.0 \u0026ndash; Modified Vegetation Category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBuffer, Clip\u003c/em\u003e and \u003cem\u003eCalculate Geometry\u003c/em\u003e geoprocessing functions (100m, 250m, 500m, 1km, 2km, 5km)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiparian distance\u003c/p\u003e\n \u003cp\u003e(metres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIST Hydrology\u003c/p\u003e\n \u003cp\u003eLIST Hydrography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNear\u003c/em\u003e geoprocessing function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand productivity\u003c/p\u003e\n \u003cp\u003e(7 categories)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand capability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSpatial join\u003c/em\u003e geoprocessing function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSocio-Economic and Land Management Survey\u003c/h2\u003e\n \u003cp\u003eSocio-economic and land management data were collected via a survey sent to the 160 landholders participating in the WildTracker program. The survey questions were based on factors identified as most important during the stakeholder workshop (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The survey was also sent to 1066 neighbouring landholders within 500 metres of a data collection site, because the workshop identified neighbouring land management as a potentially important driver of wildlife conservation outcomes. In total 454 responses to the survey were received (response rate 57%).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLandholder survey categories, sub-categories, and question types.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub-category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuestion type\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProperty information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProperty size\u003c/p\u003e\n \u003cp\u003eOwnership time\u003c/p\u003e\n \u003cp\u003eProportion of income earned from property\u003c/p\u003e\n \u003cp\u003eResidential status\u003c/p\u003e\n \u003cp\u003eNatural resources\u003c/p\u003e\n \u003cp\u003eEnvironmental values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea in hectares or acres\u003c/p\u003e\n \u003cp\u003eYears of ownership\u003c/p\u003e\n \u003cp\u003e5 categories\u003c/p\u003e\n \u003cp\u003eResident/absentee\u003c/p\u003e\n \u003cp\u003e5 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e6 categories plus \u0026lsquo;other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProperty type\u003c/p\u003e\n \u003cp\u003ePrimary land manager\u003c/p\u003e\n \u003cp\u003eLand management objectives\u003c/p\u003e\n \u003cp\u003eLand management activities (current)\u003c/p\u003e\n \u003cp\u003eLand management activities (historic)\u003c/p\u003e\n \u003cp\u003eLand management time\u003c/p\u003e\n \u003cp\u003eLand management expenditure\u003c/p\u003e\n \u003cp\u003eEnvironmental / NRM program participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e4 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e6 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e15 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e15 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e4 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003eDollars per month\u003c/p\u003e\n \u003cp\u003e6 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandholder information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eCountry of birth\u003c/p\u003e\n \u003cp\u003eWeekly household income\u003c/p\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 categories\u003c/p\u003e\n \u003cp\u003eOpen ended response\u003c/p\u003e\n \u003cp\u003eOpen ended response\u003c/p\u003e\n \u003cp\u003e8 categories\u003c/p\u003e\n \u003cp\u003e5 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003eOpen ended response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnvironmental values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Ecological Paradigm Scale (Dunlap and Van Liere \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eSense of place\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 questions, Likert scale rating\u003c/p\u003e\n \u003cp\u003eOpen ended response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity organisation participation\u003c/p\u003e\n \u003cp\u003eCommon interests\u003c/p\u003e\n \u003cp\u003eCommunity relationships\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 categories plus \u0026lsquo;other\u003c/p\u003e\n \u003cp\u003e3 categories\u003c/p\u003e\n \u003cp\u003e3 categories\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal ecological knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSources of knowledge\u003c/p\u003e\n \u003cp\u003eTrustworthiness of knowledge sources\u003c/p\u003e\n \u003cp\u003eTypes of knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 categories plus \u0026lsquo;other\u0026rsquo;\u003c/p\u003e\n \u003cp\u003e7 categories, Likert scale rating\u003c/p\u003e\n \u003cp\u003e12 questions, Likert scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePreliminary Ecological Analysis\u003c/h2\u003e\n \u003cp\u003eLandholders collected raw data on mammals, birds, and habitat condition in the form of photos and sound recordings. Mammal and habitat images were classified by landholders and a team of volunteers, hosted by the Tasmanian Land Conservancy. Training sessions were provided by the research team and a guidebook was prepared to aid mammal identifications. Volunteer-classified images were validated by the research team through a hierarchical review process that focused on identifying unclassified images, then checking rare species, before finally checking a subset of commonly recorded species. In total volunteers classified 35,431 fauna detections from 160 camera deployments.\u003c/p\u003e\n \u003cp\u003eAn activity index for each species was calculated for each site as the proportion of days that the species was detected during a camera deployment. Total richness of mammals and feral animals was also calculated for each site and aggregated across deployments for properties with multiple survey sites.\u003c/p\u003e\n \u003cp\u003eAs a cross-validation procedure, the images were also classified using a recently developed deep-learning algorithm that had been trained on a dataset of over one million tagged images from parallel University of Tasmania fauna research projects (Brook et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cross validation of species detected at least 50 times showed a mean discrepancy between classification datasets of 11.7%. Rarer species were proportionally more likely to be misclassified by both human and AI recognisers, highlighting the importance of cross-validation measures in wildlife citizen-science projects.\u003c/p\u003e\n \u003cp\u003eSound recordings were reviewed by a skilled volunteer ornithologist and presence, or absence of bird species was recorded for each site. The sound recordings were validated by review of a proportion of recordings by a trained ornithologist. The acoustic dataset yielded 1165 observations from 84 sites.\u003c/p\u003e\n \u003cp\u003eHabitat photos were reviewed by volunteers and were visually classified according to structural characteristics (density of three vegetation strata), and the proportion of native to introduced plants. classification system is shown in detail in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe classification system used to analyse photo-point images of habitat\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVegetation structure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNativeness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStrata\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderstorey\u003c/p\u003e\n \u003cp\u003eMid-storey\u003c/p\u003e\n \u003cp\u003eCanopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow density\u0026thinsp;\u0026lt;\u0026thinsp;30%\u003c/p\u003e\n \u003cp\u003eMedium density 30\u0026ndash;70%\u003c/p\u003e\n \u003cp\u003eHigh density\u0026thinsp;\u0026gt;\u0026thinsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMostly exotic species (\u0026lt;\u0026thinsp;30% native)\u003c/p\u003e\n \u003cp\u003eMix of species (30\u0026ndash;70% native)\u003c/p\u003e\n \u003cp\u003eMostly native species (\u0026gt;\u0026thinsp;70% native)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eProperty reports and feedback/feedforward workshops\u003c/h2\u003e\n \u003cp\u003eProperty reports were compiled by Tasmanian Land Conservancy volunteers and provided to each landholder participating in WildTracker. The reports contained summary and descriptive statistics, such as habitat condition, the number of native species, the number of feral species, the relative abundance of each species, and expected species that were not detected. The expected species list was determined from species distribution maps for Tasmanian fauna (Rounsevell et al. \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). A property map and management recommendations were also included in each report.\u003c/p\u003e\n \u003cp\u003eFeedback/feedforward workshops were held in each participating region, in order to present the preliminary findings of the survey and identify future opportunities for improving the processes and areas of focus of the WildTracker program. Summary and descriptive statistics were calculated for each region in order to compare and contrast the findings of the fauna and habitat surveys at a regional scale. There were clear differences in the patterns of distribution and abundance of many species, provoking a discussion about potential causes, both natural and anthropogenic. The preliminary survey findings were also compiled into a report that was circulated to WildTracker participants (Taylor, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eDetailed Analysis of Ecological Components of the Social-Ecological System\u003c/h2\u003e\n \u003cp\u003eDetailed analysis of social-ecological datasets followed an iterative process, guided by the focal relationships identified as most important by the stakeholder-developed social-ecological system conceptual model. Analysis focused on the ecological and land management components of our social-ecological systems model. Social factors were considered as secondary drivers that effect primary drivers like vegetation cover and fragmentation. Calculations were done using the R statistical package (R Core Team \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of analytical procedures for detailed social-ecological analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnalysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelation plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo assess relationships, correlation plots were used to identify linear correlations in ecological data. We used the Pearson correlation coefficient to measure continuous dependent variables (e.g., species richness, species activity index) and independent variables (e.g., cat abundance, landscape characteristics, landowner survey data). For analysing relationships between continuous dependent variables and binomial categorical independent variables (e.g., region, land cover type, management practices), we used the point-biserial correlation method.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-metric multidimensional scaling (NMDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFor cluster analysis, we did Non-Metric Multidimensional Scaling (NMDS) analysis using the metaMDS function from the \u003cem\u003evegan\u003c/em\u003e package in R. NMDS is a distance-based ordination method that transforms the data matrix into a dissimilarity matrix, forming the basis for ordination. For this analysis, dissimilarity matrices were calculated for native mammal and bird activity, with rows representing sites and columns representing species. The NMDS procedure is iterative, starting with defining original positions in multidimensional space and creating an initial configuration in reduced dimensions (usually 2D).\u003c/p\u003e\n \u003cp\u003eThe initial configuration is refined through regression against observed distances, with stress as a measure of disagreement. Stress levels guide the quality of dimensionality reduction, with lower values indicating better representation. Final NMDS plots were generated for both groups, with points coloured by various landscape, site, and landowner survey variables to identify potential groupings.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest machine learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFor prediction, we used Random Forests machine learning via the \u003cem\u003erandomForest\u003c/em\u003e function from the \u003cem\u003ecaret\u003c/em\u003e package in R (Kuhn \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Random Forests, an ensemble of decision trees, employs recursive partitioning on subsets of training data and features. This approach introduces randomness, enhancing diversity and mitigating overfitting. In determining appropriate data splits, the trees contribute to an average prediction, generally yielding more accurate and robust results than individual decision trees.\u003c/p\u003e\n \u003cp\u003eThe analysis focused on identifying important variables in datasets including native mammal richness, native bird richness, and the abundance of both rare (eastern quoll, eastern bettong, eastern barred bandicoot, long nosed potoroo) and common species (Bennetts wallaby, bare-nosed wombat, grey currawong). For each independent tree, the dataset was divided into training (70%) and testing (30%) sets. Model tuning was via the \u003cem\u003etuneRF\u003c/em\u003e function to optimize the number of trees (\u003cem\u003entrees\u003c/em\u003e) and the number of variables tried at each split (\u003cem\u003emtry\u003c/em\u003e).\u003c/p\u003e\n \u003cp\u003eThe fit of the models was assessed using variance explained, and variable importance plots along with partial dependency plots to examine the relationships between predictor (landscape, site, and landowner variables) and dependent variables (species richness or abundance). Given the limited number of sites contributing landowner variables, these were analysed separately from other landscape and site variables, ensuring a focused and relevant examination of their impact.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe integration of diverse datasets including landholder-contributed data presented analytical challenges. Under the WildTracker participatory citizen-science model, the research collated extensive data from 160 properties and 285 sites. This approach, while robust in data collection, encountered gaps and inconsistencies across sites, a common issue in social-ecological and citizen science research (Dickinson et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Guerrero et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, these issues were manifested in a high number of moderately significant predictor variables. This emphasised the need for more uniform data collection methods and highlighted the intricacies of balancing the depth and breadth of data in complex systems. Overcoming these difficulties are crucial to understanding the dynamics affecting mammal and bird assemblages in privately managed landscapes, and they underscore the trade-offs inherent in such comprehensive social-ecological research endeavours.\u003c/p\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eSummary and descriptive statistics\u003c/h2\u003e\n \u003cp\u003eA total of 52 species were identified by the wildlife camera survey. This included 40 native species and 12 introduced species. Of the native species, 18 were mammals and 22 were birds. Of the introduced species, 9 were mammals and 3 were birds. Significant regional differences in the relative activity of native mammals (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) and introduced mammals (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The most common native mammal species are generalist herbivores such as the Tasmanian pademelon (\u003cem\u003eThylogale billardierii\u003c/em\u003e), Bennetts wallaby (\u003cem\u003eMacropus rufogriseus\u003c/em\u003e) and brushtail possum (\u003cem\u003eTrichosurus vulpecula\u003c/em\u003e), which were frequently detected across all study regions. Threatened species such as the eastern quoll (\u003cem\u003eDasyurus viverinus\u003c/em\u003e), Tasmanian devil (\u003cem\u003eSarcophilus harrisii\u003c/em\u003e) and eastern barred bandicoot (\u003cem\u003ePerameles gunnii\u003c/em\u003e) were less frequently detected. The Huon valley remains a relative stronghold for these species. Some species listed as least concern, such as the bare-nosed wombat (\u003cem\u003eVombatus ursinus\u003c/em\u003e), long-nosed potoroo (\u003cem\u003ePotorous tridactylus\u003c/em\u003e) and southern brown bandicoot (\u003cem\u003eIsoodon obesulus\u003c/em\u003e) were among the least frequently detected species. Note that Bruny Island has a naturally lower diversity of native mammal species: species such as the Tasmanian devil and spotted tailed quoll were absent from the island at the time of European colonisation.\u003c/p\u003e\n \u003cp\u003eData on both domestic and feral animals are presented here in order to show the relative abundance of these species in comparison to native fauna. Sheep and cattle are the most abundant domestic livestock species in southeast Tasmania. Sheep were most frequently detected on Bruny Island where there are extensive grazing properties on the northern part of the island. Cats were second most frequently detected introduced species, despite their typically low density and cryptic nature. They are detected at a similar frequency across all three study regions. Fallow deer were detected most frequently on Bruny Island and is noteworthy in that they are a recently established population following a documented escape into the wild.\u003c/p\u003e\n \u003cp\u003eA total of 64 species of birds were identified by the acoustic survey. This included 54 native species, seven of which were Tasmanian endemic species, and ten introduced species. Three threatened species were identified: the swift parrot \u003cem\u003e(Lathamus discolor)\u003c/em\u003e was identified at 15 sites, the blue-winged parrot (\u003cem\u003eNeophema chrysostoma\u003c/em\u003e) was detected at five sites, and grey goshawk (\u003cem\u003eAccipiter novaehollandiae\u003c/em\u003e) was identified at one site. The most frequently detected species was the forest raven (\u003cem\u003eCorvus tasmanicus\u003c/em\u003e), which was detected at 92% of sites. The ten most frequently detected species were all native and were detected at \u0026gt;\u0026thinsp;50% of sites. The most frequently detected introduced species was the common blackbird (\u003cem\u003eTurdus merula\u003c/em\u003e), which was detected at 49% of sites. Three introduced species of management concern were detected: the rainbow lorikeet, superb lyrebird and Eurasian starling. The rainbow lorikeet and superb lyrebird are native to the Australian mainland but were introduced to Tasmania. These species potentially impact native species via competition for nesting and foraging resources, and habitat alteration (lyrebird). Rainbow lorikeet and Eurasian starling Species richness per site was greatest in the Derwent Valley (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), but the Huon Vally recorded the highest diversity of bird species (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eSocio-Ecological Drivers of Wildlife Conservation \u0026ndash; Land Management and Ecological Components\u003c/h2\u003e\n \u003cp\u003eCorrelation plots of mammal and bird richness with site and landscape variables showed low to moderate correlations across a wide range of variables. The focal species showed intercorrelation amongst those species, and stronger correlations between them and site factors than landscape factors. Common species (Bennetts wallaby, common wombat, brushtail possum, grey currawong) all showed week relationships with predictor variables, indicating their ubiquity in the landscape (low habitat selectivity). Correlations were also observed between land management predictors and site and landscape scale vegetation predictors. These variables are the nexus between ecological and social components of the social-ecological system. Correlations between social-ecological predictor variables and mammal richness, bird richness, and focal species activity index are shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eNon-metric multidimensional scaling of mammal and bird observation data showed no obvious differentiation of fauna into distinctive communities across the study area when visualised against the majority of predictor variables. The only clear grouping separated the Bruny Island mammal assemblage from the other study regions (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e), which is to be expected given that it is an island with a naturally depauperate fauna. This community separation was not observed for birds, which are able to transit the narrow channel between the island and mainland Tasmania.\u003c/p\u003e\n \u003cp\u003eRandom forest (RF) analysis confirmed the importance of relationships between predictors and dependent variables identified through correlation plots. Dependency plots show non-linear relationships and evidence of ecological thresholds, especially for the native-vegetation extent landscape variables. The activity level of cats was found to predict both mammal richness and the activity of focal mammal species. The same relationship was also found for introduced animal richness. RF analysis also identified additional predictor variables of importance for native mammals relating to the extent of native vegetation within a radius of a monitoring site. Mammal richness declined when native vegetation cover within 1km of a site decreased beyond 80%, and when native vegetation within 100m of a site decreased beyond 50% (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). Eastern barred bandicoot activity decreased sharply with distance from a stream or waterbody, and when native vegetation within 2km of a site decreased below 50% (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e). Eastern bettong activity increased with increasing shrub cover, and decreased when native vegetation within 1km of a site was below 70% (Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e). Eastern quoll activity decreased substantially when native vegetation extent within 1km of a site was below 50% and increased with increasing shrub cover (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e). Long-nosed potoroo activity decreased when native vegetation within 100m of a site decreased below 50% and increased within an increasing proportion of native understorey vegetation (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e). The importance of predictor variables is covered comprehensively in the Supplementary Materials.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHerein we have identified the key social-ecological drivers influencing wildlife populations and highlights the importance of \u0026lsquo;messy\u0026rsquo;, ecologically heterogeneous, human-dominated landscapes for wildlife conservation. The study's approach, centred on co-design and active stakeholder participation, led to a broad analysis of the social-ecological system, moving away from a conventional hypothesis-driven model. This expansive analysis identified several distinct and significant relationships within the system. These insights, emerging from a thorough and inclusive research process, provide key understandings into the complexities of wildlife conservation on private lands.\u003c/p\u003e \u003cp\u003eThe discussion below is structured according to the guiding conceptual model of social-ecological systems. It focuses on major findings related to land management, landscape-scale drivers, site-scale drivers, and invasive species affecting wildlife species richness and activity patterns. A common finding across theses social-ecological components, is that native wildlife can tolerate or thrive in the highly modified habitats that have been created by people in rural landscapes. Additionally, the important role of citizen scientists in wildlife monitoring and management is examined, and we advocate for a greater role for private landholders in landscape scale wildlife monitoring and management.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLand management\u003c/h2\u003e \u003cp\u003eLand management serves as a critical nexus in the interaction among humans, wildlife, and habitats within our social-ecological model. Human activities have profoundly modified private landscapes creating ecologically heterogeneous environments. Conventional conservation theory would indicate that the impact of this modification on native wildlife would be deleterious. However, we found a mix of positive and negative associations with different categories of land use. Utilising survey data from landholders, our study investigated the dynamics of land management and its implications for wildlife conservation. We observed a complex impact of land management on wildlife populations, revealing interactions among property size, land use, and wildlife dynamics. Notably, not all examined factors were important to wildlife, and the relationships presented here are relatively weak. Our analysis categorises land management factors into primary, active factors like grazing, invasive species management, and restoration, and secondary factors including property type, time spent on land management, property size, and income from land.\u003c/p\u003e \u003cp\u003eThe most significant predictor of wildlife outcomes related to grazing, which correlated with negative outcomes for all fauna indicators. Grazing emerged as the most significant predictor of negative wildlife outcomes, correlating with larger properties and higher farm income. It impacts fauna populations by degrading understorey habitat quality and extent (Kirkpatrick et al. 2005) and is linked to broader landscape scale drivers such as native vegetation clearance and fragmentation (Eichenwald et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Grazing management was associated with larger properties and greater farm income. Note that our study didn\u0026rsquo;t quantify grazing intensity, and research suggests that some grazing strategies are conducive to wildlife and habitat conservation (Leonard and Kirkpatrick \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In contrast, properties dedicated to conservation purposes, while associated with less time spent on land management, exhibited higher diversity of mammals and birds, and were positively correlated with our four focal mammal species. This emphasises the important role of private conservation lands in complementing and connecting public reserve systems and nature conservation initiatives (Ivanova and Cook \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bingham et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interestingly, bird diversity was negatively correlated with the presence of active or historic native vegetation restoration on a property, an effect not observed in mammals. Restoration was negatively correlated with vegetation extent and site-scale habitat condition variables. Revegetation typically occurs in highly cleared and fragmented landscapes (Davidson et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and there has been a historic tendency in restoration initiatives to plant primarily canopy tree species rather than diverse understorey that provides structural complexity (Lindenmayer et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jones et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This can favour aggressive forest and woodland birds if there is an absence of suitable cover for smaller species such as honeyeaters (Munro et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bennett et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe variation in responses between bird and mammal species underscores the need for targeted conservation strategies that address the unique needs of different faunal groups. The historical context of land management is also critical, as past practices may continue to influence present ecological conditions (Race et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The participation of landholders in the research process as survey respondents and data collectors provides valuable insights but may introduce a self-selection bias, a factor that must be considered when interpreting these results (Pateman et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Landholders with a disinterested or antagonistic attitude to wildlife are unlikely to have engaged in our conservation-centric project. This study highlights the diverse and sometimes counterintuitive effects of land management practices on wildlife conservation. The diversity of management approaches evident in private landscapes determines landscape-scale patterns in the extent, configuration, and condition of habitats for wildlife and is therefore a fundamental social-ecological driver of wildlife conservation outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLandscape-scale social-ecological drivers: thresholds of habitat loss and fragmentation\u003c/h2\u003e \u003cp\u003eAgricultural and residential development have profoundly modified native ecosystems at landscapes scales (Mackey et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Magioli et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The most damaging impact has been the conversion and degradation of habitat (Legge et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these impacts, this study shows that wildlife is resilient and can persist in landscapes that have been significantly modified by human activities. A key finding of this research is the distinction between the impacts when viewed at the site versus landscape scales. While site-specific factors influence immediate habitats, landscape-scale considerations are pivotal in shaping broader ecological networks and corridors. These larger-scale factors significantly affect faunal movement, genetic diversity, and long-term species viability, emphasising the need for a strategic landscape-scale approach to wildlife conservation (Downes et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Mackey et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Davidson et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the principal landscape-scale factors influencing mammal and bird diversity is the intactness of native vegetation within a radius of a site. Our findings confirm that both mammal and bird assemblages can tolerate a relatively high degree of fragmentation (Fischer and Lindenmayer \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), with this tolerance extending from hundreds of meters to kilometres from a detected location. However, there are critical thresholds for native vegetation loss, beyond which species richness at the landscape level diminishes (Saunders et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Fischer and Lindenmayer \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). We found that at certain degree of native vegetation loss, a decline in species richness at a site becomes evident. This pattern was observed for the focal mammal species, with the distance and the percentage threshold of intact habitat varying between species. For instance, the activity of the long-nosed potoroo showed a sharp decline when native vegetation within 100 meters of a site dropped below 50%. This aligns with other studies indicating the species' preference for intact forest areas (Norton et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, the activity of eastern quolls and eastern barred bettongs declined significantly at sites where there was a loss of more than 50% of native vegetation within a 2km radius. Species such as the eastern quoll and eastern barred bandicoot are more tolerant of landscape scale disturbance compared to the long-nosed potoroo. The contrast in conservation status, with both the quoll and bandicoot being threatened while the potoroo is not (although it is patchily distributed), underscores the importance of managing site-scale factors in conjunction with landscape-scale vegetation configuration. This finding supports findings from quantitative and expert elicitation analyses of dispersion in Australian species (Jones and Davidson \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lechner et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such an understanding of species-specific thresholds can inform evidence-based landscape scale conservation planning, tailoring strategies to the unique ecological needs of each species (Noss \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lechner et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Proft et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gardiner et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and thereby helping wildlife to persist in modified and heterogeneous private landscapes.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSite scale socio-ecological drivers: the importance of productive, mixed-use lands\u003c/h2\u003e \u003cp\u003eOur study found that many wildlife species are able to persist and even thrive in highly modified habitats at the site scale. Some groups and taxa even displayed a preference for modified habitats, highlighting the importance of productive landscapes where mixed agricultural and residential land uses dominate. Mammals were more diverse in areas of high land productivity, regardless of the composition of the vegetation, and were positively correlated with the \u003cem\u003eModified Land\u003c/em\u003e vegetation category. The eastern quoll and eastern barred bandicoot (both threatened species) showed a preference for modified land and valley locations in proximity to streams or waterbodies. At face value this finding contradicts an established literature that consistently demonstrates that conversion of habitat leads to decline in native wildlife populations (Johnson et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Almond et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Legge et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Productive landscapes are the focus of agricultural development and human settlement, which has resulted in the loss of significant proportion of native vegetation.\u003c/p\u003e \u003cp\u003eHowever, there is a growing literature that recognises the values of mixed agricultural and peri-urban landscapes for faunal conservation (Burel et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Dotta and Verdade \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ehlers Smith et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Semenchuk et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Productive landscapes provide the highest and the most consistent supply of natural resources and historically supported the richest native fauna communities prior to extensive agricultural activities. Many native faunal species are not dependent on native plants for food and are able to coexist alongside people and agriculture, as long as the basic requirements of foraging resources and sheltering habitat are met (Burel et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rodewald \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Dertien and Baldwin \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, introduced plants have been found to provide important habitat (e.g., food, shelter, cover from predation) in the absence of native alternatives (Marris \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ranyard et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This finding supports more nuanced approach to conservation that decouples the conservation of fauna from the conservation of native habitats, in favour of a focus on managing specific pressures that threaten native fauna in human landscape on private land.\u003c/p\u003e \u003cp\u003eContrasting the pattern observed in mammals, two of our focal species, the eastern bettong, and the long-nosed potoroo, exhibited distinct preferences for native habitat. The eastern bettong favoured intact native vegetation with substantial ground cover, while the long-nosed potoroo's site-scale habitat preferences were less specific, though it did show a preference for areas with ground cover and a closed canopy. Avian diversity also showed a stronger correlation with mixed and undisturbed remnant vegetation, being highest in sites with intact native understorey, and negatively correlated with sites with primarily introduced vegetation. Bird assemblages were more diverse in locations with a higher density of native shrubs. Diverse native habitats provide a greater variety of foraging niches and shelter from larger aggressive birds such as forest raven and noisy minor (Catterall et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Lindenmayer et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bennett et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hingee et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and our research confirms pervious research on the importance of native understorey habitat for these species. These findings underscore the necessity of a multifaceted approach to fauna conservation and management, one that is attuned to the diverse needs of local species. Effective management hinges on robust monitoring data; without knowledge of the species present on a property or in a specific area, it becomes challenging to devise land management strategies that cater to all species. A \u0026lsquo;diversified strategy\u0026rsquo; in conservation management is likely to yield the most resilient outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSite-scale socio-ecological drivers:\u003c/h2\u003e \u003cp\u003eThe feral cat is a mid-sized invasive predator that is widespread in Australia. Cats prey on a wide range of taxa, from small rodents, reptiles, and amphibians to small and mid-sized marsupials (Doherty et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). They are recognised as significant invasive species both globally and especially on offshore islands (Medina et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Dickman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Legge et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A notable finding of our study was that feral cats were more prevalent in modified landscapes, in areas of higher land productivity and sites with the greatest diversity and activity of native mammals and birds. This aligns with findings from other researchers, such as Hamer et al (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who observed that both cats and the native spotted-tailed quoll were abundant in high-productivity areas, which support plentiful prey populations. Notably, mammal and bird richness, as well as the activity of all four focal mammal species, showed positive correlations with high cat activity in our study. This finding seems counterintuitive, because numerous studies have documented the adverse impact of cats on fauna, including their capacity to cause local extinctions and their role in the extinction of many of Australia's 34 mammal species since European colonisation (Legge et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, our study also found that the activity of the four focal species was positively correlated with the presence of medium to high-density ground layer vegetation, while being negatively correlated with areas lacking dense ground vegetation, and that bird diversity was similarly linked to the presence of an intact native shrub layer and medium-density ground vegetation. These findings underscore the significance of sheltering habitats in protecting 'critical weight range' mammals and other native fauna from predation, supporting the emerging perspective that complex understory habitats enable small to mid-sized Australian mammals to coexist with high densities of feral cats (Cunningham et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Radford et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given the high cost and logistical challenges associated with feral cat control, managing land to maintain understory vegetation emerges as a pragmatic and implementable strategy for conserving native wildlife in Australia and beyond (Lazenby et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In modified Tasmanian private landscapes, exotic plants species such as gorse (\u003cem\u003eUlex europaeus\u003c/em\u003e) may have an important ecological role to play in wildlife conservation (Ranyard et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), further supporting our key finding that \u0026lsquo;messy\u0026rsquo; heterogeneous landscapes and properties are important for many wildlife species.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eA role for citizen scientists in wildlife monitoring and threatened species assessment?\u003c/h2\u003e \u003cp\u003eThe WildTracker research collaboration was primarily aimed at enhancing the capacity of landholders in wildlife management on their properties, fostering a network that includes landholders, researchers, and conservation practitioners. This approach not only aimed at co-designing locally relevant data gathering tools but also at enabling landholders to address specific, meaningful questions related to local wildlife management issues. The significant number of observations of threatened species by WildTracker participants, corresponded to strong community interest in those species, and demonstrates the potential benefits of utilising citizen science in both social-ecological research and wildlife monitoring.\u003c/p\u003e \u003cp\u003eDespite presenting significant logistical, training and data-integrity challenges, the unrealised potential of citizen science in biodiversity research, particularly in filling the data gaps that hinder effective conservation efforts has been emphasised by a substantive literature (Locke et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dissanayake et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fischer et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our findings corroborate this, showing that citizen scientists were adept at identifying both threatened mammal and bird species across various locations, contributing essential data that might otherwise be unavailable. A total of four threatened mammals and three threatened bird species were identified across numerous locations in all three regions. The data presented in this paper is from one year of data, but ongoing participation in WildTracker is now starting to yield information on trends in wildlife populations. This approach, especially in regions like Tasmania where ecological monitoring is under-resourced, presents a promising avenue for enhancing biodiversity tracking. Many jurisdictions, including Tasmania, suffer from a lack of investment in ecological monitoring. The need for additional resourcing of monitoring is also highlighted by the finding that many species categorised as least concern were found in far lower frequency than some endangered species.\u003c/p\u003e \u003cp\u003eThere is a case for assessment of least-concern species such as the southern brown bandicoot, a species considered common and widespread, but which was only identified at a small proportion of sites by this study. Although potentially still locally common on intact public lands, our data suggests a significant decline on private lands. The listing of the eastern quoll as endangered further illustrates this point: this iconic species jumped from least-concern to endangered, only because of a targeted research project that documented a significant decline of the species (Fancourt et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fancourt \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This underscores the importance of continuous and comprehensive monitoring strategies, a task where citizen science can play a pivotal role (Mckinley et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Lack of data is a serious impediment to effective conservation because the prioritisation of environmental investments and policy are often based on the listing status of species. The role of citizen scientists in identifying the range and trajectory of threatened and more common species can help fill this gap, especially in private landscapes of which they are the custodians.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrates the importance of private lands for wildlife conservation, particularly productive environments where there a mix of land uses including agriculture and human settlement. These landscapes are characterised by their ‘messiness’, ecologically heterogeneous at both the site and landscape scale. The diversity of land management across these areas creates a complex mosaic that supports a high diversity of wildlife and offers more stable resources such as food and water. This underscores the need for a new model that recognises the value of modified landscapes in wildlife conservation, akin to the concept of 'rambunctious gardens' (Marris 2013), where ecologically varied agricultural and peri-urban areas serve as sanctuaries for both people and wildlife. Our research shows how the interaction between ecological, socio-economic, and land management factors shape wildlife conservation on private landscapes in Tasmania and has relevance to global wildlife conservation efforts. Understanding these dynamics is crucial for developing effective conservation strategies that encompass all aspects of socio-ecological systems, including mammals, birds, their habitats, and private landholder (Hull et al. 2023). Innovative solutions that acknowledge the importance of modified and novel ecosystems are critical to reversing the decline of native wildlife populations on private lands.\u003c/p\u003e\n\u003cp\u003eParticipatory initiatives such as WildTracker can play a significant role, empowering local communities with ecological data, knowledge, and management tools. By integrating insights from WildTracker workshops and interviews, we generated specific hypotheses, validated through analytical methods. This blended empirical data with local observations, enhancing our understanding of socio-ecological dynamics. It identified relationships between fauna assemblages and land management practices at both site and landscape scales, emphasising the need to consider local and broader ecological processes in conservation strategies. The significance of spatial scale in wildlife conservation on private lands cannot be overstated. While individual property-level habitat management is important, it often falls short for wide-ranging species that require broader landscape-level conservation actions (Mackey et al. 2013; Lindenmayer et al. 2016). Consequently, successful conservation strategies on private lands require a synergistic approach: one that not only caters to specific local habitat requirements but also promotes collaborative initiatives at the landscape level among property owners. Employing a socio-ecological methodology, which integrates diverse disciplinary perspectives and stakeholder insights, ensures that conservation practices are both contextually appropriate at the local level and practical to implement, thereby addressing the varied requirements of wildlife species across private landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research team wishes to acknowledge the invaluable contribution of our research partner the Tasmanian Land Conservancy, which provided major financial, logistical, and technical assistance with this project. Dr Michael Lockwood was the initial supervisor of this research project and contributed substantially to research design. Associate Professor Aidan Davison and Dr Andrew Harwood are part of the research team and have contributed substantial social-science knowledge to the design of the project. We would also like to thank the many participating landholders who contributed their time, ideas and enthusiasm that made this research possible. Finally, we\u0026rsquo;d like to thank NRM South and the Land for Wildlife Program, which provided assistance with recruitment of participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement of Country\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Tasmanian Aboriginal people, as custodians of the land where this project was undertaken and pay respect to Elders, past, present and emerging. Lutruwita (Tasmania) has been home to the palawa people for thousands of years, and there is much that western ecological science can learn from their traditional knowledge about living in balance with nature. Aboriginal and Torres Strait Islander sovereignty was never ceded, this was and always will be Aboriginal land.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests and funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests in the conduct of this research. This paper has not been published nor submitted for publication elsewhere. The authors received research funding and financial support from the Tasmanian Land Conservancy (MT) and the University of Tasmania (MT, BB, CJ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project has received human and animal ethics permits from the University of Tasmania\u0026rsquo;s Human Research and Animal Ethics Research Committees (refs. H0016014 and A0015788). All research participants have provided written consent to participate and for their data to be used for this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors whose names appear on the submission:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003emade substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;\u003c/li\u003e\n \u003cli\u003edrafted the work or revised it critically for important intellectual content;\u003c/li\u003e\n \u003cli\u003eapproved the version to be published; and\u003c/li\u003e\n \u003cli\u003eagree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified interview data, survey data and quantitative ecological data may be made available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors whose names appear on the submission:1. made substantial contributions to the conception or design of the work (MT, BB, CJ); or the acquisition (MT), analysis (MT, SDL), or interpretation of data (MT);2. drafted the work (MT) or revised it critically for important intellectual content (BB, CJ, SDL);3. approved the version to be published (ALL); and4. agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved (ALL).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkamani K, Holzmueller EJ, Groninger JW (2016) Managing wicked environmental problems as complex social-ecological systems: the promise of adaptive governance. 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Biol Conserv 165:128\u0026ndash;138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2013.05.025\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2013.05.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wildlife conservation, social-ecological systems, private land, transdisciplinary research, citizen science","lastPublishedDoi":"10.21203/rs.3.rs-3916808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3916808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs human activity accelerates the global crisis facing wildlife populations, private land conservation provides an example of wildlife management challenges in social-ecological systems. This study reports on the research phase of \u0026lsquo;WildTracker\u0026rsquo; - a co-created citizen science project, involving 160 landholders across three Tasmanian regions. This was a transdisciplinary collaboration between an environmental organisation, university researchers, and local landholders. Focusing on mammal and bird species, the project integrated diverse data types and technologies: social surveys, quantitative ecology, motion sensor cameras, acoustic recorders, and advanced machine-learning analytics. An iterative analytical methodology encompassed Pearson and point-biserial correlation for interrelationships, Non-Metric Multidimensional Scaling (NMDS) for clustering, and Random Forest machine learning for variable importance and prediction. Taken together, these analyses revealed complex relationships between wildlife populations and a suite of ecological, socio-economic, and land management variables. Both site-scale habitat characteristics and landscape-scale vegetation patterns were useful predictors of mammal and bird activity, but these relationships were different for mammals and birds. Four focal mammal species showed variation in their response to ecological and land management drivers. Unexpectedly, threatened species, such as the eastern quoll (\u003cem\u003eDasyurus viverinus)\u003c/em\u003e, favoured locations where habitat was substantially modified by human activities. The research provides actionable insights for landowners, and highlights the importance of \u0026lsquo;messy\u0026rsquo;, ecologically heterogeneous, mixed agricultural landscapes for wildlife conservation. The identification of thresholds in habitat fragmentation reinforced the importance of collaboration across private landscapes. Participatory research models such as WildTracker can complement efforts to address the wicked problem of wildlife conservation in the Anthropocene.\u003c/p\u003e","manuscriptTitle":"Wildlife conservation on private land: a social-ecological systems study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-05 10:09:45","doi":"10.21203/rs.3.rs-3916808/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-25T21:50:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-24T04:31:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"856fd631-a300-4e9c-ae4c-4fb81dff9fe5","date":"2024-02-02T09:13:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-02T01:07:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-02T01:00:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-01T12:59:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Management","date":"2024-02-01T09:13:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"89018d47-30fb-455f-a21c-badaa4938d3f","owner":[],"postedDate":"February 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-25T15:12:36+00:00","versionOfRecord":{"articleIdentity":"rs-3916808","link":"https://doi.org/10.1007/s00267-024-01962-w","journal":{"identity":"environmental-management","isVorOnly":false,"title":"Environmental Management"},"publishedOn":"2024-03-23 15:01:03","publishedOnDateReadable":"March 23rd, 2024"},"versionCreatedAt":"2024-02-05 10:09:45","video":"","vorDoi":"10.1007/s00267-024-01962-w","vorDoiUrl":"https://doi.org/10.1007/s00267-024-01962-w","workflowStages":[]},"version":"v1","identity":"rs-3916808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3916808","identity":"rs-3916808","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}