Combining social media photographs and species distribution models to map cultural ecosystem services: The case of a Natural Park in Portugal

Clemente, Pedro; Calvache, Marta F.; Antunes, Paula; Santos, Rui; Cerdeira, J. Orestes; Martins, Maria João

doi:10.1016/j.ecolind.2018.08.043

Cited by 111 publications

(65 citation statements)

References 33 publications

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“…In this paper, we examined multiple methods of eliciting cultural ecosystem services in urban greenspace in a spatialized fashion, to examine the presence, type, and distribution of CES in Prospect Park, Brooklyn, a flagship urban park located in a large city. Previously, work has focused on mapping cultural ecosystem services through photo-based social media [11,49,55], focus groups [56], participatory Geographic Information Systems (GIS) [43], interviews [34,47], and surveys [8,48]. Here, we add geotagged text-based social media as another method of capturing the presence of CES, specifically in an urban greenspace context.…”

Section: Discussionmentioning

confidence: 99%

“…Previous work examining photo based social media platforms in European landscapes have also elicited multiple CES: (Recreation; Aesthetic landscape; Scientific and educational; Cultural heritage and identity; Spiritual and religious; and Inspiration) [11] and recreation, cultural heritage, social, and spiritual [59]. Our coding of tweets and associated photos (where present) and interviews find similar CES, but as our work focuses on an urban park in the United States, the underlying resource and the social context are very different.…”

Section: Cultural Ecosystem Services In Prospect Parkmentioning

confidence: 99%

“…In a review of human-environment urban green space studies, Kabisch et al identified that most studies focused on a single park and applied surveys as the dominant methodology [9]. With the advent of 'big data', social media can provide crowdsourced, geotagged data about people's experiences with and interactions in parks that enable us to study park usership in new ways [10,11]. Methods are needed to harness and triangulate these newly available data that maximize reliability and validity.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Urban Park Cultural Ecosystem Services: A Comparison of Twitter and Semi-Structured Interview Methods

et al. 2019

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Understanding the benefits received from urban greenspace is critical for planning and decision-making. The benefits of parks can be challenging to measure and evaluate, which calls for the development of novel methods. Crowdsourced data from social media can provide a platform for measuring and understanding social values. However, such methods can have drawbacks, including representation bias, undirected content, and a lack of demographic data. We compare the amount and distribution of park benefits elicited from (1) tweets on Twitter about Prospect Park, Brooklyn, New York (n = 451) with park benefits derived from (2) broad (n = 288) and (3) directed (n = 39) questions on two semi-structured interview protocols for park users within Prospect Park. We applied combined deductive and inductive coding to all three datasets, drawing from the Millennium Ecosystem Assessment’s (MEA) cultural ecosystem services (CES) framework. All three methods elicited an overlapping set of CES, but only the Twitter dataset captured all 10 MEA-defined CES. All methods elicited social relations and recreation as commonly occurring, but only the directed question interview protocol was able to widely elicit spiritual values. We conclude this paper with a discussion of tradeoffs and triangulation opportunities when using Twitter data to measure CES and other urban park benefits.

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Section: Discussionmentioning

confidence: 99%

Section: Cultural Ecosystem Services In Prospect Parkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping Urban Park Cultural Ecosystem Services: A Comparison of Twitter and Semi-Structured Interview Methods

et al. 2019

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“…While there are many methods with which to generate SDMs (Guisan & Thuiller, ), maxent has become the most popular for both its ease of use and functionality (Morales, Fernández, & Baca‐González, ). maxent is a machine learning algorithm that allows SDMs to be generated using presence‐only data, making it an effective tool for predicting species distribution when obtaining presence–absence data is logistically impractical (Clemente et al, ; Kabir et al, ; Zhang, Yao, Meng, & Tao, ).…”

Section: Methodsmentioning

confidence: 99%

Species distribution modelling supports “nectar corridor” hypothesis for migratory nectarivorous bats and conservation of tropical dry forest

Burke

Frey

Ganguli

et al. 2019

Diversity and Distributions

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Aim:The Mexican long-tongued bat (Choeronycteris mexicana), Mexican long-nosed bat (Leptonycteris nivalis) and lesser long-nosed bat (Leptonycteris yerbabuenae) (Phyllostomidae: Glossophaginae) undertake long-distance migrations from southcentral Mexico to the south-western United States. It is proposed that these bats migrate along a nectar corridor of columnar cacti and Agave species, but this has not been tested with independent data and the spatiotemporal nature of this relationship is poorly understood. Our goal was to test this nectar corridor hypothesis and determine the relative importance of food plant and abiotic variables to the distribution and seasonal movements of these migratory nectarivores.Location: Mexico and the south-western United States. Methods:We generated species distribution models (SDMs) of documented food plants for these bats. We then created SDMs for each bat following a model selection approach, using food plant and abiotic predictor variables. We modelled migration pathways for C. mexicana and L. yerbabuenae using circuit theory and seasonal SDMs based on seasonally available food plants. Main conclusions: Food plants were more important than climatic and topographic variables in shaping the distribution of these bats. The most important predictors of distribution were Agave, columnar cacti and species richness of food plants. Species richness of food plants was the most consistently important variable, but the components of this diversity varied by bat species: Choeronycteris mexicana was influenced by Agave and cacti; Leptonycteris nivalis was influenced solely by Agave; Leptonycteris yerbabuenae was influenced more generally by cacti, Agave and C3 plants. Migration models for C. mexicana and L. yerbabuenae provided independent support for the nectar corridor hypothesis and indicate shifts in relative importance of specific food plants throughout the year. These results suggest that conservation of these bats should focus more broadly on management for species richness of food plants, especially in tropical dry forests.

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“…We used MaxEnt, a statistical learning technique that can be used to model environmental suitability across a geographic extent (Elith et al, ). Widely used in species distribution modeling (for recent examples, see Berthon, Esperon‐Rodriguez, Beaumont, Carnegie, & Leishman, ; Clemente et al, ; Coxen, Frey, Carleton, & Collins, ; Zhang, Yao, Meng, & Tao, ), we applied this approach to examine the spatial distribution of the risk of illegal activity across the study area because this approach produces robust predictions and is one of the most user‐friendly techniques with regards to both model construction and interpretation (Merow, Smith, & Silander, ). The illegal activity data is “presence only” data, as there are no direct observations of “absence.” This is a common situation, particularly in the context of species distribution modeling (Elith & Leathwick, ).…”

Section: Methodsmentioning

confidence: 99%

Predicting the risk of illegal activity and evaluating law enforcement interventions in the western Serengeti

Snyder

Mneney²,

Wittemyer

2019

Conservat Sci and Prac

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Illegal activity within protected areas is a primary driver of species decline and threatens conservation efforts. In the western corridor of the Serengeti Ecosystem of northern Tanzania, the Grumeti and Ikorongo Game Reserves and Ikona Wildlife Management Area provide an important buffer between permanent settlements and Serengeti National Park, while simultaneously maintaining critical wildlife habitat. Understanding the spatial distribution and environmental drivers of illegal activity is critical to optimize biodiversity protection efforts in this important ecosystem. We examined a rare dataset containing detailed records of reserve game scout patrol effort and occurrences of illegal activity between 2013 and 2016. We used presence only data to construct predictive models of five categories of illegal activity. We derived spatial predictions of the likelihood of different activities and identified the environmental variables predictive of risk for each activity. The highest risk areas were located along reserve edges, further from roads and scout camps, suggesting avoidance of enforcement presence. Activities associated with wildlife offtake were the most widely distributed in the study area and extended into the national park. Permanent scout camps were more effective deterrents of all illegal activities than observation posts, but their limited spatial influence demonstrated additional enforcement strategies are required. K E Y W O R D SAfrica, bushmeat, predictive modeling, protected areas, risk assessment, snare

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Combining social media photographs and species distribution models to map cultural ecosystem services: The case of a Natural Park in Portugal

Cited by 111 publications

References 33 publications

Mapping Urban Park Cultural Ecosystem Services: A Comparison of Twitter and Semi-Structured Interview Methods

Mapping Urban Park Cultural Ecosystem Services: A Comparison of Twitter and Semi-Structured Interview Methods

Species distribution modelling supports “nectar corridor” hypothesis for migratory nectarivorous bats and conservation of tropical dry forest

Predicting the risk of illegal activity and evaluating law enforcement interventions in the western Serengeti

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