Predicting animal abundance through local ecological knowledge: An internal validation using consensus analysis

Given the ongoing environmental degradation from local to global scales, it is fundamental to develop more efficient means of gathering data on species and ecosystems. Local ecological knowledge, in which local communities can consistently provide information on the status of animal species over time, has been shown to be effective. Several studies demonstrate that data gathered using local ecological knowledge (LEK)-based methods are comparable with data obtained from conventional methods (such as line transects and camera traps). Here, we employ a consensus analysis to validate and evaluate the accuracy of interview data on LEK. Additionally, we investigate the influence of social and bioecological variables on enhancing data quality. We interviewed 323 persons in 19 villages in the Western and Central Amazon to determine the level of consensus on the abundance of hunted and non-hunted forest species. These villages varied in size, socio-economic characteristics and in the experience with wildlife of their dwellers. Interviewees estimated the relative abundance of 101 species with a broad spectrum of bioecological characteristics using a four-point Likert scale. High consensus was found for species population abundance in all sampled villages and for 79.6% of interviewees. The village consensus of all species abundance pooled was negatively correlated with village population size. The consensus level was high regardless of the interviewees' hunting experience. Species that are more frequently hunted or are more apparent had greater consensus values; only two species presented a low consensus level, which are rare and solitary species. We show in our study in the Amazon that information gathered by local peoples, Indigenous as well as non-Indigenous, can be useful in understanding the status of animal species found within their environment. The high level of cultural consensus we describe likely arises from knowledge sharing and the strong connection between the persons interviewed and the forest. We suggest that consensus analysis can be used to validate LEK-generated data instead of comparing these types of data with information obtained by conventional methods

Congruence of local ecological knowledge (LEK)-based methods and line-transect surveys in estimating wildlife abundance in tropical forests

Effective estimation of wildlife population abundance is an important component of population monitoring, and ultimately essential for the development of conservation actions. Diurnal line-transect surveys are one of the most applied methods for abundance estimations. Local ecological knowledge (LEK) is empirically acquired through the observation of ecological processes by local people. LEK-based methods have only been recognized as valid scientific methods for surveying fauna abundance in the last three decades. However, the agreement between both methods has not been extensively analysed. We compared concomitant abundance data for 91 wild species (mammals, birds and tortoises) from diurnal line transects (9,221 km of trails) and a LEK-based method (291 structured interviews) at 18 sites in Central and Western Amazonia. We used biological and socioecological factors to assess the agreements and divergences between abundance indices obtained from both methods. We found a significant agreement of population abundance indices for diurnal and game species. This relationship was also positive regardless of species sociality (solitary or social), body size and locomotion mode (terrestrial and arboreal); and of sampled forest type (upland and flooded forests). Conversely, we did not find significant abundance covariances for nocturnal and non-game species. Despite the general agreement between methods, line transects were not effective at surveying many species occurring in the area, with 40.2% and 39.8% of all species being rarely and never detected in at least one of the survey sites. On the other hand, these species were widely reported by local informants to occur at intermediate to high abundances. Although LEK-based methods have been long neglected by ecologists, our comparative study demonstrated their effectiveness for estimating vertebrate abundance of a wide diversity of taxa and forest environments. This can be used simultaneously with line-transect surveys to calibrate abundance estimates and record species that are rarely sighted during surveys on foot, but that are often observed by local people during their daily extractive activities. Thus, the combination of local and scientific knowledge is a potential tool to improve our knowledge of tropical forest species and foster the development of effective strategies to meet biodiversity conservation goals

Predicting animal abundance through local ecological knowledge: An internal validation using consensus analysis

Given the ongoing environmental degradation from local to global scales, it is fundamental to develop more efficient means of gathering data on species and ecosystems. Local ecological knowledge, in which local communities can consistently provide information on the status of animal species over time, has been shown to be effective. Several studies demonstrate that data gathered using local ecological knowledge (LEK)‐based methods are comparable with data obtained from conventional methods (such as line transects and camera traps). Here, we employ a consensus analysis to validate and evaluate the accuracy of interview data on LEK. Additionally, we investigate the influence of social and bioecological variables on enhancing data quality. We interviewed 323 persons in 19 villages in the Western and Central Amazon to determine the level of consensus on the abundance of hunted and non‐hunted forest species. These villages varied in size, socio‐economic characteristics and in the experience with wildlife of their dwellers. Interviewees estimated the relative abundance of 101 species with a broad spectrum of bioecological characteristics using a four‐point Likert scale. High consensus was found for species population abundance in all sampled villages and for 79.6% of interviewees. The village consensus of all species abundance pooled was negatively correlated with village population size. The consensus level was high regardless of the interviewees' hunting experience. Species that are more frequently hunted or are more apparent had greater consensus values; only two species presented a low consensus level, which are rare and solitary species. We show in our study in the Amazon that information gathered by local peoples, Indigenous as well as non‐Indigenous, can be useful in understanding the status of animal species found within their environment. The high level of cultural consensus we describe likely arises from knowledge sharing and the strong connection between the persons interviewed and the forest. We suggest that consensus analysis can be used to validate LEK‐generated data instead of comparing these types of data with information obtained by conventional methods. Read the free Plain Language Summary for this article on the Journal blog

Participatory Mapping for Strengthening Environmental Governance on Socio-Ecological Impacts of Infrastructure in the Amazon: Lessons to Improve Tools and Strategies

The Amazon region has been viewed as a source of economic growth based on extractive industry and large-scale infrastructure development endeavors, such as roads, dams, oil and gas pipelines and mining. International and national policies advocating for the development of the Amazon often conflict with the environmental sector tasked with conserving its unique ecosystems and peoples through a sustainable development agenda. New practices of environmental governance can help mitigate adverse socio-economic and ecological effects. For example, forming a “community of practice and learning” (CoP-L) is an approach for improving governance via collaboration and knowledge exchange. The Governance and Infrastructure in the Amazon (GIA) project, in which this study is embedded, has proposed that fostering a CoP-L on tools and strategies to improve infrastructure governance can serve as a mechanism to promote learning and action on factors related to governance effectiveness. A particular tool used by the GIA project for generating and sharing knowledge has been participatory mapping (Pmap). This study analyzes Pmap exercises conducted through workshops in four different Amazonian regions. The goal of Pmap was to capture different perspectives from stakeholders based on their experiences and interests to visualize and reflect on (1) areas of value, (2) areas of concern and (3) recommended actions related to reducing impacts of infrastructure development and improvement of governance processes. We used a mixed-methods approach to explore textual analysis, regional multi-iteration discussion with stakeholders, participatory mapping and integration with ancillary geospatial datasets. We believe that by sharing local-knowledge-driven data and strengthening multi-actor dialogue and collaboration, this novel approach can improve day to day practices of CoP-L members and, therefore, the transparency of infrastructure planning and good governance

Participatory Mapping for Strengthening Environmental Governance on Socio-Ecological Impacts of Infrastructure in the Amazon: Lessons to Improve Tools and Strategies

The Amazon region has been viewed as a source of economic growth based on extractive industry and large-scale infrastructure development endeavors, such as roads, dams, oil and gas pipelines and mining. International and national policies advocating for the development of the Amazon often conflict with the environmental sector tasked with conserving its unique ecosystems and peoples through a sustainable development agenda. New practices of environmental governance can help mitigate adverse socio-economic and ecological effects. For example, forming a “community of practice and learning” (CoP-L) is an approach for improving governance via collaboration and knowledge exchange. The Governance and Infrastructure in the Amazon (GIA) project, in which this study is embedded, has proposed that fostering a CoP-L on tools and strategies to improve infrastructure governance can serve as a mechanism to promote learning and action on factors related to governance effectiveness. A particular tool used by the GIA project for generating and sharing knowledge has been participatory mapping (Pmap). This study analyzes Pmap exercises conducted through workshops in four different Amazonian regions. The goal of Pmap was to capture different perspectives from stakeholders based on their experiences and interests to visualize and reflect on (1) areas of value, (2) areas of concern and (3) recommended actions related to reducing impacts of infrastructure development and improvement of governance processes. We used a mixed-methods approach to explore textual analysis, regional multi-iteration discussion with stakeholders, participatory mapping and integration with ancillary geospatial datasets. We believe that by sharing local-knowledge-driven data and strengthening multi-actor dialogue and collaboration, this novel approach can improve day to day practices of CoP-L members and, therefore, the transparency of infrastructure planning and good governance

Predicting animal abundance through local ecological knowledge : An internal validation using consensus analysis

Given the ongoing environmental degradation from local to global scales, it is fundamental to develop more efficient means of gathering data on species and ecosystems. Local ecological knowledge, in which local communities can consistently provide information on the status of animal species over time, has been shown to be effective. Several studies demonstrate that data gathered using local ecological knowledge (LEK)-based methods are comparable with data obtained from conventional methods (such as line transects and camera traps). Here, we employ a consensus analysis to validate and evaluate the accuracy of interview data on LEK. Additionally, we investigate the influence of social and bioecological variables on enhancing data quality. We interviewed 323 persons in 19 villages in the Western and Central Amazon to determine the level of consensus on the abundance of hunted and non-hunted forest species. These villages varied in size, socio-economic characteristics and in the experience with wildlife of their dwellers. Interviewees estimated the relative abundance of 101 species with a broad spectrum of bioecological characteristics using a four-point Likert scale. High consensus was found for species population abundance in all sampled villages and for 79.6% of interviewees. The village consensus of all species abundance pooled was negatively correlated with village population size. The consensus level was high regardless of the interviewees' hunting experience. Species that are more frequently hunted or are more apparent had greater consensus values; only two species presented a low consensus level, which are rare and solitary species. We show in our study in the Amazon that information gathered by local peoples, Indigenous as well as non-Indigenous, can be useful in understanding the status of animal species found within their environment. The high level of cultural consensus we describe likely arises from knowledge sharing and the strong connection between the persons interviewed and the forest. We suggest that consensus analysis can be used to validate LEK-generated data instead of comparing these types of data with information obtained by conventional methods. Read the free Plain Language Summary for this article on the Journal blog