Identifying thresholds of logging intensity on dung beetle communities to improve the sustainable management of Amazonian tropical forests

França, Filipe; Frazão, Fábio; Korasaki, Vanesca; Louzada, Júlio; Barlow, Jos

doi:10.1016/j.biocon.2017.10.014

Cited by 53 publications

(35 citation statements)

References 70 publications

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“…GAMs have been widely applied to investigate nonlinear relationships between dependent and non-explanatory variables in various fields of study such as plant and aquatic ecology (e.g., [58]) and water quality (e.g., [59]), as well as for nonlinear relationships among biotic and bon-biotic variables in environmental settings due to complex interactions between environmental factors. Many studies have identified nonlinear relationships among components of forest ecosystems (e.g., [3,50,[60][61][62][63][64][65]). In particular, Lee et al [3] reported that burn severity might be related to topographic characteristics such as elevation and slope.…”

Section: Linear and Nonlinear Model Estimationmentioning

confidence: 99%

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

2018

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Forest fires and burn severity mosaics have profound impacts on the post-fire dynamics and complexity of forest ecosystems. Numerous studies have investigated the relationship between topographic variables and susceptible tree covers with regard to burn severity. However, these relationships have not been fully elucidated, because most studies have assumed linearity in these relationships. Therefore, we examined the linearity and the nonlinearity in the relationships between topographic variables and susceptible tree covers with burn severity by comparing linear and nonlinear models. The site of the Samcheok fire, the largest recorded forest fire in Korea, was used as the study area. We generated 802 grid cells with a 500-m resolution that encompassed the entire study area and collected a dataset that included the topographic variables and percentage of red pine trees, which are the most susceptible tree cover types in Korea. We used conventional linear models and generalized additive models to estimate the linear and the nonlinear models based on topographic variables and Japanese red pine trees. The results revealed that the percentage of red pine trees had linear effects on burn severity, reinforcing the importance of silviculture and forest management to lower burn severity. Meanwhile, the topographic variables had nonlinear effects on burn severity. Among the topographic variables, elevation had the strongest nonlinear effect on burn severity, possibly by overriding the effects of susceptible fuels over elevation effects or due to the nonlinear effects of topographic characteristics on pre-fire fuel conditions, including the spatial distribution and availability of susceptible tree cover. To validate and generalize the nonlinear effects of elevation and other topographic variables, additional research is required at different fire sites with different tree cover types in different geographic locations.

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Section: Linear and Nonlinear Model Estimationmentioning

confidence: 99%

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

2018

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“…Harvesting natural tropical rainforest with selective cutting systems for timber production is the greatest driver of forest disturbance (Willot et al 2000;Franca et al 2017), which affects rainforest biodiversity and has disrupted the ecosystem processes (Barlow et al 2007;Ewers et al 2015). Logging causes significant shifts in community composition, distribution, and abundance of species (Edwards et al 2012;Franca et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Forest openness affects the abundance and species richness of invertebrates (Koivula and Niemela 2003;Thorn et al 2016). For example, sensitive dung beetle species may be lost following even low cutting intensity (Franca et al 2017). Natural tropical rainforest in eastern Borneo, Indonesia, has been logged with selective cutting system since 1970.…”

Section: Introductionmentioning

confidence: 99%

Temporal effects of cutting intensity on Diptera assemblages in eastern Borneo rainforest Indonesia

et al. 2020

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Abstract. Budiaman A, Haneda NF, Indahwati, Febrian D, Rahmah LN . 2020. Temporal effects of cutting intensity on Diptera assemblages in eastern Borneo rainforest Indonesia. Biodiversitas 21: 1074-1081. Studies on the effects of varying cutting intensity on the abundance and species richness of Diptera in tropical rainforest are limited, particularly in Southeast Asia region. The aim of the study was to assess the temporal effect of cutting intensity on Diptera community in tropical rainforest in the eastern Borneo rainforest, Indonesia, which was logged using the Indonesian Selective Cutting and Planting system. The field study was carried out in 2016. Responses of Diptera to the Indonesian Selective Cutting and Planting systems in the eastern Borneo rainforest, Indonesia were examined. We compared the abundance and morphospecies composition of Diptera before cutting and after cutting at three different treatments: low cutting intensity, medium cutting intensity and high cutting intensity. Diptera was collected using a malaise trap. Selective cutting of tropical rainforest altered biodiversity of Diptera. The abundance and morphospecies composition of Diptera were greater after cutting than before cutting at all cutting intensities. Our study showed that cutting intensity did not significantly affect the abundance and morphospecies composition of Diptera. Results of the study clearly indicated that the percentage of forest canopy cover could be a single predictor for abundance and morphospecies composition of Diptera in the natural rainforest of eastern Borneo, Indonesia.

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“…Therefore, using Landsat data to map and quantify selective logging at lower logging intensities (< 20 m 3 ha -1 ) remains a major challenge, and the amount of forest disturbance overlooked using currently available techniques is unknown. Yet, growing concerns over the impacts of selective logging on carbon and biodiversity (Bicknell et al, 2014;Edwards et al, 2014;França et al, 2017;Martin et al, 2015;Putz et al, 2008) has led to increased use of improved forest management practices, such as reducedimpact logging (Putz and Pinard, 1993). Consequently, the extent of tropical forests being logged at lower intensities and with reduced-impact is almost certainly expanding.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Map Tropical Selective Logging

Hethcoat

Edwards

Carreiras

et al. 2018

Preprint

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Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (>20 m 3 ha -1 ). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondônia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (< 15 m 3 ha -1 ). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Pará, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbonbased payments for ecosystem service schemes.

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Identifying thresholds of logging intensity on dung beetle communities to improve the sustainable management of Amazonian tropical forests

Cited by 53 publications

References 70 publications

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity

Temporal effects of cutting intensity on Diptera assemblages in eastern Borneo rainforest Indonesia

A Machine Learning Approach to Map Tropical Selective Logging

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