The importance of scattered trees for biodiversity conservation: A global meta‐analysis

Prevedello, Jayme Augusto; Almeida‐Gomes, Mauricio; Lindenmayer, David B.

doi:10.1111/1365-2664.12943

Cited by 125 publications

(102 citation statements)

References 62 publications

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“…This is consistent with findings of previous studies on bats in forested landscapes (Adams & Hayes, 2008;Brigham, 2007;Holloway & Barclay, 2000). These results will be important in guiding conservation efforts for bats in landscapes where trees are commonly viewed as a negative feature, but may serve an important ecological function in the appropriate context (Prevedello et al, 2017). These results will be important in guiding conservation efforts for bats in landscapes where trees are commonly viewed as a negative feature, but may serve an important ecological function in the appropriate context (Prevedello et al, 2017).…”

Section: Discussionsupporting

confidence: 91%

“…The activity of three of four species (L. noctivagans, L. cinereus, and L. borealis) increased as landscape-level tree cover increased. Trees also provide foraging opportunities (Prevedello et al, 2017) and shelter from weather and predators (Verboom & Spoelstra, 1999). Many North American bats, including the four focal species, use trees for roosting (Harvey, Altenbach, & Best, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Trees are vital to the life histories of many North American bat species, as they are used during both roosting (Barclay & Kurta, 2007;Carter & Menzel, 2007) and foraging (Prevedello, Almeida-Gomes, & Lindenmayer, 2017). Similarly, water is important to bats for drinking, particularly because bats experience high evaporative water loss during day roosting (Adams & Hayes, 2008).…”

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confidence: 99%

“…We investigated variables that may provide roosting resources (trees, human-built structures) and foraging or drinking resources (trees, open water, herbaceous wetlands), and variables that may disrupt access to these resources (roads, row crops) (Adams & Hayes, 2008;Barclay & Kurta, 2007;Carter & Menzel, 2007;Prevedello et al, 2017;Zurcher, Sparks & Bennett, 2010). We investigated variables that may provide roosting resources (trees, human-built structures) and foraging or drinking resources (trees, open water, herbaceous wetlands), and variables that may disrupt access to these resources (roads, row crops) (Adams & Hayes, 2008;Barclay & Kurta, 2007;Carter & Menzel, 2007;Prevedello et al, 2017;Zurcher, Sparks & Bennett, 2010).…”

mentioning

confidence: 99%

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Habitat associations of bats in a working rangeland landscape

Trubitt

Hovick

Gillam

et al. 2018

Ecology and Evolution

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Land‐use change has resulted in rangeland loss and degradation globally. These changes include conversion of native grasslands for row‐crop agriculture as well as degradation of remaining rangeland due to fragmentation and changing disturbance regimes. Understanding how these and other factors influence wildlife use of rangelands is important for conservation and management of wildlife populations. We investigated bat habitat associations in a working rangeland in southeastern North Dakota. We used Petterson d500x acoustic detectors to systematically sample bat activity across the study area on a 1‐km point grid. We identified calls using Sonobat autoclassification software. We detected five species using this working rangeland, which included Lasionycteris noctivagans (2,722 detections), Lasiurus cinereus (2,055 detections), Eptesicus fuscus (749 detections), Lasiurus borealis (62 detections), and Myotis lucifugus (1 detection). We developed generalized linear mixed‐effects models for the four most frequently detected species based on their ecology. The activity of three bat species increased with higher tree cover. While the scale of selection varied between the four species, all three investigated scales were explanatory for at least one bat species. The broad importance of trees to bats in rangelands may put their conservation needs at odds with those of obligate grassland species. Focusing rangeland bat conservation on areas that were treed prior to European settlement, such as riparian forests, can provide important areas for bat conservation while minimizing negative impacts on grassland species.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Habitat associations of bats in a working rangeland landscape

Trubitt

Hovick

Gillam

et al. 2018

Ecology and Evolution

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“…In any case, our CNN-based approach could support the detection and monitoring of trees and arborescent shrubs in general, which has a huge relevance for biodiversity conservation and for reducing uncertainties in carbon accounting worldwide ( [60,61]). The presence of scattered trees have been recently highlighted as keystone structures capable of maintaining high levels of biodiversity and ecosystem services provision in open areas ( [62]). Global initiatives could greatly benefit from CNNs, such as those recently implemented by the United Nations Food and Agricultural Organization ( [60]) to estimate the overall extension of forests in drylands biomes, where they used the collaborative work of hundreds of people that visually explored hundreds of VHR images available from Google Earth to detect the presence of forests in drylands.…”

Section: Discussionmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).

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