Rocha Daniel G, de Barros Ferraz Katia Maria Paschoaletto Micchi, Gonçalves Lucas, Tan Cedric Kai Wei, Lemos Frederico G, Ortiz Carolina, Peres Carlos A, Negrões Nuno, Antunes André Pinassi, Rohe Fabio, Abrahams Mark, Zapata-Rios Galo, Teles Davi, Oliveira Tadeu, von Mühlen Eduardo M, Venticinque Eduardo, Gräbin Diogo M, Mosquera B Diego, Blake John, Lima Marcela Guimarães Moreira, Sampaio Ricardo, Percequillo Alexandre Reis, Peters Felipe, Payán Esteban, Borges Luiz Henrique Medeiros, Calouro Armando Muniz, Endo Whaldener, Pitman Renata Leite, Haugaasen Torbjørn, Silva Diego Afonso, de Melo Fabiano R, de Moura André Luis Botelho, Costa Hugo C M, Lugarini Camile, de Sousa Ilnaiara Gonçalves, Nienow Samuel, Santos Fernanda, Mendes-Oliveiras Ana Cristina, Del Toro-Orozco Wezddy, D'Amico Ana Rafaela, Albernaz Ana Luisa, Ravetta André, do Carmo Elaine Christina Oliveira, Ramalho Emiliano, Valsecchi João, Giordano Anthony J, Wallace Robert, Macdonald David W, Sollmann Rahel
Department of Wildlife, Fish, and Conservation Biology, University of California - Davis, Davis, CA, USA.
Grupo de Pesquisa em Ecologia e Conservação de Felinos na Amazônia, Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, AM, Brazil.
R Soc Open Sci. 2020 Apr 22;7(4):190717. doi: 10.1098/rsos.190717. eCollection 2020 Apr.
The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species.
亚马逊森林持续的高森林砍伐率和碎片化是其生物多样性面临的主要威胁。为了预测和缓解这些威胁，了解和预测物种如何应对快速变化的景观至关重要。短耳犬是亚马逊地区特有的唯一犬科动物，也是全球研究最少的野生犬类之一。我们在两个空间尺度上调查了短耳犬的栖息地关联。首先，我们使用了有史以来为短耳犬编制的最大记录数据库，并结合物种分布模型来绘制物种栖息地适宜性图，估计其分布范围，并预测整个亚马逊地区（区域尺度）因预计的森林砍伐而导致的物种分布变化。其次，我们使用系统的相机陷阱调查和占有率模型来研究森林覆盖和森林碎片化如何影响巴西南部亚马逊地区该物种的空间利用（局部尺度）。物种分布模型表明，短耳犬可能出现在亚马逊河南部大部分地区的广阔连续区域。然而，由于森林砍伐，预计到2027年（三代以内），短耳犬目前分布的约30%将丧失或栖息地适宜性急剧下降。在未受保护地区，这一比例可能达到该物种分布的40%，在亚马逊流域的一些河间地区（即被大河隔开的土地部分）超过60%。我们的局部尺度分析表明，森林的存在对短耳犬的空间利用有积极影响，而森林边缘的密度有负面影响。除了阐明短耳犬的生态学并细化其分布范围外，我们的结果强调森林砍伐在短时间内对该物种的保护构成严重威胁。因此，我们建议根据此处呈现的研究结果重新评估短耳犬目前在世界自然保护联盟红色名录中的地位（近危）。我们的研究例证了如何跨数据源和建模程序整合数据，以增进我们对研究相对较少的物种的了解。
