Conclusion: An Attempt to Describe the State of Habitat and Species Modeling Today

Drew, C. Ashton; Wiersma, Yolanda F.; Huettmann, Falk

doi:10.1007/978-1-4419-7390-0_15

Cited by 13 publications

(29 citation statements)

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“…Estimating species distributions in undersampled areas is a fundamental problem in ecology, biogeography, biodiversity conservation and natural resource management (Drew, Wiersma & Huettmann, 2011). That is specifically true for rare and difficult to be detected species and which are usually high on the conservation priority.…”

Section: Discussionmentioning

confidence: 99%

“…That is specifically true for rare and difficult to be detected species and which are usually high on the conservation priority. The use of SDMs and with machine learning has become the method for deriving such estimates (Guisan & Thuiller, 2005; Drew, Wiersma & Huettmann, 2011; Guisan et al, 2013) and could contribute to detect new and to confirm populations of rare species. However, the application of a few samples to project a distribution area widely beyond the sample range is a greater challenge and has rarely been attempted in the literature until recently.…”

Section: Discussionmentioning

confidence: 99%

“…However, the application of a few samples to project a distribution area widely beyond the sample range is a greater challenge and has rarely been attempted in the literature until recently. Only now have conservationists realized its substantial value for pro-active decision making in conservation management (see work by Ohse et al, 2009; Drew, Wiersma & Huettmann, 2011; Kandel et al, 2015 etc.). Our results based on AUC, violin plots for RIOs and spatial assessment of testing data (satellite tracking data and literature data) all suggest there are difference in the generalization performance of different modeling techniques (TreeNet, Random Forest, CART and Maxent).…”

Section: Discussionmentioning

confidence: 99%

“…Species distribution models (SDMs) are empirical ecological models that relate species observations to environmental predictors (Guisan & Zimmermann, 2000; Drew, Wiersma & Huettmann, 2011). SDMs have become an increasingly important and now essential tool in ecology, biogeography, evolution and, more recently, in conservation biology (Guisan et al, 2013), management (Cushman & Huettmann, 2010), impact assessments (Humphries & Huettmann, 2014) and climate change research (Lei et al, 2011; Mi, Huettmann & Guo, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…However, predictions on existing knowledge and data offer less progress. The model prediction and conservation frontier obviously sits in the unknown and to provide progress there (Breiman, 2001; Drew, Wiersma & Huettmann, 2011). …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

Huettmann

Guo

et al. 2017

PeerJ

Self Cite

194

108

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Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33), White-naped Crane (Grus vipio, n = 40), and Black-necked Crane (Grus nigricollis, n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

Huettmann

Guo

et al. 2017

PeerJ

Self Cite

194

108

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The winter distribution of the Spoon-billed Sandpiper Calidris pygmaeus

Zöckler¹,

Beresford²,

Bunting³

et al. 2016

Bird Conservation International

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SummaryDeclines in populations of the Critically Endangered Spoon-billed Sandpiper Calidris pygmaeus have been rapid, with the breeding population now perhaps numbering fewer than 120 pairs. The reasons for this decline remain unresolved. Whilst there is evidence that hunting in wintering areas is an important factor, loss of suitable habitat on passage and wintering areas is also of concern. While some key sites for the species are already documented, many of their wintering locations are described here for the first time. Their wintering range primarily stretches from Bangladesh to China. Comprehensive surveys of potential Spoon-billed Sandpiper wintering sites from 2005 to 2013 showed a wide distribution with three key concentrations in Myanmar and Bangladesh, but also regular sites in China, Vietnam and Thailand. The identification of all important nonbreeding sites remains of high priority for the conservation of the species. Here, we present the results of field surveys of wintering Spoon-billed Sandpipers that took place in six countries between 2005 and 2013 and present species distribution models which map the potential wintering areas. These include known and currently unrecognised wintering locations. Our maximum entropy model did not identify any new extensive candidate areas within the winter distribution, suggesting that most key sites are already known, but it did identify small sites on the coast of eastern Bangladesh, western Myanmar, and the Guangxi and Guangdong regions of China that may merit further investigation. As no extensive areas of new potential habitat were identified, we suggest that the priorities for the conservation of this species are habitat protection in important wintering and passage areas and reducing hunting pressure on birds at these sites.

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Linking ecosystems to public health based on combination of social and ecological systems

Khosravi Mashizi,

Sharafatmandrad

2024

Sci Rep

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Promotion of public health is one of the most important benefits of ecosystems. Nevertheless, the relationship between ecosystems and social health’ needs is not well understood. Therefore, a study was done to investigate the potential of natural (forests and rangelands) and artificial (urban parks and gardens) ecosystems in ensuring the five dimensions of public health (i.e. physical, mental, spiritual, social and environmental) in the social systems (urban and rural societies). Therefore, 47 health indicators were used in order to relate different ecosystems and social’ needs to five dimensions of public health through questionnaire. The results indicated that natural ecosystems had the greatest potential in providing mental, spiritual and environmental health due to ecological characteristics of wilderness and aesthetic. The artificial ecosystems had the greatest potential in providing physical and social health due to their easy access. However, there was a match between social health’ needs and ecosystem potential in the rural areas. The study highlighted the need for promotion of ecological indicators related to mental health in urban areas by enhancing silence and aesthetic in artificial ecosystems. Presented framework can provide comprehensive information on the weaknesses and strengths of different ecosystems to promote public health based on social needs and fixing the weaknesses of artificial ecosystems in urban areas.

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Conclusion: An Attempt to Describe the State of Habitat and Species Modeling Today

Cited by 13 publications

References 0 publications

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

The winter distribution of the Spoon-billed Sandpiper Calidris pygmaeus

Linking ecosystems to public health based on combination of social and ecological systems

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