Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest, USA

Yost, Andrew

doi:10.1016/j.ecolind.2006.12.003

Cited by 29 publications

(28 citation statements)

References 20 publications

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“…Regarding the proposed models, our results corroborated the spatial heterogeneity in the Chl-a distribution in the Lake Mangueira, as observed previously in other studies (CROSSETTI et al, 2013;FRAGOSO JUNIOR et al, 2011;2008).…”

Section: Performance Of Chl-a Algorithmssupporting

confidence: 79%

Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

Munar

Cavalcanti

Bravo

et al. 2018

RBRH

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Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.Keywords: Chl-a; MLR; ANN; NPRM; Remote sensing; Lake Mangueira. RESUMOA estimativa da concentração de Clorofila-a (Chl-a) em ambientes aquáticos através de técnicas de sensoriamento remoto é complexa devido as diferenças entre as propriedades óticas da água. O objetivo deste trabalho foi estimar concentrações de Chl-a a partir das combinações de bandas espectrais do sensor MODIS, aplicando análise de regressão linear múltipla (MLR), redes neurais artificiais (ANN), regressão multiplicativa não paramétrica e quatro modelos (Appel, Kahru, FAI e O14a). Os modelos de regressão linear múltipla (MLR), NPMR e ANN foram calibrados e validados utilizando medições in-situ de Clorofila-a. Os resultados demostraram que um simples e eficiente modelo desenvolvido e validado através de regressão linear múltipla (MLR) produz vantagens (i.e., melhor performance e poucas variáveis de entrada) em comparação com os modelos ANN, NPMR e quatro modelos (Appel, Kahru, FAI e O14a). Além disso, foi observado que em um grande lago raso subtropical, no qual o vento (intensidade e direção) e a hidrodinâmica são fatores essenciais na heterogeneidade espacial (distribuição de Chl-a), o modelo MLR desenvolvido utilizando um ponto específico do lago teve melhor performance que utilizando o total de dados, sugerindo que não seria apropriado generalizar um único modelo para estimar Clorofila-a nesses grandes lagos rasos utilizando o total de dados. Esta abordagem é uma ferramenta útil para estimar concentrações de Chl-a em ambientes rasos meso -oligotróficos e corroborar a heterogeneidade espacial desses ecossistemas. Can chlorophyll-a in meso-oligotrophic s...

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Section: Performance Of Chl-a Algorithmssupporting

confidence: 79%

Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

Munar

Cavalcanti

Bravo

et al. 2018

RBRH

View full text Add to dashboard Cite

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“…Neighbourhood size can be defined as the sum of weights or amounts of data used to calculate the estimated response at any given point, and the average neighbourhood size is the sum of neighbourhood sizes for all points divided by the total number of points. This value decreases with the increase in number of variables (Yost, 2008). The minimum neighbourhood size for an estimate therefore controls how broadly a model is extrapolated in the predictor space avoiding estimates of a response in a region of the predictor space with insufficient data.…”

Section: Species Distribution Modellingmentioning

confidence: 96%

A comparison of modelled and actual distributions of eleven benthic macroinvertebrate species in a Central European mountain catchment

et al. 2015

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Species distribution Modelling (SDM) constitutes a useful tool to predict the distribution of freshwater species based on selected habitat variables. Model performance (goodness-of-fit) expressed as coefficient of determination is straightforward to judge on SDM quality but does not sufficiently address predictive success. Hence, predictive performance (e.g. AUC) accounts for the correctness of predicted species occurrences. In this study, we compared the model and predictive performance of SDMs on eleven macroinvertebrate taxa in a mountain catchment, with emphasis on species prevalence. SDMs were based on two regression methods using broad-scale environmental predictors (land use, instream habitat quality). We applied a cross-validation and a field validation approach using newly sampled field data. Opposed to other species, SDMs showed acceptable performance (pseudo-R 2 [ 0.3) for the stonefly Dinocras cephalotes and the caddisflies Silo piceus and Silo pallipes.Model performance was neither positively nor linearly correlated with predictive accuracy. The comparison of cross-and field validation revealed an overestimation of the discriminatory power of cross-validated models. SDMs of less prevalent species tend to overpredict absences rather than presences. Consequently, model performance is decoupled from predictive performance. The validation results suggest the use of new field data providing a more reliable benchmark for SDM assessment.

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“…NPMR has the capacity to deal with quantitative and categorical variables and can define complex response surfaces using predictors in a multiplicative rather than in an additive way (McCune, 2006a). The technique has recently been applied to model the response of lichens to climate change (Ellis et al, 2007) and the distribution of tree species (Yost, 2008). NPMR was applied to 29 macroinvertebrate taxa from 134 sites using the software HyperNiche version 1.12 (McCune & Mefford, 2004).…”

Section: Statistical Analysesmentioning

confidence: 99%

Modelling lake macroinvertebrate species in the shallow sublittoral: relative roles of habitat, lake morphology, aquatic chemistry and sediment composition

et al. 2009

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Macroinvertebrates are one of the key components of lake ecosystems and are required to be monitored alongside other biological groups to define ecological status according to European Union legislation. Macroinvertebrate communities are highly variable and complex and respond to a diverse series of environmental conditions. The purpose of this study was to examine the relative importance of environmental variables in explaining macroinvertebrate abundance. A total of 45 sub-alpine lakes were sampled for macroinvertebrates in the shallow sublittoral. Environmental variables were grouped into four types: (1) aquatic physical and chemical parameters, (2) littoral and riparian habitat, (3) lake morphometric parameters and (4) sediment chemical characteristics. Nonparametric multiplicative regression (NPMR) was used to model the abundance of individual macroinvertebrate taxa. Significant models were produced for nine out of the 24 taxa examined. Sediment characteristics were the group most frequently included in models and also the factors to which taxa abundance was the most sensitive. Aquatic physical and chemical variables were the next group most frequently included in models although chlorophyll a was not included in any of the models and total phosphorus in only one. This indicates that many taxa may not show a direct easily interpretable response to eutrophication pressure. Lake morphometric factors were included in several of the models although the sensitivity of macroinvertebrate abundance tended to be lower than for sediment and aquatic physical and chemical factors. Habitat factors were only included in three models although riparian vegetation was found to have a significant influence on the abundance of Ephemera danica indicating that ecotone integrity is likely to play a role in its ecology. Overall, the models tended to be specific for species with limited commonality across taxa. Models produced by NPMR indicate that the response of macroinvertebrates to environmental variables can be successfully described but further research is required focussing in more detail on the response of key taxa to relevant environmental parameters and anthropogenic pressures

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Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest, USA

Cited by 29 publications

References 20 publications

Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

A comparison of modelled and actual distributions of eleven benthic macroinvertebrate species in a Central European mountain catchment

Modelling lake macroinvertebrate species in the shallow sublittoral: relative roles of habitat, lake morphology, aquatic chemistry and sediment composition

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