Predicting current and future disease outbreaks of Diplodia sapinea shoot blight in Italy: species distribution models as a tool for forest management planning

2017 ◽  
Vol 400 ◽  
pp. 655-664 ◽  
Author(s):  
Luciano Bosso ◽  
Nicola Luchi ◽  
Giorgio Maresi ◽  
Gennaro Cristinzio ◽  
Sonia Smeraldo ◽  
...  
Keyword(s):  
Forest Management ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Disease Outbreaks ◽  
Distribution Models ◽  
Management Planning ◽  
Forest Management Planning ◽  
Shoot Blight

scholarly journals A Lack of “Environmental Earth Data” at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 133 ◽  
Author(s):  
Emily L. Pascoe ◽  
Sajid Pareeth ◽  
Duccio Rocchini ◽  
Matteo Marcantonio
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Disease Outbreaks ◽  
Economic Consequences ◽  
Mitigation Strategies ◽  
Distribution Models ◽  
Data Gaps ◽  
Open Access Database ◽  
Temporal And Spatial

We currently live in an era of major global change that has led to the introduction and range expansion of numerous invasive species worldwide. In addition to the ecological and economic consequences associated with most invasive species, invasive arthropods that vector pathogens (IAVPs) to humans and animals pose substantial health risks. Species distribution models that are informed using environmental Earth data are frequently employed to predict the distribution of invasive species, and to advise targeted mitigation strategies. However, there are currently substantial mismatches in the temporal and spatial resolution of these data and the environmental contexts which affect IAVPs. Consequently, targeted actions to control invasive species or to prepare the population for possible disease outbreaks may lack efficacy. Here, we identify and discuss how the currently available environmental Earth data are lacking with respect to their applications in species distribution modeling, particularly when predicting the potential distribution of IAVPs at meaningful space-time scales. For example, we examine the issues related to interpolation of weather station data and the lack of microclimatic data relevant to the environment experienced by IAVPs. In addition, we suggest how these data gaps can be filled, including through the possible development of a dedicated open access database, where data from both remotely- and proximally-sensed sources can be stored, shared, and accessed.

scholarly journals SPATIAL SCALE EFFECTS OF SAMPLING ON THE INTERPOLATION OF SPECIES DISTRIBUTION MODELS IN THE SOUTHWESTERN AMAZON

2016 ◽  
Vol 40 (4) ◽  
pp. 617-625 ◽  
Author(s):  
Symone Maria de Melo Figueiredo ◽  
Eduardo Martins Venticinque ◽  
Evandro Orfanó Figueiredo
Keyword(s):  
Forest Management ◽  
Spatial Scale ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Scale Effects ◽  
Projection Area ◽  
Distribution Models ◽  
Forest Inventories ◽  
Occurrence Data

ABSTRACT Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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