scholarly journals SPECIES DISTRIBUTION MODELLING OF TWO SPECIES ENDEMIC TO THE PHILIPPINES TO SHOW THE APPLICABILITY OF MAXENT

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 449-454
Author(s):  
M. Z. G. Untalan ◽  
D. F. M. Burgos ◽  
K. P. Martinez
Keyword(s):  
Species Distribution ◽  
Endemic Species ◽  
Species Distribution Model ◽  
Species Distribution Modelling ◽  
The Philippines ◽  
Distribution Model ◽  
Distribution Modelling ◽  
Machine Learning Model ◽  
Monitor Lizard ◽  
Bioclimatic Variables

Abstract. Maxent is a machine learning model used for species distribution modelling (SDM) that is rising in popularity. As with any species distribution model, it needs to be validated for certain species before being used to generate insights and trusted predictions. Using Maxent, SDM of two endemic species in the Philippines, Varanus palawanensis (Palawan monitor lizard) and Caprimulgus manillensis (Philippine nightjar), were created using presence-only data, with 14 V. palawanensis and 771 C. manillensis occurrences, and 19 bioclimatic variables from BIOCLIM. This study shows the consistency to historical facts of Maxent on two endemic species of the Philippines of varying nature. The applicability of Maxent on the two very different species show that Maxent has high likelihood to give good results for other species. Showing that Maxent is applicable to the species of the Philippines gives additional tools for ecologists and national administrators to lead the development of the Philippines in the direction that conserves the biodiversity of the Philippines and that increases the productivity and quality of life in the Philippines.

scholarly journals embarcadero: Species distribution modelling with Bayesian additive regression trees in R

2019 ◽  
Author(s):  
Colin J. Carlson
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Regression Trees ◽  
Species Distribution Modelling ◽  
Classification And Regression Tree ◽  
Transmission Risk ◽  
Distribution Model ◽  
Distribution Modelling ◽  
Additive Regression ◽  
Bayesian Additive Regression Trees

embarcadero is an R package of convenience tools for species distribution modelling with Bayesian additive regression trees (BART), a powerful machine learning approach that has been rarely applied to ecological problems. Like other classification and regression tree methods, BART estimates the probability of a binary outcome based on a set of decision trees. Unlike other methods, BART iteratively generates sets of trees based on a set of priors about tree structure and nodes, and builds a posterior distribution of estimated classification probabilities. So far, BARTs have yet to be applied to species distribution modelling. embarcadero is a workflow wrapper for BART species distribution models, and includes functionality for easy spatial prediction, an automated variable selection procedure, several types of partial dependence visualization, and other tools for ecological application. The embarcadero package is available open source on Github and intended for eventual CRAN release. To show how embarcadero can be used by ecologists, I illustrate a BART workflow for a virtual species distribution model. The supplement includes a more advanced vignette showing how BART can be used for mapping disease transmission risk, using the example of Crimean-Congo haemorrhagic fever in Africa.

scholarly journals Data quantity is more important than its spatial bias for predictive species distribution modelling

2020 ◽  
Author(s):  
Willson Gaul ◽  
Dinara Sadykova ◽  
Hannah J. White ◽  
Lupe León-Sánchez ◽  
Paul Caplat ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Species Distribution Modelling ◽  
Prediction Performance ◽  
Training Data ◽  
Distribution Model ◽  
Spatial Bias ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Modelling Method

ABSTRACTBiological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of 1) spatial bias in training data, 2) sample size (the average number of observations per species), and 3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but only when the bias was relatively strong. Sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

scholarly journals Data quantity is more important than its spatial bias for predictive species distribution modelling

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10411
Author(s):  
Willson Gaul ◽  
Dinara Sadykova ◽  
Hannah J. White ◽  
Lupe Leon-Sanchez ◽  
Paul Caplat ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Species Distribution Modelling ◽  
Prediction Performance ◽  
Training Data ◽  
Distribution Model ◽  
Spatial Bias ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Modelling Method

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average number of observations per species), and (3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

scholarly journals A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Billy Joel M. Almarinez ◽  
Mary Jane A. Fadri ◽  
Richard Lasina ◽  
Mary Angelique A. Tavera ◽  
Thaddeus M. Carvajal ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Habitat Suitability ◽  
Species Distribution Model ◽  
Biological Control Agent ◽  
The Philippines ◽  
Distribution Model ◽  
Field Surveys ◽  
Probability Of Occurrence ◽  
Precipitation Seasonality ◽  
Bioclimatic Variables

Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.

Bioclimatic variables derived from remote sensing: assessment and application for species distribution modelling

2014 ◽  
Vol 5 (10) ◽  
pp. 1033-1042 ◽  
Author(s):  
Eric Waltari ◽  
Ronny Schroeder ◽  
Kyle McDonald ◽  
Robert P. Anderson ◽  
Ana Carnaval
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Distribution Modelling ◽  
Bioclimatic Variables

scholarly journals Evaluating the influence of spatially varying catchability on multispecies distribution modelling

2020 ◽  
Vol 77 (5) ◽  
pp. 1841-1853
Author(s):  
Chongliang Zhang ◽  
Yong Chen ◽  
Binduo Xu ◽  
Ying Xue ◽  
Yiping Ren
Keyword(s):  
Species Distribution ◽  
Predictive Power ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Species Response ◽  
Challenges And Opportunities ◽  
Empirical Tests ◽  
Spatially Varying

Abstract Varying catchability is a common feature in fisheries and has great impacts on fisheries assessments and species distribution models. However, spatial variations in catchability have been rarely evaluated, especially in the multispecies context. We advocate that the need for multispecies models stands for both challenges and opportunities to handle spatial catchability. This study evaluated the influence of spatially varying catchability on the performance of a novel joint species distribution model, namely Hierarchical Modelling of Species Communities (HMSC). We implemented the model under nine simulation scenarios to account for diverse spatial patterns of catchability and conducted empirical tests using survey data from Yellow Sea, China. Our results showed that ignoring variability in catchability could lead to substantial errors in the inferences of species response to environment. Meanwhile, the models’ predictive power was less impacted, yielding proper predictions of relative abundance. Incorporating a spatially autocorrelated structure substantially improved the predictability of HMSC in both simulation and empirical tests. Nevertheless, combined sources of spatial catchabilities could largely diminish the advantage of HMSC in inference and prediction. We highlight situations where catchability needs to be explicitly accounted for in modelling fish distributions, and suggest directions for future applications and development of JSDMs.

scholarly journals Is a species distribution model output reliable to test niche limitation at the northern range limit of a Pacific coastal dune plant?

2021 ◽  
Author(s):  
Jacqueline Grubel
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Range Limits ◽  
Distribution Model ◽  
Range Limit ◽  
Endemic Plant ◽  
Niche Limitation ◽  
Bioclimatic Variables ◽  
Northern Range Limit ◽  
Dune Plant

Jacqueline Grubel* and Christopher G. Eckert (Faculty Supporter) It is widely thought that the size, shape and location of a species’ geographical distribution are a spatial expression of its realized niche, and this assumption is central to evolutionary biology, biogeography and conservation. Yet, the hypothesis that geographical range limits are niche limits is not well supported by experimental translocations of species beyond their range limits. Beyond range populations often exhibit fitness high enough for self-replacement. In contrast, environmental niche models based on bioclimatic data often suggest a decline in habitat suitability beyond range limits, thereby supporting niche limitation. However very few studies have evaluated whether species distribution models (SDMs) accurately predict the viability of populations in nature, and scant results to date are not supportive. Long-term transplant with the short-lived, Pacific costal dune endemic plant Camissoniopsis cheiranthifolia (Onagraceae) suggest that populations are viable beyond the northern range limit over multiple generations. We constructed an SDM based on a large range-wide database of species records plus standard bioclimatic variables and substrate type. We also included sea surface temperature, which greatly modifies the climate of dune habitat. Preliminary results suggest that our SDM reliably predicts the fitness of experimental populations. However, both approaches indicate that something other than niche limitation enforces the northern range limit of this species. Results from this well-studied dune plant suggest that range limitation via constraints on dispersal may play an important role in limiting northern range expansion.

scholarly journals Incorporating uncertainty in predictive species distribution modelling

2012 ◽  
Vol 367 (1586) ◽  
pp. 247-258 ◽  
Author(s):  
Colin M. Beale ◽  
Jack J. Lennon
Keyword(s):  
Species Distribution ◽  
Goodness Of Fit ◽  
Spatial Scales ◽  
Predictive Performance ◽  
Species Distribution Modelling ◽  
Control Measures ◽  
Distribution Model ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Demographic Models

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

scholarly journals Climate change impacts on migration of Pinus koraiensis during the Quaternary using species distribution models

Plant Ecology ◽  
2021 ◽  
Author(s):  
Takuto Shitara ◽  
Shunsuke Fukui ◽  
Tetsuya Matsui ◽  
Arata Momohara ◽  
Ikutaro Tsuyama ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Eastern China ◽  
Disjunct Distribution ◽  
Distribution Model ◽  
Pinus Koraiensis ◽  
Japanese Archipelago ◽  
Bioclimatic Variables ◽  
The Japanese Archipelago ◽  
Suitable Habitats

AbstractClarifying the influences of paleoclimate changes on the disjunct distribution formation of plants allows a historical and mechanical understanding of current vegetation and biodiversity. This study investigated the influences of paleoclimate changes on the present disjunct distribution formation of Pinus koraiensis (Korean pine) using species distribution modeling. A species distribution model (SDM) was built using maximum entropy principle algorithms (MaxEnt), data from 152 occurrences of the species, and four bioclimatic variables at 2.5 arcminute (approximately 5 km) spatial resolution. The simulation revealed the excellent fit of the MaxEnt model performance, with an area under the curve (AUC) value of 0.922 and continuous Boyce index (BCI) value of 0.925 with fivefold cross-validation. The most important climatic factor was the minimum temperature of the coldest month. Suitable habitats for the species ranged between − 30.1 and − 4.1 °C. Projected suitable habitats under the Last Glacial Maximum (approximately 22,000 years ago [ka BP]: LGM) period showed wide distributions in eastern China, the southern part of the Korean Peninsula, and the Japanese Archipelago. After the mid-Holocene (approximately 6 ka BP), the suitable habitats expanded northwards in continental regions and retreated from both north and southwest of Japan. This eventually formed disjunct suitable habitats in central Japan. An increase in temperature after the LGM period caused the migration of P. koraiensis toward new, suitable habitats in continental Northeast Asia, while species in the Japanese Archipelago retreated, forming the present disjunct distributions.

scholarly journals Negros Bleeding-heart Gallicolumba keayi prefers dense understorey vegetation and dense canopy cover, and species distribution modelling shows little remaining suitable habitat

2020 ◽  
pp. 1-16
Author(s):  
HOLLY MYNOTT ◽  
MARK ABRAHAMS ◽  
DAPHNE KERHOAS
Keyword(s):  
Species Distribution ◽  
Bird Species ◽  
Canopy Cover ◽  
Species Distribution Modelling ◽  
The Philippines ◽  
Camera Traps ◽  
Suitable Habitat ◽  
Distribution Modelling ◽  
Understorey Vegetation ◽  
Natural Park

Summary The Philippines is a global biodiversity hotspot, with a large number of threatened bird species, one of which is the ‘Critically Endangered’ Negros Bleeding-heart Gallicolumba keayi. The aim of this study was to investigate the habitat preference of the Negros Bleeding-heart and undertake species distribution modelling to locate areas of conservation importance based on identified suitable habitat. A survey of 94 point-counts was undertaken and eight camera traps were deployed from May to August 2018 in the Northwest Panay Peninsula Natural Park, Panay, Philippines. Habitat variables (canopy cover, understorey cover, ground cover, elevation, presence of rattan Calamus or Daemonorops spp. and pandan Pandanus sp., tree diameter at breast height, and branching architecture were measured in 5 m-radius quadrats. To identify areas of potentially suitable habitat for the Negros Bleeding-heart, species distribution was modelled in MaxEnt using tree cover and elevation data on Panay and Negros. Using a Generalised Linear Model, Negros Bleeding-heart presence was found to be significantly positively associated with dense understorey cover and dense canopy cover. Species distribution modelling showed that the Northwest Panay Peninsula Natural Park is currently the most suitably located protected area for Negros Bleeding-heart conservation, while protected areas in Negros require further law enforcement. It is imperative that protection is continued in the Northwest Panay Peninsula Natural Park, and more survey effort is needed to identify other critical Negros Bleeding-heart populations, around which deforestation and hunting ban enforcement is strongly recommended.

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