SEAGRASS HABITAT SUITABILITY MAP AT MERAMBONG SHOAL, JOHOR: A PRELIMINARY STUDY USING MULTIBEAM ECHOSOUNDER AND MAXENT MODELLING

Abstract. In recent years, there has been an increasing interest to use high-resolution multibeam dataset and Species Distribution Modelling (SDM) for seagrass habitat suitability model. This requires a specific variable derived from multibeam data and in-situ seagrass occurrence samples. The purpose of this study was (1) to derive variables from multibeam bathymetry data to be used in seagrass habitat suitability model, (2) to produce seagrass habitat suitability model using Maximum Entropy (MaxEnt), and (3) to quantify the contribution of each variable for predicting seagrass habitat suitability map. The study area was located at Merambong Shoal, covering an area of 0.04 km2, situated along Johor Strait. First, twelve (12) variables were derived from bathymetry data collected from multibeam echosounder using Benthic Terrain Modeller (BTM) tool. Secondly, all variables and seagrass occurrence samples were integrated in MaxEnt to produce seagrass habitat suitability map. The results showed that the Area Under Curve (AUC) values based on training and test data were 0.88 and 0.65, respectively. The northwest region of survey area indicated higher habitat suitability of seagrass, while the southeast region of survey area indicated lower suitability. Bathymetry mean found to be the most contributed variables among others. The spatial distribution of seagrass from modelling technique agreed with the previous studies and they are found to be distributed at depths ranging from 2.2 to 3.4 meters whilst less suitable with increasing of water depth. This study concludes that seagrass habitat suitability map with high-resolution pixel size (0.5 meter) can be produced at Merambong Shoal using acoustic data from multibeam echosounder coupled with MaxEnt and underwater video observations.

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Maxent Data Mining Technique and Its Comparison with a Bivariate Statistical Model for Predicting the Potential Distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran

Sustainability ◽

10.3390/su11123452 ◽

2019 ◽

Vol 11 (12) ◽

pp. 3452 ◽

Cited By ~ 6

Author(s):

Marjaneh Mousazade ◽

Gholamabbas Ghanbarian ◽

Hamid Reza Pourghasemi ◽

Roja Safaeian ◽

Artemi Cerdà

Keyword(s):

Data Mining ◽

Statistical Model ◽

Habitat Suitability ◽

Environmental Variables ◽

Potential Distribution ◽

Data Mining Technique ◽

Mining Technique ◽

Suitability Map ◽

Habitat Suitability Map ◽

Edaphic Variables

The identification of geographical distribution of a plant species is crucial for understanding the importance of environmental variables affecting plant habitat. In the present study, the spatial potential distribution of Astragalus fasciculifolius Boiss. as a key specie was mapped using maximum entropy (Maxent) as data mining technique and bivariate statistical model (FR: frequency ratio) in marl soils of southern Zagros, Iran. The A. fasciculifolius locations were identified and recorded by intensive field campaigns. Then, localities points were randomly split into a 70% training dataset and 30% for validation. Two climatic, four topographic, and eight edaphic variables were used to model the A. fasciculifolius distribution and its habitat potential. Maps of environmental variables were generated using Geographic Information System (GIS). Next, the habitat suitability index (HSI) maps were produced and classified by means of Maxent and FR approaches. Finally, the area under the receiver operating characteristic (AUC-ROC) curve was used to compare the performance of maps produced by Maxent and FR models. The interpretation of environmental variables revealed that the climatic and topographic parameters had less impact compared to edaphic variables in habitat distribution of A. fasciculifolius. The results showed that bulk density, nitrogen, acidity (pH), sand, and electrical conductivity (EC) of soil are the most significant variables that affect distribution of A. fasciculifolius. The validation of results showed that AUC values of Maxent and FR models are 0.83 and 0.76, respectively. The habitat suitability map by the better model (Maxent) showed that areas with high and very high suitable classes cover approximately 22% of the study area. Generally, the habitat suitability map produced using Maxent model could provide important information for conservation planning and a reclamation project of the degraded habitat of intended plant species. The distribution of the plants identifies the water, soil, and nutrient resources and affects the fauna distribution, and this is why it is relevant to research and to understand the plant distribution to properly improve the management and to achieve a sustainable management.

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Identifying Potential Habitat for the Endangered Aleutian Shield Fern Using Topographical Characteristics

Journal of Fish and Wildlife Management ◽

10.3996/032012-jfwm-023 ◽

2012 ◽

Vol 3 (2) ◽

pp. 303-310

Author(s):

Adam Duarte ◽

Daniel M. Wolcott ◽

T. Edwin Chow ◽

Mark A. Ricca

Keyword(s):

Habitat Suitability ◽

Habitat Characteristics ◽

Geographical Information ◽

Slope Aspect ◽

Suitable Habitat ◽

Gis Model ◽

Potential Habitat ◽

Suitability Map ◽

Habitat Suitability Map ◽

The U.S

Abstract The Aleutian shield fern Polystichum aleuticum is endemic to the Aleutian archipelago of Alaska and is listed as endangered pursuant to the U.S. Endangered Species Act. Despite numerous efforts to discover new populations of this species, only four known populations are documented to date, and information is needed to prioritize locations for future surveys. Therefore, we incorporated topographical habitat characteristics (elevation, slope, aspect, distance from coastline, and anthropogenic footprint) found at known Aleutian shield fern locations into a Geographical Information System (GIS) model to create a habitat suitability map for the entirety of the Andreaonof Islands. A total of 18 islands contained 489.26 km2 of highly suitable and moderately suitable habitat when weighting each factor equally. This study reports a habitat suitability map for the endangered Aleutian shield fern using topographical characteristics, which can be used to assist current and future recovery efforts for the species.

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Challenges of predicting the potential distribution of a slow-spreading invader: a habitat suitability map for an invasive riparian tree

Biological Invasions ◽

10.1007/s10530-010-9798-4 ◽

2010 ◽

Vol 13 (1) ◽

pp. 153-163 ◽

Cited By ~ 51

Author(s):

Catherine S. Jarnevich ◽

Lindsay V. Reynolds

Keyword(s):

Habitat Suitability ◽

Potential Distribution ◽

Suitability Map ◽

Riparian Tree ◽

Slow Spreading ◽

Habitat Suitability Map

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Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes

PLoS ONE ◽

10.1371/journal.pone.0257761 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257761

Author(s):

Muhammad Abdul Hakim Muhamad ◽

Rozaimi Che Hasan ◽

Najhan Md Said ◽

Jillian Lean-Sim Ooi

Keyword(s):

Spatial Resolution ◽

Habitat Suitability ◽

Prediction Models ◽

Seagrass Habitat ◽

Multibeam Echosounder ◽

Marine Park ◽

Marine Habitats ◽

Fringing Reefs ◽

Analysis Window ◽

Resolution Dataset

Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.

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A tamarisk habitat suitability map for the continental United States

Frontiers in Ecology and the Environment ◽

10.1890/1540-9295(2006)004[0012:athsmf]2.0.co;2 ◽

2006 ◽

Vol 4 (1) ◽

pp. 11-17 ◽

Cited By ~ 88

Author(s):

Jeffrey T. Morisette ◽

Catherine S. Jarnevich ◽

Asad Ullah ◽

Weijie Cai ◽

Jeffrey A. Pedelty ◽

...

Keyword(s):

United States ◽

Habitat Suitability ◽

Suitability Map ◽

Habitat Suitability Map

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Challenges in updating habitat suitability models: An example with the lesser prairie-chicken

PLoS ONE ◽

10.1371/journal.pone.0256633 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0256633

Author(s):

Catherine S. Jarnevich ◽

Pairsa N. Belamaric ◽

Kent Fricke ◽

Mike Houts ◽

Liza Rossi ◽

...

Keyword(s):

Land Cover ◽

Habitat Suitability ◽

Negative Relationship ◽

Suitable Habitat ◽

Habitat Suitability Model ◽

Current Information ◽

Land Cover Data ◽

Prairie Chicken ◽

Anthropogenic Development ◽

Iteration Model

Habitat loss from land-use change is one of the top causes of declines in wildlife species of concern. As such, it is critical to assess and reassess habitat suitability as land cover and anthropogenic features change for both monitoring and developing current information to inform management decisions. However, there are obstacles that must be overcome to develop consistent assessments through time. A range-wide lek habitat suitability model for the lesser prairie-chicken (Tympanuchus pallidicinctus), currently under review by the U. S. Fish and Wildlife Service for potential listing under the Endangered Species Act, was published in 2016. This model was based on lek data from 2002 to 2012, land cover data ranging from 2001 to 2013, and anthropogenic features from circa 2011, and has been used to help guide lesser prairie-chicken management and anthropogenic development actions. We created a second iteration model based on new lek surveys (2015 to 2019) and updated predictors (2016 land cover and cleaned/updated anthropogenic data) to evaluate changes in lek suitability and to quantify current range-wide habitat suitability. Only three of 11 predictor variables were directly comparable between the iterations, making it difficult to directly assess what predicted changes resulted from changes in model inputs versus actual landscape change. The second iteration model showed a similar positive relationship with land cover and negative relationship with anthropogenic features to the first iteration, but exhibited more variation among candidate models. Range-wide, more suitable habitat was predicted in the second iteration. The Shinnery Oak Ecoregion, however, exhibited a loss in predicted suitable habitat that could be due to predictor source changes. Iterated models such as this are important to ensure current information is being used in conservation and development decisions.

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