Plankton Biomass Models Based on GIS and Remote Sensing Technique for Predicting Marine Megafauna Hotspots in the Solor Waters

Putra, M. Syukur Wahyu; Lewis, Sarah A.; Kurniasih, Eka; Prabuning, Derta; Faiqoh, Elok

doi:10.1088/1755-1315/47/1/012015

Cited by 8 publications

(12 citation statements)

References 25 publications

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“…tidal mixing) in coastal areas. These studies agree that the manta ray habitat preference is associated with near-shore shallow productive waters driven by coastal upwelling and tidal currents that bring high density ZB (Putra et al, 2016). Previous studies also recognized near-shore, highly productive shallow waters in the Mexican Caribbean as a preferred habitat of manta rays (Hacohen-Domené, Martínez-Rincón, Galván-Magaña, Cárdenas-Palomo, & Herrera-Silveira, 2017).…”

Section: Foraging Strategiessupporting

confidence: 79%

“…The Solor waters are characterized by complex bathymetry, internal tides, vertical mixing, and seasonal upwelling from deep waters, resulting in abundant food sources for marine organisms (Molcard, Fieux, & Syamsudin, 2001; Moore & Marra, 2002; Potemra, Sprintall, Hautala, & Pandoe, 2003). This region is very important for marine megafauna such as the cetaceans and mobulid rays that use this region seasonally for foraging (Putra, Lewis, Kurniasih, Prabuning, & Faiqoh, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…The final ZB was expressed per filtered water volume (mg m −3 ). The spatial analysis of ZB and DZBH in this study follows Putra et al (2016).…”

Section: Environmental Predictorsmentioning

confidence: 99%

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Incorporatingin situprey distribution into foraging habitat modelling for marine megafauna in the Solor waters of the Savu Sea, Indonesia

Putra¹,

Mustika

2020

Aquatic Conservation

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1. Many marine animals aggregate seasonally at predictable locations to exploit their prey in oligotrophic environments, making them vulnerable to human disturbance. 2. The foraging strategies and habitats of manta rays and Stenella spp. in the Solor waters were largely unknown, despite the importance of this information for their effective management, e.g. through the establishment of marine protected areas in this region. 3. Over the last few decades, the use of foraging habitat models in describing the foraging strategy and delineating the predicted habitats has grown in popularity. However, most of these studies have relied on remotely sensed data such as sea surface chlorophyll-a to estimate prey distribution. Very few studies have used the distribution of in situ prey, which is that closer to the trophic level of modelled species, although such a strategy would improve model performance. 4. The study compares foraging habitat model performance for manta rays and Stenella spp. in the waters of Solor using remotely sensed sea surface chlorophylla concentrations and in situ zooplankton biomass as proxies for prey quantities and distribution. A Maximum Entropy model that integrated species sightings with environmental predictors was used to quantify the importance of predictors in explaining habitat preference and distribution of manta rays and Stenella spp. and to compare model performance and predict foraging habitats. 5. Results indicate that the use of prey proxy closer to the trophic level of examined species improve model performance, ecological explanations, and spatial predictions. 6. The zooplankton biomass distribution performed much better in explaining the manta rays' habitats compared to that of Stenella spp., indicating that a trophic level gap might influence the zooplankton's ability to predict foraging habitats of Stenella spp. 7. This study highlights the importance of integrating foraging habitat models into marine protected area design to ensure the effectiveness of species management.

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Section: Foraging Strategiessupporting

confidence: 79%

Section: Methodsmentioning

confidence: 99%

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Incorporatingin situprey distribution into foraging habitat modelling for marine megafauna in the Solor waters of the Savu Sea, Indonesia

Putra¹,

Mustika

2020

Aquatic Conservation

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“…Mesoscale physical processes (i.e., eddies, fronts, and plumes) enhance the concentration of phytoplankton within the area and, in turn, trigger the formation of krill as essential food for larger cetaceans (Bailleul, Cotté, & Guinet, 2010; Ballance et al, 2006). In our study area, the dolphins in the Lamakera Strait were distributed in productivity hotspots and in areas close to the transition zone of fronts and eddies, indicating foraging activities (Putra et al, 2016). Our paper confirms this finding, indicating that the Stenella longirostris habitat preference in the Waibalun Bay during September–November was likely due to higher SSC‐ a concentrations.…”

Section: Discussionmentioning

confidence: 78%

Maximum entropy model: Estimating the relative suitability of cetacean habitat in the northern Savu Sea, Indonesia

Putra¹,

Mustika

2020

Marine Mammal Science

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“…Post-hoc analyses were conducted with a Tukey HSD, when p < 0.05. An optimized hot spot analysis was used to calculate the Gi* statistic, measuring the z-score of each point corresponding to other points as a relative clustering measure to indicate an affinity or habituation to a particular location (Getis and Ord, 1992, Love et al 2015, Putra et al 2016. Area utilized was estimated for the male and female by a minimum bounding convex hull around all GPS points.…”

Section: (A) (B)mentioning

confidence: 99%

Interpreting Feral Goat (Capra hircus) Movement to Guide Management in a Mesic Watershed on Oʻahu, Hawaiʻi

Char¹,

Leary²,

Litton³

2018

vertebrate_pest_conference

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Endemic Hawaiian ecosystems evolved without ungulate herbivores. Feral goats, a common nonnative ungulate on Pacific Islands, browse and trample vegetation which can denude watersheds and expose them to soil erosion. Since 1993, the Hawaiʻi Division of Forestry and Wildlife has actively suppressed feral goat populations to protect critical watersheds in a 1600-ha management area on Mount Kaʻala in the leeward Waiʻanae mountain range of Oʻahu. Despite these ongoing efforts, total eradication has not been achieved to date. This study examined the movement patterns of one male and one female feral goat in this management area over one year in 2015-16 with GPS collars. The primary objective was to determine if feral goat movements are related to seasonality, moon phase and precipitation to inform future management efforts. The male goat exhibited a large range, extending out 2.5 km from its starting point and over an 800m elevation range within the year, traveling to lower elevations during the summer months. The female goat, in turn, had a much smaller range and stayed relatively close to its initial starting point. The male goat exhibited greater movement at night than during the day, and this was particularly evident during full moon phases; animals also exhibited aversion to movement during precipitation events. Hot spot analyses showed that the female was largely confined to one location throughout the course of the study, while the male moved to multiple locations but displayed a strong affinity to the eastern portion of the management area in the Fall season. These initial findings help build knowledge on local patterns of feral goat movement within this managed area that can be used for control efforts. GPS collars provided useful high-resolution data to support further adoption of this technology to better inform management decisions. Future management strategies should incorporate longerterm movement data sets from multiple individuals to better understand habituation (e.g., return to favored locations), patterns of sexual segregation and rutting. This would allow a more demographic approach for maintaining populations below levels that are considered detrimental to this critical habitat.

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Plankton Biomass Models Based on GIS and Remote Sensing Technique for Predicting Marine Megafauna Hotspots in the Solor Waters

Cited by 8 publications

References 25 publications

Incorporatingin situprey distribution into foraging habitat modelling for marine megafauna in the Solor waters of the Savu Sea, Indonesia

Incorporatingin situprey distribution into foraging habitat modelling for marine megafauna in the Solor waters of the Savu Sea, Indonesia

Maximum entropy model: Estimating the relative suitability of cetacean habitat in the northern Savu Sea, Indonesia

Interpreting Feral Goat (Capra hircus) Movement to Guide Management in a Mesic Watershed on Oʻahu, Hawaiʻi

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