Modelling the probability of capture for New Zealand's longfin eels ('Anguilla dieffenbachii') and shortfin eels ('Anguilla australis')

<p>Longfin eel and shortfin eel probability of capture models can be used to build probability of capture maps. These maps can help identify eel encounter hotspots in New Zealand and are useful for managing and conserving the species. This research models longfin eel and shortfin eel presence/absence data using regularized random forest (RRF) models, vectorautoregressive spatial-temporal (VAST) models and Bayesian Gaussian random field (GRaF) models. Probability of capture maps built under VAST and GRaF remain approximately consistent with the maps built under RRF models. That is, longfin eels have high probabilities of capture around the coast of New Zealand’s North Island and have low probabilities of capture throughout the centre of New Zealand’s South Island. Shortfin eels have high probabilities of capture in small isolated regions of New Zealand’s North Island and have very low probabilities of capture throughout most of New Zealand’s South Island. Cross validation and spatial cross validation was used to compare the models. Cross validation results show that, compared to RRF models, VAST models improve predictive accuracy for the longfin eel and shortfin eel. Whereas, GRaF only improves predictive performance for the longfin eel. However, spatial cross validation shows no significant difference between VAST and RRF models. Hence, VAST models have higher predictive accuracy than RRF models for the longfin eel and shortfin eel when the training set is spatially correlated to the test set.</p>

Modelling the probability of capture for New Zealand's longfin eels ('Anguilla dieffenbachii') and shortfin eels ('Anguilla australis')

<p>Longfin eel and shortfin eel probability of capture models can be used to build probability of capture maps. These maps can help identify eel encounter hotspots in New Zealand and are useful for managing and conserving the species. This research models longfin eel and shortfin eel presence/absence data using regularized random forest (RRF) models, vectorautoregressive spatial-temporal (VAST) models and Bayesian Gaussian random field (GRaF) models. Probability of capture maps built under VAST and GRaF remain approximately consistent with the maps built under RRF models. That is, longfin eels have high probabilities of capture around the coast of New Zealand’s North Island and have low probabilities of capture throughout the centre of New Zealand’s South Island. Shortfin eels have high probabilities of capture in small isolated regions of New Zealand’s North Island and have very low probabilities of capture throughout most of New Zealand’s South Island. Cross validation and spatial cross validation was used to compare the models. Cross validation results show that, compared to RRF models, VAST models improve predictive accuracy for the longfin eel and shortfin eel. Whereas, GRaF only improves predictive performance for the longfin eel. However, spatial cross validation shows no significant difference between VAST and RRF models. Hence, VAST models have higher predictive accuracy than RRF models for the longfin eel and shortfin eel when the training set is spatially correlated to the test set.</p>

Growth of stunted elver of the Indonesian shortfin eel Anguilla bicolor McClelland, 1844 rearing in semi-natural media

This study aimed to evaluate the growth of stunted elver Anguilla bicolor from three different periods of stunting process. Prior to experiment, the stunting process of elver was carried out by limiting the feeding rate at 2% of the fish biomass and temperature media with 24 oC for 2, 4, and 6 months period. The study used a completely randomized design with three different sources of stunted elvers (i.e., A = 2 months; B = 4 months; C = 6 months) as treatments with three replications. Measurement of RNA:DNA ratio, protein content, profile blood description and elver otolith growth was carried out at the beginning and end of rearing process. Post-stunting elvers were reared on artificial media designed according to elver habitat and controlled. Elver was stocked with 15 individuals in the artificial semi-natural media with a temperature of 28 oC and fed with 1 kg media-1 day-1 tubifex worm for three months. The results showed that the compensatory growth of stunted elvers increased two to three times with 100% of survival rate after three months of rearing process. Stunting has a significant effect on treatment B, indicated by the value of weight specific growth rates, RNA : DNA ratio, protein content, blood glucose levels and otolith growth of elvers. This study revealed that the growth performance of stunted elver reared in semi-natural media for four months is better than other treatments.

Pengaruh Pemberian Pakan Buatan, Pakan Alami, dan Kombinasinya terhadap Pertumbuhan, Rasio Konservasi Pakan dan Tingkat Kelulushidupan Ikan Sidat (Anguilla bicolor) [Effect Of Artificial Feed, Natural Feed And Combination Between Them To Growth Rate, Food Convertion Ratio And Survival Rate Of Indonesian Shortfin Eel (Anguilla bicolor) ]

Indonesian shortfin eel (Anguilla bicolor) is one of kind of fish sold in the International markets especially for Japan and Korea, so this fish has the potential as an export commodity. Indonesian shortfin eel has a high nutrient content. Indonesian shortfin eel reached the size of concumption when it is weighted 120-500 grams. The purpose of this research was to know the effect of artifial feed, natural feed and combination between them to growth rate, food convertion ratio and survival rate of Indonesian shortfin eel (Anguilla bicolor). This research used experimental method and Completely Random Design method with five treatments. Each treatment was replicated four times. The result of this research showed that artificial feed, natural feed and combination between them were significantly difference (p<0,05) on growth rate of body wight and food convertion ratio but not significantly difference (p>0,05) on survival rate of Indonesian shortfin eel. The best growth in treatment E (1.72%), then a row followed by treatment A (1.51%), B (1.29%), D (1.25%) and C (1.25%) . The lowest feed conversion ratio obtained in treatment E (6.73) and highest feed conversion ratio obtained in treatment C (9.91). Survival rate obtained was 100%. Water quality of maintenance media of eel was the temperature 28-31°C, pH 7-8,5, dissolve oxygen 3,5-5,8 mg/l and ammonia 0,003 mg/l.