Performance and physiological responses of combined t-bar and PIT tagged giant mud crabs (Scylla serrata)

Mud crabs (Scylla spp.) are intensively caught throughout South-East Asia and support a very substantial commercial, recreational fishing and aquaculture industry. Identification of individual animals is important to improve understanding and management of this species. However, tagging of crustaceans is difficult as they frequently molt and internal tags can pose a hazard to consumers. In this pilot study we tested a new method combining passive integrated transponder tags and t-bar tags externally. 45 giant mud crabs (Scylla serrata) were captured from the wild and kept in tanks for a maximum of 10 months. We inserted tags into the abdomen of 35 giant mud crabs and tested a modified method where the combined t-bar/PIT-tag was inserted into the muscle tissue of the rear leg between the dorsal carapace plate and the top of the abdominal flap. Tagged crabs with the modified method showed 85% tag retention for molting crabs. We tested the same method in the field where 852 individuals were tagged with combined t-bar/PIT-tags of which 82 were recaptured showing 100% tag retention but without any evidence of molting having occurred. The tested method of combined t-bar/PIT-tags in giant mud crabs can further improve monitoring for wild and aquaculture populations and can be deployed widely with low cost.

Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.