Fauna community trends during early restoration of alluvial open forest/woodland ecosystems on former agricultural land

Vertebrate fauna was studied over 10 years following revegetation of a Eucalyptus tereticornis ecosystem on former agricultural land. We compared four vegetation types: remnant forest, plantings of a mix of native tree species on cleared land, natural regeneration of partially cleared land after livestock removal, and cleared pasture land with scattered paddock trees managed for livestock production. Pasture differed significantly from remnant in both bird and nonbird fauna. Although 10 years of ecosystem restoration is relatively short term in the restoration process, in this time bird assemblages in plantings and natural regeneration had diverged significantly from pasture, but still differed significantly from remnant. After 10 years, 70 and 66% of the total vertebrate species found in remnant had been recorded in plantings and natural regeneration, respectively. Although the fauna assemblages within plantings and natural regeneration were tracking toward those of remnant, significant differences in fauna between plantings and natural regeneration indicated community development along different restoration pathways. Because natural regeneration contained more mature trees (dbh > 30 cm), native shrub species, and coarse woody debris than plantings from the beginning of the study, these features possibly encouraged different fauna to the revegetation areas from the outset. The ability of plantings and natural regeneration to transition to the remnant state will be governed by a number of factors that were significant in the analyses, including shrub cover, herbaceous biomass, tree hollows, time since fire, and landscape condition. Both active and passive restoration produced significant change from the cleared state in the short term.

Hazard Function Models to Estimate Mortality Rates Affecting Fish Populations with Application to the Sea Mullet (Mugil cephalus) Fishery on the Queensland Coast (Australia)

Fisheries management agencies around the world collect age data for the purpose of assessing the status of natural resources in their jurisdiction. Estimates of mortality rates represent a key information to assess the sustainability of fish stocks exploitation. Contrary to medical research or manufacturing where survival analysis is routinely applied to estimate failure rates, survival analysis has seldom been applied in fisheries stock assessment despite similar purposes between these fields of applied statistics. In this paper, we developed hazard functions to model the dynamic of an exploited fish population. These functions were used to estimate all parameters necessary for stock assessment (including natural and fishing mortality rates as well as gear selectivity) by maximum likelihood using age data from a sample of catch. This novel application of survival analysis to fisheries stock assessment was tested by Monte Carlo simulations to assert that it provided unbiased estimations of relevant quantities. The method was applied to the data from the Queensland (Australia) sea mullet (Mugil cephalus) commercial fishery collected between 2007 and 2014. It provided, for the first time, an estimate of natural mortality affecting this stock: 0.22±0.08 year −1 .

Physical Oceanographic Influences on Queensland Reef Fish and Scallops : Final Report FRDC 2013/020

The project examined coastal and physical oceanographic influences on the catch rates of coral trout (Plectropomus leopardus) and saucer scallops (Amusium balloti) in Queensland. The research was undertaken to explain variation observed in the catches, and to improve quantitative assessment of the stocks and management advice. 3.1 OBJECTIVES 1. Review recent advances in the study of physical oceanographic influences on fisheries catch data, and describe the major physical oceanographic features that are likely to influence Queensland reef fish and saucer scallops. 2. Collate Queensland’s physical oceanographic data and fisheries (i.e. reef fish and saucer scallops) data. 3. Develop stochastic population models for reef fish and saucer scallops, which can link physical oceanographic features (e.g. sea surface temperature anomalies) to catch rates, biological parameters (e.g. growth, reproduction, natural mortality) and ecological aspects (e.g. spatial distribution).