Development of a Gulf community based natural resource monitoring program.

Development of a Gulf community based natural resource monitoring program, with sawfish as an initial focus.

Evaluation of aerial photography for predicting trends in structural attributes of Australian woodland including comparison with ground-based monitoring data

The accurate assessment of trends in the woody structure of savannas has important implications for greenhouse accounting and land-use industries such as pastoralism. Two recent assessments of live woody biomass change from north-east Australian eucalypt woodland between the 1980s and 1990s present divergent results. The first estimate is derived from a network of permanent monitoring plots and the second from woody cover assessments from aerial photography. The differences between the studies are reviewed and include sample density, spatial scale and design. Further analyses targeting potential biases in the indirect aerial photography technique are conducted including a comparison of basal area estimates derived from 28 permanent monitoring sites with basal area estimates derived by the aerial photography technique. It is concluded that the effect of photo-scale; or the failure to include appropriate back-transformation of biomass estimates in the aerial photography study are not likely to have contributed significantly to the discrepancy. However, temporal changes in the structure of woodlands, for example, woodlands maturing from many smaller trees to fewer larger trees or seasonal changes, which affect the relationship between cover and basal area could impact on the detection of trends using the aerial photography technique. It is also possible that issues concerning photo-quality may bias assessments through time, and that the limited sample of the permanent monitoring network may inadequately represent change at regional scales

Plant based monitoring for irrigation scheduling in vegetable horticulture: A case study in South Australian onions

Direct measurement of plant water status for irrigation scheduling may be more sensitive, and promote better horticultural crop quality, than indirect methods such as soil moisture monitoring. In our research project, we sought to identify instances where direct methods of plant-water status previously used in horticultural crops in Australia. We present the outcomes, suitability or obstacles for adoption by horticultural producers.

Phenology of Carpophilus spp. (Coleoptera: Nitidulidae)in stone fruit orchards as determined by pheromone trapping: Implications for prediction of crop damage

Traps baited with synthetic aggregation pheromone and fermenting bread dough were used to monitor seasonal incidence and abundance of the ripening fruit pests, Carpophilus hemipterus (L.), C. mutilatus Erichson and C. davidsoni Dobson in stone fruit orchards in the Leeton district of southern New South Wales during five seasons (1991-96). Adult beetles were trapped from September-May, but abundance varied considerably between years with the amount of rainfall in December-January having a major influence on population size and damage potential during the canning peach harvest (late February-March). Below average rainfall in December-January was associated with mean trap catches of < 10 beetles/trap/week in low dose pheromone traps during the harvest period in 1991/92 and 1993/94 and no reported damage to ripening fruit. Rainfall in December-January 1992/93 was more than double the average and mean trap catches ranged from 8-27 beetles/week during the harvest period with substantial damage to the peach crop. December-January rainfall was also above average in 1994/95 and 1995/96 and means of 50-300 beetles/trap/week were recorded in high dose pheromone traps during harvest periods. Carpophilus spp. caused economic damage to peach crops in both seasons. These data indicate that it may be possible to predict the likelihood of Carpophilus beetle damage to ripening stone fruit in inland areas of southern Australia, by routine pheromone-based monitoring of beetle populations and summer temperatures and rainfall.

Population dynamics of house mice in Queensland grain-growing areas

Context. Irregular plagues of house mice cause high production losses in grain crops in Australia. If plagues can be forecast through broad-scale monitoring or model-based prediction, then mice can be proactively controlled by poison baiting. Aims. To predict mouse plagues in grain crops in Queensland and assess the value of broad-scale monitoring. Methods. Regular trapping of mice at the same sites on the Darling Downs in southern Queensland has been undertaken since 1974. This provides an index of abundance over time that can be related to rainfall, crop yield, winter temperature and past mouse abundance. Other sites have been trapped over a shorter time period elsewhere on the Darling Downs and in central Queensland, allowing a comparison of mouse population dynamics and cross-validation of models predicting mouse abundance. Key results. On the regularly trapped 32-km transect on the Darling Downs, damaging mouse densities occur in 50% of years and a plague in 25% of years, with no detectable increase in mean monthly mouse abundance over the past 35 years. High mouse abundance on this transect is not consistently matched by high abundance in the broader area. Annual maximum mouse abundance in autumn–winter can be predicted (R2 = 57%) from spring mouse abundance and autumn–winter rainfall in the previous year. In central Queensland, mouse dynamics contrast with those on the Darling Downs and lack the distinct annual cycle, with peak abundance occurring in any month outside early spring.Onaverage, damaging mouse densities occur in 1 in 3 years and a plague occurs in 1 in 7 years. The dynamics of mouse populations on two transects ~70 km apart were rarely synchronous. Autumn–winter rainfall can indicate mouse abundance in some seasons (R2 = ~52%). Conclusion. Early warning of mouse plague formation in Queensland grain crops from regional models should trigger farm-based monitoring. This can be incorporated with rainfall into a simple model predicting future abundance that will determine any need for mouse control. Implications. A model-based warning of a possible mouse plague can highlight the need for local monitoring of mouse activity, which in turn could trigger poison baiting to prevent further mouse build-up.