Judit K. Szabo

Predicting spatio-temporal distribution for eastern Australian birds using birds Australia's atlas data : survey method, habitat and seasonal effects

Abstract Faunal atlases are landscape-level survey collections that can be used for describing spatial and temporal patterns of distribution and densities. They can also serve as a basis for quantitative analysis of factors that may influence the distributions of species. We used a subset of Birds Australias Atlas of Australian Birds data (January 1998 to December 2002) to examine the spatio-temporal distribution patterns of 280 selected species in eastern Australia (17–37°S and 136–152°E). Using geographical information systems, this dataset was converted into point coverage and overlaid with a vegetation polygon layer and a half-degree grid. The exploratory data analysis involved calculating species-specific reporting rates spatially, per grid and per vegetation unit, and also temporally, by month and year. We found high spatiotemporal variability in the sampling effort. Using generalised linear models on unaggregated point data, the influences of four factors—survey method and month, geographical location and habitat type—were analysed for each species. When counts of point data were attributed to grid-cells, the total number of species correlated with the total number of surveys, while the number of records per species was highly variable. Surveys had high interannual location fidelity. The predictive values of each of the four factors were species-dependent. Location and habitat were correlated and highly predictive for species with restricted distribution and strong habitat preference. Month was only of importance for migratory species. The proportion of incidental sightings was important for extremely common or extremely rare species. In conclusion, behaviour of species differed sufficiently to require building a customized model for each species to predict distribution. Simple models were effective for habitat specialists with restricted ranges, but for generalists with wide distributions even complex models gave poor predictions.

Plasma cholinesterase characteristics in native Australian birds: significance for monitoring avian species for pesticide exposure.

Abstract Cholinesterase-inhibiting pesticides are applied throughout Australia to control agricultural pests. Blood plasma cholinesterase (ChE) activity is a sensitive indicator of exposure to organophosphorus insecticides in vertebrates. To aid biomonitoring and provide reference data for wildlife pesticide-risk assessment, plasma acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) activities were characterised in nine species of native bird: King Quails (Excalfactoria chinensis), Budgerigars (Melopsittacus undulatus), White-plumed Honeyeaters (Lichenostomas penicillatus), Yellow- throated Miners (Manorina flavigula), Willie Wagtails (Rhipidura leucophrys), Australian Reed-Warblers (Acrocephalus australis), Brown Songlarks (Cincloramphus cruralis), Double-barred Finches (Taeniopygia bichenovii) and Australasian Pipits (Anthus novaeseelandiae). Plasma ChE activities in all species were within the range ofmost other avian species and all but one contained AChE and BChE; no AChE was present in King Quail, which has not previously been reported for any species. The lowest detectable plasma AChE activity was 0.10 μmol min−1 mL−1 in Budgerigars and the highest was 0.86 μmol min−1 mL−1 in Australian Reed-Warblers. BChE in the plasma ranged from 0.37 mmolmin−1 mL−1 in Double-barred Finches to 0.90 mmolmin−1 mL−1 in White-plumed Honeyeaters and Australian Reed-Warblers. The lowest proportion of AChE was found in Budgerigars (12.8%) and highest in Willie Wagtails (67.8%). No differences were detected in ChE activity at any time of day in Budgerigars and Zebra Finches (Taeniopygia guttata), although there was a significant difference in all ChE activity between seasons in Zebra Finches.

Predicting avian distributions to evaluate spatiotemporal overlap with locust control operations in eastern Australia

Locusts and grasshoppers cause considerable economic damage to agriculture worldwide. The Australian Plague Locust Commission uses multiple pesticides to control locusts in eastern Australia. Avian exposure to agricultural pesticides is of conservation concern, especially in the case of rare and threatened species. The aim of this study was to evaluate the probability of pesticide exposure of native avian species during operational locust control based on knowledge of species occurrence in areas and times of application. Using presence-absence data provided by the Birds Australia Atlas for 1998 to 2002, we developed a series of generalized linear models to predict avian occurrences on a monthly basis in 0.5 degrees grid cells for 280 species over 2 million km2 in eastern Australia. We constructed species-specific models relating occupancy patterns to survey date and location, rainfall, and derived habitat preference. Model complexity depended on the number of observations available. Model output was the probability of occurrence for each species at times and locations of past locust control operations within the 5-year study period. Given the high spatiotemporal variability of locust control events, the variability in predicted bird species presence was high, with 108 of the total 280 species being included at least once in the top 20 predicted species for individual space-time events. The models were evaluated using field surveys collected between 2000 and 2005, at sites with and without locust outbreaks. Model strength varied among species. Some species were under- or over-predicted as times and locations of interest typically did not correspond to those in the prediction data set and certain species were likely attracted to locusts as a food source. Field surveys demonstrated the utility of the spatially explicit species lists derived from the models but also identified the presence of a number of previously unanticipated species. These results also emphasize the need for special consideration of rare and threatened species that are poorly predicted by presence-absence models. This modeling exercise was a useful a priori approach in species risk assessments to identify species present at times and locations of locust control applications, and to discover gaps in our knowledge and need for further focused data collection.

What does the future hold for shorebirds in the East Asian–Australasian Flyway?

East Asian–Australasian Flyway Partnership Secretariat, 3F G-Tower, 175 Art center-daero (24-4 Songdo-dong), Yeonsu-gu, Incheon 406-840, Republic of Korea. Ecology Group, Institute of Agriculture and Environment, PN 624, Massey University, Private Bag 11-222, Palmerston North 4442, New Zealand. School of Life and Environmental Sciences, DeakinUniversity, Locked Bag 20000,Geelong, Vic. 3220, Australia. Arthur Rylah Institute for Environmental Research, PO Box 137, Heidelberg, Vic. 3084, Australia. Corresponding author. Email: drogers@melbpc.org.au

DYNAMICS AND SPATIO-TEMPORAL VARIABILITY OF ENVIRONMENTAL FACTORS IN EASTERN AUSTRALIA USING FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 1998–2004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types.