Spatial and temporal drivers of small mammal distributions in a semi-arid environment : the role of rainfall, vegetation and life-history

A key task in ecology is to understand the drivers of animal distributions. In arid and semi-arid environments, this is challenging because animal populations show considerable spatial and temporal variation. An effective approach in such systems is to examine both broad-scale and long-term data. We used this approach to investigate the distribution of small mammal species in semi-arid ‘mallee’ vegetation in south-eastern Australia. First, we examined broad-scale data collected at 280 sites across the Murray Mallee region. We used generalized additive mixed models (GAMMs) to examine four hypotheses concerning factors that influence the distribution of individual mammal species at this scale: vegetation structure, floristic diversity, topography and recent rainfall. Second, we used long-term data from a single conservation reserve (surveyed from 1997 to 2012) to examine small mammal responses to rainfall over a period spanning a broad range of climatic conditions, including record high rainfall in 2011. Small mammal distributions were strongly associated with vegetation structure and rainfall patterns, but the relative importance of these drivers was species-specific. The distribution of the mallee ningaui Ningaui yvonneae, for example, was largely determined by the cover of hummock grass; whereas the occurrence of the western pygmy possum Cercartetus concinnus was most strongly associated with above-average rainfall. Further, the combination of both broad-scale and long-term data provided valuable insights. Bolam's mouse Pseudomys bolami was uncommon during the broad-scale survey, but long-term surveys showed that it responds positively to above-average rainfall. Conceptual models developed for small mammals in temperate and central arid Australia, respectively, were not, on their own, adequate to account for the distributional patterns of species in this semi-arid ecosystem. Species-specific variation in the relative importance of different drivers was more effectively explained by qualitative differences in life-history attributes among species.

Spatial and temporal definition of Ochlerotatus camptorhynchus (Thomson) (Diptera: Culicidae) in the Gippsland Lakes system of eastern Victoria

The confirmed vector of Ross River virus, Ochlerotatus camptorhynchus (Thomson), is the dominant mosquito species inhabiting saline marshes in coastal Victoria. This paper re-examines previously published data on Oc. camptorhynchus, plus additional data collected since that time, and provides greater spatial and temporal definition of Oc. camptorhynchus numbers at seven sites across the Gippsland Lakes system of eastern Victoria. A total of 357 672 Oc. camptorhynchus was captured from 1188 trap-nights across the seven trap sites during trapping seasons from 1990 to 2001. The  dominance of Oc. camptorhynchus across the seven sites averaged 75%, with significant differences in mean abundance of Oc. camptorhynchus found between all trap sites. Significant differences in monthly abundance of Oc. camptorhynchus were observed for Wellington Shire. Increase in populations of Oc. camptorhynchus was associated with increases in rainfall at all trap sites, higher minimum temperatures at two of the seven trap sites, and wind speed at one trap site. Prioritisation of mosquito control may be applied based on spatial and temporal factors according to the findings of this study.

Spatial analysis of urban stormwater quality

Urban stormwater non-point source pollutants are recognized as a major cause of receiving waters quality deterioration. To date most research has focused on specifying temporal variations of stormwater quality parameters which includes high uncertainties and also increases the risk of pollution control structures failure. Traditionally, the temporal variations of quality parameters in forms of either pollutograph or Event Mean Concentration (EMC) is obtained by sampling stormwater at the outlet of urban catchments for quality analysis in addition to measurement of flow rate over years. Spatial variations of the runoff quality are the key factor in non-point source pollution studies. This research investigates spatial variability of urban runoff quality parameters such as Total Phosphorous (TP), Total Nitrogen (TN), Suspended Solids (SS) and Biochemical Oxygen Demands (BOD) in relation to land use of urban catchments. In spatial analysis, stormwater will be sampled over the whole catchment area for a number of rainfall events during a year without any requirement to measure flow rate. This research showed comparable results for average pollutant concentrations with those of other urban catchments in Australia where traditional sampling method was used. The research outcomes will reliably estimate pollutants concentration for improved and efficient design of pollution control structures for each land use.

Bayesian classification of catchments using spatial data: a first step to improved modelling of catchment effects on stream ecological condition

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

Spatial and temporal variation in the breeding of Masked Lapwings (Vanellus miles) in Australia

Spatial and temporal variation in the breeding of Masked Lapwings (Vanellus miles) in Australia were examined using data from Birds Australia’s Nest Record Scheme (NRS; 1957–2002), the Atlas of Australian Birds (1998–2006), and climatic data (1952–2006). Breeding in north-western Australia was concentrated in summer, while in other regions the peak of breeding occurred during spring. Breeding success varied between regions and years but was generally highest in Tasmania. Clutch-size (mean 3.57 eggs ± 0.033 s.e., n = 549 clutches) did not vary regionally or temporally. In the north-east, breeding became earlier over time (~1.9 days per year, NRS), while in the south-east, breeding became later (~0.9 days per year); in other regions temporal trends were not evident. Only Tasmania showed a significant temporal change in breeding success (decrease of ~1.5% per year). All regions experienced warming climates, and annual rainfall increased in north-western regions and decreased in eastern regions. There were weak or no relationships between the amount or success of breeding, clutch-size and the climatic variables considered (with the possible exception of Tasmania), suggesting either that data limitations precluded us from detecting subtle effects or that Masked Lapwings have been little influenced or are resilient to changes in climate over most of their range.

Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AICC). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R 2 and lower AICC. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.

Temporal and spatial variability of breeding in Australian birds and the potential implications of climate change

Climate change has profound implications for biodiversity worldwide. To understand its effects on Australia's avifauna, we need to evaluate the effects of annual climatic variability and geographical climate gradients. Here, we use national datasets to examine variation in breeding of 16 species of common and widespread Australian landbirds, in relation to four variables: altitude, latitude, year and the Southern Oscillation Index. Analysis of 30 years of nesting records confirmed that breeding was generally later in colder altitudes and latitudes (geographic variation), but was not consistently related to year or the Southern Oscillation Index (temporal variation). However, power to detect expected temporal effects was low. The timing of breeding became significantly earlier with year only in south-eastern Australia. In contrast, an index of breeding activity (the proportion of atlas records for a species for which breeding was reported) increased with increasing winter values of the Southern Oscillation Index (generally wetter conditions) for all 16 species across Australia. This suggests that annual fluctuations in rainfall can have dramatic and immediate effects on breeding, even for largely sedentary, seasonally breeding species. If, as expected, climate change creates drier conditions over much of Australia, we predict a marked negative effect on bird breeding.