Anthropogenic disturbance and the formation of oak savanna in central Kentucky, USA

Aim To deepen understanding of the factors that influenced the formation of oak savanna in central Kentucky, USA. Particular attention was focused on the link between historical disturbance and the formation of savanna ecosystem structure. Location Central Kentucky, USA. Methods We used dendrochronological analysis of tree‐ring samples to understand the historical growth environment of remnant savanna stems. We used release detection and branch‐establishment dates to evaluate changes in tree growth and the establishment of savanna physiognomy. We contrasted our growth chronology with reference chronologies for regional tree growth, climate and human population dynamics. Results Trees growing in Kentucky Inner Bluegrass Region (IBR) savanna remnants exhibited a period of suppression, extending from the establishment date of the tree to release events that occurred c. 1800. This release resulted in a tripling of the annual radial growth rate from levels typical of oaks suppressed under a forest canopy (< 1 mm year^?1) to levels typical of open‐grown stems (3 mm year^?1). The growth releases in savanna trees coincided with low branch establishment. Over the release period, climatic conditions remained relatively constant and growth in regional forest trees was even; however, the growth increase in savanna stems was strongly correlated with a marked increase in Euro‐American population density in the region. Main conclusions Our data suggest that trees growing in savanna remnants originated in the understorey of a closed canopy forest. We hypothesize that Euro‐American land clearing to create pasturelands released these trees from light competition and resulted in the savanna physiognomy that is apparent in remnant stands in the IBR. Although our data suggest that savanna trees originated in a forest understorey, this system structure itself may have been a result of an unprecedented lack of Native American activity in the region due to population loss associated with pandemics brought to North America by Euro‐Americans. We present a hypothetical model that links human population dynamics, land‐use activities and ecosystem structure. Our model focuses on the following three land‐use eras: Native American habitation/utilization; land abandonment; and Euro‐American land clearance. Ecological understanding of historical dynamics in other ecosystems of eastern North America may be enhanced through recognition of these eras.     

Late holocene human impact on two coastal environments in New South Wales,Australia: a comparison of Aboriginal and European impacts

There are few historical analyses quantifying impacts of human activity in Australia. This paper compares vegetation change, fire regime, erosion and eutrophication rates between the European period and the recent prehistoric past in two lake systems on the south coast of New South Wales. The variance in pollen abundance and hence species population changes increased markedly in the historical period, especially amongst understorey taxa, and this could be related to changes in the local fire regimes and to the effects of grazing. Local fire activity decreased from the prehistorical period at both sites. Erosion rates increased in the historical period and both organic and inorganic components were deposited in the lakes. Erosion episodes could be related to fire during some periods but are clearly controlled by forest disturbance and land-use at other periods. The trophic status of the lakes was increasing from before European settlement but accelerated in the recent past. This was in part due to the increased erosion rates and in part due to fertiliser application. The results suggest that lower rates of erosional and eutrophic change occur in catchments with basaltic than with Holocene sand substrata.     

Anthropogenic changes to the landscape resulted in colonization of koalas in north‐east New South Wales,Australia

Significant changes in the distribution and persistence of species have been driven by Pleistocene cyclical climate changes and, more recently, by human modification of the environment. In eastern Australia, Pleistocene cyclical patterns in temperature and aridity led to the expansion and retraction of rainforest and likely affected the distribution of the koala (Phascolarctos cinereus, family Phascolarctidae), a species preferring dry or open woodland. More recently, anthropogenic landscape modification has led to a large‐scale change in distribution of the koala following the destruction of approx. 75 000 ha of subtropical rainforest in north‐east New South Wales termed the ‘Big Scrub’. Sharing of the control region haplotypes to the north and south of this region indicate historical connectivity of coastal koala populations. However, the majority (110/115) of sampled koalas from this region shared a single mitochondrial control region haplotype, suggesting that koalas did not persist in multiple refugial pockets within a heterogenous rainforest but expanded into the region following deforestation. Bayesian cluster analysis of microsatellite data consistently identified two clusters of koalas. One cluster, in the north of the area, had high microsatellite diversity (10 alleles per locus, He = 0.79) and clustered with koalas further to the north, thus suggesting a southerly expansion into the cleared area. To the south was a cluster with significantly lower diversity (six alleles per locus, He = 0.59, P < 0.001). It is possible there has been restricted or filtered movement of koalas between these clusters, which coincides with a cleared river valley and associated roads or immigration from populations both to the north and to the south. This study gives an insight into the timescale of changes in species distribution following rapid alterations to suitable habitat.     

The roles of statistical inference and historical sources in understanding landscape change: the case of feral buffalo in the freshwater floodplains of Kakadu National Park

The statistical analysis of Bowman et al. ( Journal of Biogeography , 2008, 35 , 1976–1988) revealed the weak relationship between the rate of woody cover encroachment onto the freshwater floodplains in the central section of Kakadu National Park (KNP) over a 40-year period and estimates of proximate water buffalo ( Bubalus bubalis ) density. The analysis relied on detailed mapping of buffalo tracks, the best historical record of spatial variation of buffalo density in KNP. In their reply, Petty & Werner ( Journal of Biogeography , 2009, doi: [DOI link] ) prefer to privilege an amalgam of historical sources to claim that buffalo removal is the primary driver of the woody expansion on floodplains. The contrasting weight placed on data analysis and differences of interpretation underscore a tension between statistically based historical ecology approaches and environmental history narratives, a tension that forms part of the broader cultural clash between the Sciences and Humanities.     

Impact of geographic area level on measuring socioeconomic disparities in cancer survival in New South Wales,Australia: A period analysis

BackgroundArea-based socioeconomic measures are widely used in health research. In theory, the larger the area used the more individual misclassification is introduced, thus biasing the association between such area level measures and health outcomes. In this study, we examined the socioeconomic disparities in cancer survival using two geographic area-based measures to see if the size of the area matters.MethodsWe used population-based cancer registry data for patients diagnosed with one of 10 major cancers in New South Wales (NSW), Australia during 2004–2008. Patients were assigned index measures of socioeconomic status (SES) based on two area-level units, census Collection District (CD) and Local Government Area (LGA) of their address at diagnosis. Five-year relative survival was estimated using the period approach for patients alive during 2004–2008, for each socioeconomic quintile at each area-level for each cancer. Poisson-regression modelling was used to adjust for socioeconomic quintile, sex, age-group at diagnosis and disease stage at diagnosis. The relative excess risk of death (RER) by socioeconomic quintile derived from this modelling was compared between area-units.ResultsWe found extensive disagreement in SES classification between CD and LGA levels across all socioeconomic quintiles, particularly for more disadvantaged groups. In general, more disadvantaged patients had significantly lower survival than the least disadvantaged group for both CD and LGA classifications. The socioeconomic survival disparities detected by CD classification were larger than those detected by LGA. Adjusted RER estimates by SES were similar for most cancers when measured at both area levels.ConclusionsWe found that classifying patient SES by the widely used Australian geographic unit LGA results in underestimation of survival disparities for several cancers compared to when SES is classified at the geographically smaller CD level. Despite this, our RER of death estimates derived from these survival estimates were generally similar for both CD and LGA level analyses, suggesting that LGAs remain a valuable spatial unit for use in Australian health and social research, though the potential for misclassification must be considered when interpreting research. While data confidentiality concerns increase with the level of geographical precision, the use of smaller area-level health and census data in the future, with appropriate allowance for confidentiality     

Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales,Australia

Aim We consider three questions. (1) How different are the predicted distribution maps when climate‐only and climate‐plus‐terrain models are developed from high‐resolution data? (2) What are the implications of differences between the models when predicting future distributions under climate change scenarios, particularly for climate‐only models at coarse resolution? (3) Does the use of high‐resolution data and climate‐plus‐terrain models predict an increase in the number of local refugia? Location South‐eastern New South Wales, Australia. Methods We developed two species distribution models for Eucalyptus fastigata under current climate conditions using generalized additive modelling. One used only climate variables as predictors (mean annual temperature, mean annual rainfall, mean summer rainfall); the other used both climate and landscape (June daily radiation, topographic position, lithology, nutrients) variables as predictors. Predictions of the distribution under current climate and climate change were then made for both models at a pixel resolution of 100 m. Results The model using climate and landscape variables as predictors explained a significantly greater proportion of the deviance than the climate‐only model. Inclusion of landscape variables resulted in the prediction of much larger areas of existing optimal habitat. An overlay of predicted future climate on the current climate space indicated that extrapolation of the statistical models was not occurring and models were therefore more robust. Under climate change, landscape‐defined refugia persisted in areas where the climate‐only model predicted major declines. In areas where expansion was predicted, the increase in optimal habitat was always greater with landscape predictors. Recognition of extensive optimal habitat conditions and potential refugia was dependent on the use of high‐resolution landscape data. Main conclusions Using only climate variables as predictors for assessing species responses to climate change ignores the accepted conceptual model of plant species distribution. Explicit statements justifying the selection of predictors based on ecological principles are needed. Models using only climate variables overestimate range reduction under climate change and fail to predict potential refugia. Fine‐scale‐resolution data are required to capture important climate/landscape interactions. Extrapolation of statistical models to regions in climate space outside the region where they were fitted is risky.