Effects of snow cover on the timing and success of reproduction in high-Arctic pink-footed geese Anser brachyrhynchus

During four breeding seasons, 2003–2006, we studied the relationship between snow cover and nesting performance in pink-footed geese (Anser brachyrhynchus) in a key breeding site on Svalbard. Snow cover in late May, i.e., at the time of egg laying of geese, was derived from MODIS satellite images. Snow cover had a profound cascading effect on reproductive output via the number of nesting pairs and timing of nesting, which affected nest success, while there was only a tendency for a negative effect on clutch size. Hence, we estimated a five-fold difference in the number of young produced (to post-hatching) between years with little snow and years with high snow cover. The results from the study area correlated with whole-population productivity estimates recorded in autumn. Thus, snow cover derived from MODIS satellite images appears to provide a useful indicator of the breeding conditions in the Arctic.

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.

Invasive species can't cover their tracks : using microsatellites to assist management of starling (Sturnus vulgaris) populations in Western Australia

Invasive species are known to cause environmental and economic damage, requiring management by control agencies worldwide. These species often become well established in new environments long before their detection, resulting in a lack of knowledge regarding their history and dynamics. When new invasions are discovered, information regarding the source and pathway of the invasion, and the degree of connectivity with other populations can greatly benefit management strategies. Here we use invasive common starling (Sturnus vulgaris) populations from Australia to demonstrate that genetic techniques can provide this information to aid management, even when applied to highly vagile species over continental scales. Analysis of data from 11 microsatellites in 662 individuals sampled at 17 localities across their introduced range in Australia revealed four populations. One population consisted of all sampling sites from the expansion front in Western Australia, where control efforts are focused. Despite evidence of genetic exchange over both contemporary and historical timescales, gene flow is low between this population and all three more easterly populations. This suggests that localized control of starlings on the expansion front may be an achievable goal and the long-standing practice of targeting select proximal eastern source populations may be ineffective on its own. However, even with low levels of gene flow, successful control of starlings on the expansion front will require vigilance, and genetic monitoring of this population can provide essential information to managers. The techniques used here are broadly applicable to invasive populations worldwide.

Secure mobile RFID ownership transfer protocol to cover all transfer scenarios

Existing business models require RFID tag to transfer its ownership during its life cycle. As a result, a RFID tags might have many owners during its life cycle. However, the transfer of ownership should ensure that previous owners have no information about current owner's data. Physical ownership does not ensure digital ownership transfer given the wireless nature of communication with RFID tags. Most of the proposed protocol in this nature is implacable to address aU existing RFID tag ownership transfer scenarios. Moreover, they have many security concerns and vulnerabilities. In this paper, we have investigated and discussed all existing business cases and their transfer scenarios. To cover all ownership transfer scenarios, we have presented an ownership transfer protocol. The proposed protocol has used modified DiffieHellman algorithm to perform ownership request validation and authentication of involved parties. Performance comparison shows that our protocol is practical to implement passive low-cost RFID tags, securely performs tag ownership transfer and can be used for all existing ownership transfer scenarios.