No abstract

As biological invasions continue to increase globally, eradication programs have been undertaken at significant cost, often without consideration of relevant ecological theory. Theoretical fisheries models have shown that harvest can actually increase the equilibrium size of a population, and uncontrolled studies and anecdotal reports have documented population increases in response to invasive species removal (akin to fisheries harvest). Both findings may be driven by high levels of juvenile survival associated with low adult abundance, often referred to as overcompensation. Here we show that in a coastal marine ecosystem, an eradication program resulted in stage-specific overcompensation and a 30-fold, single-year increase in the population of an introduced predator. Data collected concurrently from four adjacent regional bays without eradication efforts showed no similar population increase, indicating a local and not a regional increase. Specifically, the eradication program had inadvertently reduced the control of recruitment by adults via cannibalism, thereby facilitating the population explosion. Mesocosm experiments confirmed that adult cannibalism of recruits was size-dependent and could control recruitment. Genomic data show substantial isolation of this population and implicate internal population dynamics for the increase, rather than recruitment from other locations. More broadly, this controlled experimental demonstration of stage-specific overcompensation in an aquatic system provides an important cautionary message for eradication efforts of species with limited connectivity and similar life histories.

Time series vegetation indices with high spatial resolution and high temporal frequency are important for crop growth monitoring and management. However, due to technical constraints and cloud contamination, it is difficult to obtain such datasets. In this study, a spatio-temporal vegetation index image fusion model (STVIFM) was developed to generate high spatial resolution Normalized Difference Vegetation Index (NDVI) time-series images with higher accuracy, since most of the existing methods have some limitations in accurately predicting NDVI in heterogeneous regions, or rely on very computationally intensive steps and land cover maps for heterogeneous regions. The STVIFM aims to predict the fine-resolution NDVI through understanding the contribution of each fine-resolution pixel to the total NDVI change, which was calculated from the coarse-resolution images acquired on two dates. On the one hand, it considers the difference in relationships between the fine-and coarse-resolution images on different dates and the difference in NDVI change rates at different growing stages. On the other hand, it neither needs to search similar pixels nor needs to use land cover maps. The Landsat-8 and MODIS data acquired over three test sites with different landscapes were used to test the spatial and temporal performance of the proposed model. Compared with the spatial and temporal adaptive reflectance fusion model (STARFM), enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method, the proposed STVIFM outperforms the STARFM and ESTARFM at three study sites and different stages when the land cover or NDVI changes were captured by the two pairs of fine-and coarse-resolution images, and it is more robust and less computationally intensive than the FSDAF.

Tran, H. S., Li, Y. P., You, M. P, Khan, T. N., Pdtchai-d, I., and Barbetti, M. J. 2014. Temporal and spadal changes in the pea black spot disease complex in Western Australia. Plant Dis. 98:790-796.Black spot (also referred to as Ascochyta blight, Ascochyta foot rot and black stem, and Ascochyta leaf and pod spot) is a devastating disease of pea {Pisum sativum) caused by one or more pathogenic fungi, including Didymella pinodes, Ascochyta pisi, and Phoma pinodella. Surveys were conducted across pea-growing regions of Western

G2 Blackspot Manager, the second generation (G2) of Blackspot Manager model, predicts disease severity and yield loss in addition to quantified release of seasonal ascospores in relation to ascochyta blight on field pea. The model predicts the disease severity with respect to the expected exposure of field pea crop to ascospores of D.