Abstract. Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support-vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10 0 spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model-to-model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species.

Abstract:The potential distribution of Olea ferruginea was predicted by Maxent model for present and the upcoming hypothetical (2050) climatic scenario. O. ferruginea is an economically beneficial plant species. For predicting the potential distribution of O. ferruginea in Pakistan, Worldclim variables for current and future climatic change scenarios, digital elevation model (DEM) slope, and aspects with the occurrence point were used. Pearson correlation was used to reject highly correlated variables. A total of 219 sighting points were used in the Maxent modeling. The area under curve (AUC) value was higher than 0.98. The approach used in this study is considered useful in predicting the potential distribution of O. ferruginea species, and can be an effective tool in the conservation and restoration planning for human welfare. The results show that there is a significant impact under future bioclimatic scenarios on the potential distribution of O. ferruginea in Pakistan. There is a significant decrease in the overall distribution of O. ferruginea due to loss of habitats under current distribution range, but this will be compensated by gain of habitat at higher altitudes in the future climate change scenario (habitat shift). It is recommended that the areas predicted suitable for the O. ferruginea may be used for plantation of this species while the deforested land should be restored for human welfare.

IntroductionSurgical Site Infection (SSI) after knee arthroplasty is a major cause of morbidity and mortality that increases the hospital stay, financial burden and mental anguish of the patient. Infection Control Unit at Aga Khan University Hospital (AKUH) incorporated total knee arthroplasty in its surgical care surveillance program and started collecting data in June 2012. The purpose of this study is to review Surgical Site Infection (SSI) rates in patients undergoing primary total knee replacement (TKR) surgery.Patients and methodologyAll patients from June 2012 to December 2013 undergoing knee arthroplasty at our hospital were included. Data was acquired from the hospital SSI database for knee arthroplasty surgery. Data was collected by SSI nurses for inpatients a well as post-discharge monitoring in clinics till 90 days post-op follow-up. The work has been reported in line with the PROCESS criteria.ResultsDuring this time period a total of 164 patients had primary TKR at AKUH. Out of these, 85 patients (52%) had bilateral TKR while 79 (48%) had unilateral TKR. The overall SSI was in 2 patients (1.2%).ConclusionIdentifying SSIs is multidimensional. Since our 2 infected cases after TKR occurred after discharge, this highlights the importance of post-discharge surveillance and not limiting the surveillance for inpatients only. Furthermore, the SSI program may be effective in controlling postoperative wound infections.