Abstract Background: Coexisting fibromyalgia (FM) to psoriatic arthritis (PsA) has been identified and it has been associated with more severe symptoms, impaired function, and greater disability. It was aimed to explore the effect of the presence of FM on fatigue in patients with PsA comparing with controls. Methods: Fifty patients with PsA and 34 sex-age matched controls were enrolled. In patients; pain was assessed by Visual Analogue Scale, disease activity by DAS-28, enthesitis by The Leeds Enthesitis Index. Fatigue level of all participants was evaluated by Multidimensional Assessment of Fatigue. In all participants, FM was determined according to 2010 American College of Rheumatology criteria. Results: Seventeen patients with PsA (34%) and 4 controls (11.8%) were diagnosed with FM and all of them were women. There was significant difference between the patients and controls in terms of presence of FM (p < 0.05). Patients' fatigue scores were significantly higher than controls' (p = 0.001). There were significant differences between the PsA patients with and without FM with regard to gender, enthesitis, DAS-28 and pain scores (p < 0.05); fatigue scores (p < 0.001). The significant effect of the presence of FM on fatigue was found by univariate analysis of variance in patients (p < 0.001). Conclusion: It was observed that FM presence and fatigue were more common in PsA patients than controls and comorbid FM had significant effect on fatigue in these patients. Physicians should be aware of the possibility of concomitant FM in patients with PsA.

ABSTRACT Climate change has negative effects on especially plant species that have a limited habitat. In this context, the stone pine (Pinus pinea L.), which has a limited distribution, is prominent with its economic, ecological and aesthetical characteristics, and it is frequently preferred in afforestation. In this study, the habitats in Turkey that are suitable for distribution of P. pinea L. were modelled by using Maxent based on high-resolution environmental data. In total, 13 field-based occurrence points and 19 bioclimatic variables were used to model the potential distribution area under current and two prediction model (RCP4.5 and RCP8.5) for the years 2050 and 2070. According to the results, the most important bioclimatic variables effective on the potential distribution of P. pinea L. were found as the minimum temperature of coldest month (Bio6), annual precipitation (Bio12) and precipitation of wettest quarter (Bio16). Prediction models under two future climate change scenarios displayed that P. pinea L. will lose habitat and it will shift geographical distribution towards north and higher elevation sites. Considering especially the economic contributions provided by P. pinea L., these results necessitate consideration of the areas where the rate of potential distribution of the species is the highest in afforestation work aiming rural development. In addition to habitat losses, climate change affects many variables of the ecosystem.