Integrating population and community ecology can improve our understanding of the impacts of natural disturbances. Fire-stimulated flowering occurs in many long-lived herbaceous species of firemaintained grasslands and savannas. Coexistence of these long-lived species may be affected in part by interspecific differences in the effect of fire-stimulated flowering on resource conservation, clonal growth, and longevity. This study comprised two parts. The first part investigated the effectiveness of different firerelated cues on fire-stimulated flowering in two cooccurring dominant grass species in a wet longleaf pine (Pinus palustris) savanna in southeastern Mississippi, USA. The second part investigated the immediate effects of the most important of these cues (damage and removal of aboveground vegetation and surface litter in May) along with nutrient addition on several measures of fitness and abundance over 6 years. Despite being a very effective inductive cue, when repeated frequently over 6 years, clipping plus surface litter removal significantly reduced flowering in both species. This negative effect was reduced to some extent by nutrient addition in Muhlenbergia expansa (which exhibited higher reproductive investment following clipping and fire than did Ctenium aromaticum). Frequent clipping resulted in an increasing numerical advantage of C. aromaticum over M. expansa with time. There was evidence of a modest release of C. aromaticum from competition with M. expansa in response to annual clipping. Responses suggest that suppressing flowering until after fire reduces the cost of flowering and maintains shoot densities, at least in M. expansa. Differences in the responses of these two species to repeated clipping and nutrient addition suggest that, despite their both exhibiting fire-stimulated flowering, each species is favored by slightly different fire frequencies. Moderate variation in fire frequency could maintain their coexistence in the long term.

Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.

47Currently available software tools for automated segmentation and analysis of muscle cross-48 section images often perform poorly in cases of weak or non-uniform staining conditions. To 49 address these issues, our group has developed the MyoSAT (Myofiber Segmentation and 50 Analysis Tool) image-processing pipeline. 51 52 MyoSAT combines several unconventional approaches including advanced background leveling, 53 Perona-Malik anisotropic diffusion filtering, and Steger's line detection algorithm to aid in pre-54 processing and enhancement of the muscle image. Final segmentation is based upon marker-55 based watershed segmentation. 56 57 Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that 58 MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~97%. The 59 software has been tested to work on full muscle cross-sections and works well even under non-60 optimal staining conditions.61 62 The MyoSAT software tool has been implemented as a macro for the freely available ImageJ 63 software platform. This new segmentation tool allows scientists to efficiently analyze large 64 muscle cross-sections for use in research studies and diagnostics. 3 65 Introduction: 66 67 Skeletal muscle is an adaptive tissue, which undergoes changes in mass and fiber composition in 68 response to a wide range of stimuli including exercise, aging, trauma, as well as myopathic and 69 neurological disease. Changes in muscle mass are primarily observed to be associated with 70 atrophy or hypertrophy of individual myofibers as opposed to changes in fiber number. (1,2) 71 Thus, characterization of fiber size distribution in the muscle tissue has significant diagnostic 72 importance. 73 Muscle fiber size is routinely determined through imaging and analysis of fixed or frozen cross-74 sections. The fiber size distribution is typically quantified in term of cross-sectional area (CSA) 75 or fiber diameter. The use of minimum feret diameter is preferred as it is the least affected by 76 distortion due to oblique cross-sectioning of muscle tissue. (3) 77 The development of fluorescent immunohistochemistry (IHC) protocols which label the muscle 78 fiber plasma membrane or extracellular matrix enable high contrast imaging of the fiber 79 boundaries. Effective staining protocols for delineating muscle fibers include dystrophin (4), 80 laminin (5), or collagen (6) staining techniques. 81Despite availability of these labelling procedures to aid in identification of the fiber boundaries, 82 segmentation and analysis of scans of muscle cross-sections is still most often accomplished 83 using manual techniques. This is frequently done using basic image annotation software 84 combined with a graphic tablet or mouse. This manual quantification process is tedious and time 85 consuming (7). Considerable regional variability in fiber size is often observed across a muscle 4 86 section and so a large number of regions must be sampled across the specimen to accurately 87 quantify the fiber size distribution...