Data_Sheet_1_Reactive morphology of dividing microglia following kainic acid administration.PDF

The microglial response to a pathological microenvironment is hallmarked by a change in cellular morphology. Following a pathological stimulus, microglia become reactive and simultaneously divide to create daughter cells. Although a wide array of microglial morphologies has been observed, the exact functions of these distinct morphologies are unknown, as are the morphology and reactivity status of dividing microglia. In this study, we used kainic acid to trigger microglial activation and cell division. Following a cortical kainic acid injection, microglial morphology and proliferation were examined at 3 days post-injection using immunohistochemistry for ionized calcium binding adapter molecule 1 (Iba1) to stain for microglia, and KI67 as a marker of cell division. Individual microglial cells were isolated from photomicrographs and skeletal and fractal analyses were used to examine cell size and spatial complexity. We examined the morphology of microglia in both wildtype and microglia-specific tumor necrosis factor (TNF)-α knockout mice. Data were analyzed using generalized linear mixed models or a two-way ANOVA. We found that dividing microglia had a more reactive morphology (larger cell body area, longer cell perimeter, and less ramification) compared to microglia that were not dividing, regardless of microglial release of TNF-α. However, we also observed dividing microglia with a complex, more ramified morphology. Changes in microglial morphology and division were greatest near the kainic acid injection site. This study uses robust and quantitative techniques to better understand microglial cell division, morphology, and population dynamics, which are essential for the development of novel therapeutics that target microglia.</p

Consequences of severe habitat fragmentation on density, genetics, and spatial capture-recapture analysis of a small bear population

<div><p>Loss and fragmentation of natural habitats caused by human land uses have subdivided several formerly contiguous large carnivore populations into multiple small and often isolated subpopulations, which can reduce genetic variation and lead to precipitous population declines. Substantial habitat loss and fragmentation from urban development and agriculture expansion relegated the Highlands-Glades subpopulation (HGS) of Florida, USA, black bears (<i>Ursus americanus floridanus</i>) to prolonged isolation; increasing human land development is projected to cause ≥ 50% loss of remaining natural habitats occupied by the HGS in coming decades. We conducted a noninvasive genetic spatial capture-recapture study to quantitatively describe the degree of contemporary habitat fragmentation and investigate the consequences of habitat fragmentation on population density and genetics of the HGS. Remaining natural habitats sustaining the HGS were significantly more fragmented and patchier than those supporting Florida’s largest black bear subpopulation. Genetic diversity was low (<i>A</i>_R = 3.57; <i>H</i>_E = 0.49) and effective population size was small (<i>N</i>_E = 25 bears), both of which remained unchanged over a period spanning one bear generation despite evidence of some immigration. Subpopulation density (0.054 bear/km²) was among the lowest reported for black bears, was significantly female-biased, and corresponded to a subpopulation size of 98 bears in available habitat. Conserving remaining natural habitats in the area occupied by the small, genetically depauperate HGS, possibly through conservation easements and government land acquisition, is likely the most important immediate step to ensuring continued persistence of bears in this area. Our study also provides evidence that preferentially placing detectors (e.g., hair traps or cameras) primarily in quality habitat across fragmented landscapes poses a challenge to estimating density-habitat covariate relationships using spatial capture-recapture models. Because habitat fragmentation and loss are likely to increase in severity globally, further investigation of the influence of habitat fragmentation and detector placement on estimation of this relationship is warranted.</p></div

Locations of 46 sampling cells in south-central Florida, USA, in which a single, baited barbed-wire hair trap was placed in each cell to collect black bear hair for estimating density and population genetics of the Highlands-Glades subpopulation of Florida black bears (2010–2012).

<p>Locations of 46 sampling cells in south-central Florida, USA, in which a single, baited barbed-wire hair trap was placed in each cell to collect black bear hair for estimating density and population genetics of the Highlands-Glades subpopulation of Florida black bears (2010–2012).</p

Spatial capture-recapture models for estimating density of female and male Florida black bears in the Highlands-Glades subpopulation of south-central Florida, USA (2010–2012).

<p>We modeled percent natural cover (Pnat) as a habitat covariate on density (<i>D</i>), allowed <i>D</i> to vary among sessions (Y), or fixed (~1) <i>D</i>. We modeled a trap-specific behavioral response (bk) and 2-class finite mixtures (h2) on the probability of detection at the activity center of an individual (<i>g</i>_<i>0</i>), and fixed the spatial scale of the detection function (σ). Model selection was based on ≤ 2 ΔAIC_<i>c</i>, which is the relative difference between AIC_<i>c</i> (Akaike’s Information Criterion corrected for small sample size) of the model and the highest ranked model. Weight (w_i) and log-likelihood (logLik) are presented for each model.</p

Spatial capture-recapture model coefficient estimates (β) from the top spatially inhomogeneous density model for male and female Florida black bears in the Highlands-Glades subpopulation of south-central Florida, USA (2010–2012).

<p>Model structure included density (<i>D</i>) varying with percent natural cover (Pnat), a trap-specific behavioral response (bk) on the probability of detection at the activity center of an individual (<i>g</i>_<i>0</i>), and fixed spatial scale of the detection function (σ). Estimate standard errors (SE) and lower (LCL) and upper (UCL) 95% confidence limits are presented.</p

Habitat fragmentation metrics estimated for natural habitats supporting the Highlands-Glades and Ocala-St. Johns subpopulations of Florida black bears.

<p>We estimated percent land area that was natural habitat (% HLA), patch density (PD; patches/km²), mean patch size (MPS; km²), and contagion (Contag; %) for lands occupied by the HGS, and compared to values produced by Hostetler et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181849#pone.0181849.ref089" target="_blank">89</a>] for lands occupied by the comparatively larger Ocala-St. Johns subpopulation.</p

Sex-specific posterior modes of activity centers estimated by spatially inhomogeneous and homogenous density spatial capture-recapture models relative to percent natural cover for Florida black bears in the Highlands-Glades subpopulation of south-central Florida, USA (2010–2012).

<p>Posterior modes from inhomogeneous (varies by percent natural cover) and homogenous density models are indicated by red and black circles, respectively. Locational shifts for each posterior mode between models are denoted by solid black lines, and crosses (<b>×</b>) represent the 46 hair traps that were established. Locations where only a red circle is visible without a solid black connector line indicates a black circle is at the same location. Percent natural cover within the state space is the background color gradient from white (low %) to dark green (high %).</p

Temporal comparison of genetics parameter estimates for the Highlands-Glades subpopulation of Florida black bears in south-central Florida, USA, based on 12 microsatellite markers.

<p>We estimated allelic richness (<i>A</i>_R), observed heterozygosity (<i>H</i>_O), expected heterozygosity (<i>H</i>_E), effective number of breeders (<i>N</i>_B), effective population size (<i>N</i>_E), and inbreeding coefficient (<i>F</i>_IS) from 12 microsatellites for bears sampled during 2004–2005 and 2010–2012. Confidence intervals (95%) are presented in parentheses and <i>n</i> corresponds to sample size.</p