Judicial Review for Ohio\u27s Civil Servants

With the proliferation of administrative agencies, numerous problems are naturally encountered. In spite of the tendency toward problems, one would hope that in establishing these agencies, the legislature whether it be on the local, state, or federal level would do its utmost to insure uniformity within a given area. A review of sections 119.12, 143.27, and 2506 of the Ohio Revised Code and the relevant case law, however, reveals the Ohio legislature\u27s failure to insure that uniformity

Identifying Sources of Landscape Variation to Improve Predictions of Post-Fire Sagebrush Steppe Recovery

Sagebrush steppe ecosystems are endangered landscapes, threated by the annual grass-fire cycle where invasion by annual grasses drives larger fires and larger fires drive invasion. Despite extensive input of resources by land management agencies, restoration of these ecosystems is notoriously variable and difficult to predict. Understanding and accounting for variation is key to effectively allocating limited resources and having success in restoring burned sagebrush landscapes. I utilized Bayesian modeling to assess how variation in weather, seed dispersal, and topography/slope/landscape position affects understanding of post-fire sagebrush-steppe recovery and how we can best incorporate sources of variation into models predicting where plant communities will most successfully recover. We first asked how weather conditions directly after fire (in the first 4 years) during important phenological windows or during the antecedent five-years affected long-term vegetation trajectories and how inclusion of weather metrics affected the transferability of vegetation abundance models from one site to another. We found that annual grasses, perennial grasses, and sagebrush all responded differently to post-fire weather, with grasses more limited by post-fire precipitation and sagebrush more limited by post-fire temperatures. However, while including weather variables improved model transferability from one site to another for perennial and annual grass abundance (not for sagebrush), the chosen weather metrics did not matter. Next, we aimed to assess how sagebrush seed dispersal varies across large landscapes, such as megafires. We conducted a vertical seed trapping experiment and terminal velocity measurements in the lab and combined the data to parameterize a hierarchical Bayesian model that incorporated both an empirical and mechanistic component. We determined that seed dispersal is highly variable, even at a small scale. Our seed rain projections suggest that seed dispersal from natural recovery may pose severe seed limitations for large burned areas, although natural dispersal is likely to be extremely variable. Our novel data fusion approach to seed dispersal modeling has generalizable applications to estimating seed dispersal at larger scales for other species of concern. Finally, we asked how accuracy and precision of fractional vegetation cover estimates derived from several different satellite-derived products varied with plant cover type, scale, time, and topography in post-fire systems. We found that all gridded map products tested tended to overestimate very low cover and underestimate very high cover, although some products are more accurate than others. We also found that field-derived models of vegetation tend to agree more with satellite-derived models of vegetation at larger scale and less at a smaller scale. Finally, we found that annual herbaceous cover tends to be overestimated in higher elevation, more topographically diverse areas, whereas perennial herbaceous cover tends to be underestimated in these areas. Together these analyses provide a means by which to better understand variability and the reliability of post-fire vegetation recovery models. Incorporation of the sources of variability we have identified here will help refine future models of recovery, whether they are based on data sources from the field, lab, or remote-sensing

Judicial Review for Ohio\u27s Civil Servants

With the proliferation of administrative agencies, numerous problems are naturally encountered. In spite of the tendency toward problems, one would hope that in establishing these agencies, the legislature whether it be on the local, state, or federal level would do its utmost to insure uniformity within a given area. A review of sections 119.12, 143.27, and 2506 of the Ohio Revised Code and the relevant case law, however, reveals the Ohio legislature\u27s failure to insure that uniformity

Airport Searches and the Right to Travel: Some Constitutional Questions

Historically the constitutional right to travel has arisen in two contexts. First, it has arisen within the context of the competing interests of the individual to travel internationally and the interest in national security. The other is that in which an individual wishes to travel to some area, and the government restricts that right in an effort to protect the persons in the area to which the individual wishes to travel. However, under the current airport screening procedures the right to travel may be being restricted or interfered with in another context: prevention and detection of criminal activity. This note shall survey this constitutional right to travel, as it relates to airport searches. In addition, it also looks into such areas as governmental action, probable cause, waiver of fourth amendment rights, and the rule of exclusion. After reviewing these areas, one reaches the inescapable conclusion that in order to maintain the vitality of the fourth amendment, courts must apply the rule of exclusion to seized contraband that could in no way be used to hijack an aircraft

Post-Fire Seed Dispersal of a Wind-Dispersed Shrub Declined with Distance to Seed Source, yet had High Levels of Unexplained Variation

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m−2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes

Interannual Variation in Climate Contributes to Contingency in Post-Fire Restoration Outcomes in Seeded Sagebrush Steppe

Interannual variation, especially weather, is an often-cited reason for restoration “failures”; yet its importance is difficult to experimentally isolate across broad spatiotemporal extents, due to correlations between weather and site characteristics. We examined post-fire treatments within sagebrush-steppe ecosystems to ask: (1) Is weather following seeding efforts a primary reason why restoration outcomes depart from predictions? and (2) Does the management-relevance of weather differ across space and with time since treatment? Our analysis quantified range-wide patterns of sagebrush (Artemisia spp.) recovery, by integrating long-term records of restoration and annual vegetation cover estimates from satellite imagery following thousands of post-fire seeding treatments from 1984 to 2005. Across the Great Basin, sagebrush growth increased in wetter, cooler springs; however, the importance of spring weather varied with sites\u27 long-term climates, suggesting differing ecophysiological limitations across sagebrush\u27s range. Incorporation of spring weather, including from the “planting year,” improved predictions of sagebrush recovery, but these advances were small compared to contributions of time-invariant site characteristics. Given extreme weather conditions threatening this ecosystem, explicit consideration of weather could improve the allocation of management resources, such as by identifying areas requiring repeated treatments; but improved forecasts of shifting mean conditions with climate change may more significantly aid the prediction of sagebrush recovery

Bayesian Models for Spatially Explicit Interactions Between Neighbouring Plants

Interactions between neighbouring plants drive population and community dynamics in terrestrial ecosystems. Understanding these interactions is critical for both fundamental and applied ecology. Spatial approaches to model neighbour interactions are necessary, as interaction strength depends on the distance between neighbouring plants. Recent Bayesian advancements, including the Hamiltonian Monte Carlo algorithm, offer the flexibility and speed to fit models of spatially explicit neighbour interactions. We present a guide for parameterizing these models in the Stan programming language and demonstrate how Bayesian computation can assist ecological inference on plant–plant interactions. Modelling plant neighbour interactions presents several challenges for ecological modelling. First, nonlinear models for distance decay can be prone to identifiability problems, resulting in lack of model convergence. Second, the pairwise data structure of plant–plant interaction matrices often leads to large matrices that demand high computational power. Third, hierarchical structure in plant–plant interaction data is ubiquitous, including repeated measurements within field plots, species and individuals. Hierarchical terms (e.g. ‘random effects’) can result in model convergence problems caused by correlations between coefficients. We explore modelling solutions for these challenges with examples representing spatial data on plant demographic rates: growth, survival and recruitment. We show that ragged matrices reduce computational challenges inherent to pairwise matrices, resulting in higher efficiency across data types. We also demonstrate how metrics for model convergence, including divergent transitions and effective sample size, can help diagnose problems that result from complex nonlinear structures. Finally, we explore when to use different model structures for hierarchical terms, including centred and non-centred parameterizations. We provide reproducible examples written in Stan to enable ecologists to fit and troubleshoot a broad range of neighbourhood interaction models. Spatially explicit models are increasingly central to many ecological questions. Our work illustrates how novel Bayesian tools can provide flexibility, speed and diagnostic capacity for fitting plant neighbour models to large, complex datasets. The methods we demonstrate are applicable to any dataset that includes a response variable and locations of observations, from forest inventory plots to remotely sensed imagery. Further developments in statistical models for neighbour interactions are likely to improve our understanding of plant population and community ecology across systems and scales

Satellite-derived plant cover maps vary in performance depending on version and product

Understanding the accuracy and appropriate application scale of satellite-derived maps of vegetation cover is essential for effective management of the vast, remote rangelands of the world. However, the underlying models are updated frequently and may combine with rapidly changing vegetation conditions to cause variations in accuracy and precision over time. We sought to assess how model performance changed between different versions of satellite-derived cover products (Rangeland Analysis Platform, RAP, and Rangeland Condition Monitoring and Assessment Protocol, RCMAP) and how the performance of LandCart compared to RAP and RCMAP. Additionally, we asked how variability in agreement between LandCart and field-based models varied with scale. We utilized an intensive dataset of grid-point intercept functional group cover data collected between 2016 and 2020 across the ∼113 kHA 2015 Soda Wildfire to 1) evaluate r2 agreement between versions of each satellite-derived product and plot-level field data and 2) assess relative standard error of agreement in cover between LandCart and continuous field-based Empirical Bayesian Kriging (EBK) regression models. Agreement between satellite- compared to field-plot values of cover (r2) increased for RCMAP Version 5.0 compared to Version 2.0, but there were negligible changes between versions of RAP. Despite this, r2 values of RCMAP and LandCart were nearly always less than RAP. Variability in agreement between EBK regression model cover and LandCart-derived cover decreased with the scale of consideration. Variability in agreement between satellite-derived cover products and field-based metrics is lowest at larger scale (mega-fire or regional) and varies from year to year and across versions, which could complicate detection of temporal changes in plant cover