Understanding how patchy landscapes affect the distribution and abundance of species is a central issue in applied ecology. We examined the effects of landscape structure on the distribution of the Florida scrub lizard (Sceloporus woodi) in naturally fragmented habitat. Scrub lizards were absent from 89 of 132 patches, and their distribution was explained in multifactor models by two landscape variables: patch size and patch isolation. Mark-recapture studies of S. woodi and experimental displacement trials indicate that this species exhibits low vagility within a patch and that movement through dense habitat, which often characterizes the landscape matrix, is restricted. Poor dispersal ability relative to the degree of isolation of patches in this landscape may account for large gaps in its distribution. To examine the applicability of the scrub lizard model to other species, we applied the model to a sympatric lizard, the six-lined racerunner (Cnemidophorus sexlineatus), which is similar in body size, food habits, and habitat requirements. The racerunner was more widely distributed than the Florida scrub lizard within our scrub landscape, and the model failed to predict its distribution. This differing response to landscape structure by a similar species is likely due to the greater dispersal ability of C. sexlineatus. Our results, and similar studies of other scrub organisms, indicate that each species may have a unique response to habitat fragmentation. The design and management of effective preserves for Florida scrub will require a multiscaled, multispecies approach that reflects the differences in the dispersal abilities, area requirements, and habitat preferences of the species to be preserved.

This article is a U.S. government work, and is not subject to copyright in the United States. From the EditorLike many members of the American Ornithologists' Union, I developed a passion for birds early in life and have been trying to make a living from this passion ever since. As you get to know fellow AOU members, particularly over drinks, you hear stories about that moment in their lives when they discovered this incredible feeling about birds that could not be ignored. Some of us started at age six or seven, others in high school or college.Some of us, lacking the artistic skills of David Sibley or the humor of Pete Dunne, have had to fall back on doing research. Our field work allows us to earn a paycheck in pursuit of our passion. We get to see and catch and count birds in what are sometimes exciting, and hopefully always interesting, places. We are paid to write reports, scientific papers, and books that present our results from the field. In many cases, our work has direct or indirect implications for bird conservation.Unfortunately, we cannot simply write papers about anything that catches our fancy. Rather, we need to justify our work on the basis of prior studies and present our results using up-to-date methodologies. For many of us, this is where the ugly concepts of hypotheses, models, and statistics come into play and turn our lives of passion into actual work and sometimes drudgery. During my career, the profession has become more and more based on the testing of models, usually with associated quantitative measures. I took a single statistics course during eight years of college; my graduate students take at least three or four, and often more. One even has a Master's degree in statistics to go along with her MS in ecology. More and more, our field work is only as good as the model being tested, and the test only as good as the calculations provided.Of course, doing field work requires a lot of skill, though usually of the "natural history" category that involves details about birds, plants, and so forth. Doing modeling and developing statistical methods obviously requires a high comfort level with conceptual thinking and mathematics. Few ornithologists are experts in both areas; field people are always trying to figure out what is currently the best way to measure their area of specialty, while modelers are trying to provide methods for the analytical questions posed by the field people. The goal of both groups is to develop the best science possible, with the appropriate scientific models tested with the best quantitative techniques.All these areas within ornithological research change over time, so it is difficult for field people to stay current with the latest models and for modelers to ensure that field people are providing the best data possible and analyzing it properly. Ornithological Monograph No. 59 addresses this problem by serving as a bridge between field biologists and modelers. It provides a state-of-the-art review by a set of experts of the models they consider most relevant to...

Florida manatees (Trichechus manatus latirostris) overwintering in the Ten Thousand Islands and western Everglades have no access to power plants or major artesian springs that provide warm-water refugia in other parts of Florida. Instead, hundreds of manatees aggregate at artificial canals, basins, and natural deep water sites that act as passive thermal refugia (PTR). Monitoring at two canal sites revealed temperature inverted haloclines, which provided warm salty bottom layers that generally remained above temperatures considered adverse for manatees. At the largest PTR, the warmer bottom layer disappeared unless significant salt stratification was maintained by upstream freshwater inflow over a persistent tidal wedge. A detailed threedimensional hydrology model showed that salinity stratification inhibited vertical convection induced by atmospheric cooling. Management or creation of temperature inverted haloclines may be a feasible and desirable option for resource managers to provide passive thermal refugia for manatees and other temperature sensitive aquatic species.

Sea level rise (SLR) projections along the coast of Florida present an enormous challenge for management and conservation over the long term. Decision makers need to recognize and adopt strategies to adapt to the potentially detrimental effects of SLR. Structured decision making (SDM) provides a rigorous framework for the management of natural resources. The aim of SDM is to identify decisions that are optimal with respect to management objectives and knowledge of the system. Most applications of SDM have assumed that the managed systems are governed by stationary processes. However, in the context of SLR it may be necessary to acknowledge that the processes underlying managed systems may be non-stationary, such that systems will be continuously changing. Therefore, SLR brings some unique

A stochastic, stage-based population model was developed to describe the life history and forecast the population dynamics of the Florida manatee (Trichechus manatus latirostris) in four separate regions of Florida. This population model includes annual variability in survival and reproductive rates, demographic stochasticity, effects of changes in warm-water capacity, and catastrophes. Further, the model explicitly accounts for uncertainty in parameter estimates. This model is meant to serve as a flexible tool for use in assessments relevant to management decision making, and was used in the State of Florida's recent biological status review. The parameter estimates and model structure described herein reflect our understanding of manatee demography at the time that this status review was completed. In the Northwest and Upper St. Johns regions, the model predicts that the populations will increase over time until warm-water capacity is reached, at which point growth will taper off. In the Atlantic region, the model predicts a stable or slightly increasing population over the next decade or so, and then a decrease as industrial warm-water capacity is lost. In the Southwest region, the model predicts a decline over time, driven by high annual mortality in the short-term and exacerbated by loss of industrial warmwater winter refuges over the next 40 years. Statewide, the likelihood of a 50% or greater decline in three manatee generations was 12%; the likelihood of a 20% or greater decline in two generations was 56%. These declines are largely driven by the anticipated loss of warm-water capacity, especially in the Atlantic and Southwest regions. The estimates of probability of extinction within 100 years were 11.9% for the Southwest region, 0.6% for the Northwest, 0.04% for the Atlantic, and <0.02% for the Upper St. Johns. The estimated probability that the statewide population will fall below 1000 animals within 100 years was 2.3%. Thus, while the estimated probability of extinction is low, the model predicts that current and emerging threats are likely to result in a long-term decline in the statewide population and a change in the regional distribution of manatees. Analyses of sensitivity and variance contribution highlight the importance of reducing uncertainty in some life-history parameters, particularly adult survival, temporal variance of adult survival, and long-term warm-water capacity. This core biological model is expected to evolve over time, as better information becomes available about manatees and their habitat, and as new assessment needs arise. We anticipate that this core model will be customized for other state and federal assessments in the near future.

Application of metapopulation models is becoming increasingly widespread in the conservation of species in fragmented landscapes. We provide one of the first detailed comparisons of two of the most common modeling techniques, incidence function models and stage‐based matrix models, and test their accuracy in predicting patch occupancy for a real metapopulation. We measured patch occupancies and demographic rates for regional populations of the Florida scrub lizard (  Sceloporus woodi) and compared the observed occupancies with those predicted by each model. Both modeling strategies predicted patch occupancies with good accuracy ( 77–80%) and gave similar results when we compared hypothetical management scenarios involving removal of key habitat patches and degradation of habitat quality. To compare the two modeling approaches over a broader set of conditions, we simulated metapopulation dynamics for 150 artificial landscapes composed of equal‐sized patches (2–1024 ha) spaced at equal distances (50–750 m). Differences in predicted patch occupancy were small to moderate (<20%) for about 74% of all simulations, but 22% of the landscapes had differences openface> 50%. Incidence function models and stage‐based matrix models differ in their approaches, assumptions, and requirements for empirical data, and our findings provide evidence that the two models can produce different results. We encourage researchers to use both techniques and further examine potential differences in model output. The feasibility of obtaining data for population modeling varies widely among species and limits the modeling approaches appropriate for each species. Understanding different modeling approaches will become increasingly important as conservation programs undertake the challenge of managing for multiple species in a landscape context.

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

Understanding how patchy landscapes affect the distribution and abundance of species is a central issue in applied ecology. We examined the effects of landscape structure on the distribution of the Florida scrub lizard (Sceloporus woodi) in naturally fragmented habitat. Scrub lizards were absent from 89 of 132 patches, and their distribution was explained in multifactor models by two landscape variables: patch size and patch isolation. Mark–recapture studies of S. woodi and experimental displacement trials indicate that this species exhibits low vagility within a patch and that movement through dense habitat, which often characterizes the landscape matrix, is restricted. Poor dispersal ability relative to the degree of isolation of patches in this landscape may account for large gaps in its distribution. To examine the applicability of the scrub lizard model to other species, we applied the model to a sympatric lizard, the six‐lined racerunner (Cnemidophorus sexlineatus), which is similar in body size, food habits, and habitat requirements. The racerunner was more widely distributed than the Florida scrub lizard within our scrub landscape, and the model failed to predict its distribution. This differing response to landscape structure by a similar species is likely due to the greater dispersal ability of C. sexlineatus. Our results, and similar studies of other scrub organisms, indicate that each species may have a unique response to habitat fragmentation. The design and management of effective preserves for Florida scrub will require a multiscaled, multispecies approach that reflects the differences in the dispersal abilities, area requirements, and habitat preferences of the species to be preserved.