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Tropicalization is a term used to describe the transformation of temperate ecosystems by poleward-moving tropical organisms in response to warming temperatures. In North America, decreases in the frequency and intensity of extreme winter cold events are expected to allow the poleward range expansion of many cold-sensitive tropical organisms, sometimes at the expense of temperate organisms. Although ecologists have long noted the critical ecological role of winter cold temperature extremes in tropical-temperate transition zones, the ecological effects of extreme cold events have been understudied, and the influence of warming winter temperatures has too often been left out of climate change vulnerability assessments. Here, we examine the influence of extreme cold events on the northward range limits of a diverse group of tropical organisms, including terrestrial plants, coastal wetland plants, coastal fishes, sea turtles, terrestrial reptiles, amphibians, manatees, and insects. For these organisms, extreme cold events can lead to major physiological damage or landscape-scale mass mortality. Conversely, the absence of extreme cold events can foster population growth, range expansion, and ecological regime shifts. We discuss the effects of warming winters on species and ecosystems in tropical-temperate transition zones. In the 21st century, climate change-induced decreases in the frequency and intensity of extreme cold events are expected to facilitate the poleward range expansion of many tropical species. Our review highlights critical knowledge gaps for advancing understanding of the ecological implications of the tropicalization of temperate ecosystems in North America., (© 2021 John Wiley & Sons Ltd.)

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.

Despite intensive monitoring, temporary emigration from the sampling area can induce bias severe enough for managers to discard survival parameter estimates toward the terminus of the times series (terminal bias). Under random temporary emigration, unbiased parameters can be estimated with CJS models. However, unmodeled Markovian temporary emigration causes bias in parameter estimates, and an unobservable state is required to model this type of emigration. The robust design is most flexible when modeling temporary emigration, and partial solutions to mitigate bias have been identified; nonetheless, there are conditions were terminal bias prevails. Long-lived species with high adult survival and highly variable nonrandom temporary emigration present terminal bias in survival estimates, despite being modeled with the robust design and suggested constraints. Because this bias is due to uncertainty about the fate of individuals that are undetected toward the end of the time series, solutions should involve using additional information on survival status or location of these individuals at that time. Using simulation, we evaluated the performance of models that jointly analyze robust design data and an additional source of ancillary data (predictive covariate on temporary emigration, telemetry, dead recovery, or auxiliary resightings) in reducing terminal bias in survival estimates. The auxiliary resighting and predictive covariate models reduced terminal bias the most. Additional telemetry data were effective at reducing terminal bias only when individuals were tracked for a minimum of two years. High adult survival of long-lived species made the joint model with recovery data ineffective at reducing terminal bias because of small-sample bias. The naive constraint model (last and penultimate temporary emigration parameters made equal), was the least efficient, although still able to reduce terminal bias when compared to an unconstrained model. Joint analysis of several sources of data improved parameter estimates and reduced terminal bias. Efforts to incorporate or acquire such data should be considered by researchers and wildlife managers, especially in the years leading up to status assessments of species of interest. Simulation modeling is a very cost-effective method to explore the potential impacts of using different sources of data to produce high-quality demographic data to inform management.

Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last 20 years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected-value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We have also implemented these models in program MARK. This general framework could also be used by practitioners to consider constrained models of particular interest, or to model the relationship between within-primary-period parameters (e.g., state structure) and between-primary-period parameters (e.g., state transition probabilities).