Conditions and Limitations on Learning in the Adaptive Management of Mallard Harvests

In 1995, the United States Fish and Wildlife Service adopted a protocol for the adaptive management of waterfowl hunting regulations (AHM) to help reduce uncertainty about the magnitude of sustainable harvests. To date, the AHM process has focused principally on the midcontinent population of mallards (Anas platyrhynchos), whose dynamics are described by 4 alternative models. Collectively, these models express uncertainty (or disagreement) about whether harvest is an additive or a compensatory form of mortality and whether the reproductive process is weakly or strongly density-dependent. Each model is associated with a probability or weight, which describes its relative ability to predict changes in population size. These Bayesian probabilities are updated annually using a comparison of population size predicted under each model with that observed by a monitoring program. The current AHM process is passively adaptive, in the sense that there is no a priori consideration of how harvest decisions might affect discrimination among models. We contrast this approach with an actively adaptive approach, in which harvest decisions are used in part to produce the learning needed to increase long-term management performance. Our investigation suggests that the passive approach is expected to perform nearly as well as an optimal actively adaptive approach, particularly considering the nature of the model set, management objectives and constraints, and current regulatory alternatives. We offer some comments about the nature of the biological hypotheses being tested and describe some of the inherent limitations on learning in the AHM process

Conditions and Limitations on Learning in the Adaptive Management of Mallard Harvests

In 1995, the United States Fish and Wildlife Service adopted a protocol for the adaptive management of waterfowl hunting regulations (AHM) to help reduce uncertainty about the magnitude of sustainable harvests. To date, the AHM process has focused principally on the midcontinent population of mallards (Anas platyrhynchos), whose dynamics are described by 4 alternative models. Collectively, these models express uncertainty (or disagreement) about whether harvest is an additive or a compensatory form of mortality and whether the reproductive process is weakly or strongly density-dependent. Each model is associated with a probability or weight, which describes its relative ability to predict changes in population size. These Bayesian probabilities are updated annually using a comparison of population size predicted under each model with that observed by a monitoring program. The current AHM process is passively adaptive, in the sense that there is no a priori consideration of how harvest decisions might affect discrimination among models. We contrast this approach with an actively adaptive approach, in which harvest decisions are used in part to produce the learning needed to increase long-term management performance. Our investigation suggests that the passive approach is expected to perform nearly as well as an optimal actively adaptive approach, particularly considering the nature of the model set, management objectives and constraints, and current regulatory alternatives. We offer some comments about the nature of the biological hypotheses being tested and describe some of the inherent limitations on learning in the AHM process

Survival rates of band‐tailed pigeons estimated using passive integrated transponder tags

Obtaining survival estimates on the Interior population of band-tailed pigeons (Patagioenas fasciata) is challenging because they are trap shy, but the joint use of passive integrated transponder (PIT) tags and bands is a potential solution. We investigated the use of PIT tags to passively recapture band‐tailed pigeon at 3 locations in New Mexico, USA, to estimate survival. From 2013–2015, we captured, banded, and marked \u3e600 individual band‐tailed pigeons with PIT tags. To estimate annual survival rates, we used a Barker multi‐state joint live and dead encounters and resighting model. Survival models excluding transience had survival estimates across site, sex, and year of 0.86 (95% CI = 0.84–0.88) for after hatch year birds and 0.63 (95% CI = 0.48–0.76) for hatch year birds. These results are consistent with other survival estimates reported for the Interior population of band‐tailed pigeons using band return data and potentially provide an effective alternative method of monitoring survival of this population

Structured decision making as a conceptual framework to identify thresholds for conservation and management

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changing in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of he decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (systems models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision threshold inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structures decision process that include all three kinds of thresholds

Tourism‑supported working lands sustain a growing jaguar population in the Colombian Llanos

Understanding large carnivore demography on human-dominated lands is a priority to inform conservation strategies, yet few studies examine long-term trends. Jaguars (Panthera onca) are one such species whose population trends and survival rates remain unknown across working lands. We integrated nine years of camera trap data and tourist photos to estimate jaguar density, survival, abundance, and probability of tourist sightings on a working ranch and tourism destination in Colombia. We found that abundance increased from five individuals in 2014 to 28 in 2022, and density increased from 1.88 ± 0.87 per 100 km2 in 2014 to 3.80 ± 1.08 jaguars per 100 km2 in 2022. The probability of a tourist viewing a jaguar increased from 0% in 2014 to 40% in 2020 before the Covid-19 pandemic. Our results are the first robust estimates of jaguar survival and abundance on working lands. Our findings highlight the importance of productive lands for jaguar conservation and suggest that a tourism destination and working ranch can host an abundant population of jaguars when accompanied by conservation agreements and conflict interventions. Our analytical model that combines conventional data collection with tourist sightings can be applied to other species that are observed during tourism activities. Entender los patrones demográficos de los grandes carnívoros al interior de paisajes antrópicos es fundamental para el diseño de estrategias de conservación efectivas. En el Neotrópico, el jaguar (Panthera onca) es una de estas especies cuyas tendencias poblacionales y tasas de supervivencia en paisajes productivos son desconocidas. Para entender mejor estas dinámicas, integramos nueve años de fototrampeo junto a fotos de turistas para estimar la densidad, supervivencia, abundancia y probabilidad de avistamiento de esta especie en una finca ganadera y destino turístico en Colombia. Entre 2014 y 2022 encontramos que la abundancia incrementó de cinco a 28 individuos y la densidad de 1.88 ± 0.87 jaguares/ 100 km2 a 3.80 ± 1.08 jaguares/ 100 km2. La probabilidad de avistamiento por turistas aumentó de 0% en 2014 a 40% en 2020 antes de la pandemia del Covid-19. Nuestros resultados presentan las primeras estimaciones robustas de abundancia y supervivencia de este felino en paisajes antrópicos dónde el manejo de sistemas productivos combinados con turismo e intervenciones para la mitigación del conflicto puede albergar poblaciones abundantes de jaguares, demostrando su importancia para la conservación de esta especie. Nuestro modelo, al combinar datos convencionales con avistamientos, podría ser aplicado a otras especies observadas durante actividades turísticas. Supplemental files attached below

Catastrophic wildfire and number of populations as factors influencing risk of extinction for Gila trout (Oncorhynchus gilae)

We used the computer program RAMAS to explore the sensitivity of an extinction-risk model for the Gila trout (Oncorhynchus gilae) to management of wildfires and number of populations of the species. The Gila trout is an endangered salmonid presently restricted to very few headwaters of the Gila and San Francisco river tributaries in southwestern New Mexico. Life history data for 10 extant populations were used to examine sensitivity of the species' viability to changes in a variety of factors including population size, fecundity, life stage structure, number of populations, severity and probability of forest fires, and a regulated fishery. The probability and severity of forest fires and number of populations had the greatest effect on viability. Results indicate that successful conservation of Gila trout requires establishment of additional populations and reduction of the severity of forest fires through a program incorporating more frequent, but less severe, fires.Peer reviewedZoolog

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions

Mechanisms of base selection by human single-stranded selective monofunctional uracil-DNA glycosylase

hSMUG1 (human single-stranded selective monofunctional uracil-DNA glyscosylase) is one of three glycosylases encoded within a small region of human chromosome 12. Those three glycosylases, UNG (uracil-DNA glycosylase), TDG (thymine-DNA glyscosylase), and hSMUG1, have in common the capacity to remove uracil from DNA. However, these glycosylases also repair other lesions and have distinct substrate preferences, indicating that they have potentially redundant but not overlapping physiological roles. The mechanisms by which these glycosylases locate and selectively remove target lesions are not well understood. In addition to uracil, hSMUG1 has been shown to remove some oxidized pyrimidines, suggesting a role in the repair of DNA oxidation damage. In this paper, we describe experiments in which a series of oligonucleotides containing purine and pyrimidine analogs have been used to probe mechanisms by which hSMUG1 distinguishes potential substrates. Our results indicate that the preference of hSMUG1 for mispaired uracil over uracil paired with adenine is best explained by the reduced stability of a duplex containing a mispair, consistent with previous reports with Escherichia coli mispaired uracil-DNA glycosylase. We have also extended the substrate range of hSMUG1 to include 5-carboxyuracil, the last in the series of damage products from thymine methyl group oxidation. The properties used by hSMUG1 to select damaged pyrimidines include the size and free energy of solvation of the 5-substituent but not electronic inductive properties. The observed distinct mechanisms of base selection demonstrated for members of the uracil glycosylase family help explain how considerable diversity in chemical lesion repair can be achieved