Applied carrying capacity concept for integrating stormwater management and land use planning,a case study: The Kuantu plain of Taipei,Taiwan

The carrying capacity of land resources in terms of hydrologic consequences of land development is the basis of a proposed method for integrating the land planning process with stormwater management. Concepts of natural drainage such as soil moisture storage capacity for rainwater infiltration, and vegetated depressed surface for collecting storm runoff are used as criteria of performance control for ensuring balance between land development and hydrologic processes. A stormwater management model is developed which includes a land program, hydrologic process, and stormwater management techniques to simulate the hydrologic effects of various land development programs, and to estimate their land use performance standards. The model uses the Soil Cover Complex Method developed by the U.S. Soil Conservation Service to compute the hydrologic effects. The land use performance standards incorporated in this research include: pervious areas required, swale area required, and average depth of retention pond. The land characteristics of Kuantu Plain, Taipei, Taiwan are used as a case study to estimate land use performance standards of natural drainage. The model outputs provide useful guidance to help land use planners utilize land resources more efficiently.     

Single and multispecies systems: The the Eastern Tropical Atlantic

The commercial tuna fishery in the Eastern Tropical Atlantic is based on harvests from three species of tuna: Yellowfin, Skipjack, and Bigeye. Two models are developed to examine the bioeconomics of this fishery. In the first model, species are presumed ecologically independent and selectively harvested. In the second model, two of the species (Yellowfin and Skipjack) are assumed to be interspecific competitors and jointly harvested independent of the third species (Bigeye). Data on landings, effort, cost, and revenue are assembled allowing estimates of the bioeconomic parameters for the model where all species are independent and selectively harvested. Open access and bioeconomic equilibria are identified for a combination of cost and discount rates. The pristine, open access, and bioeconomic equilibria are also determined for the multispecies model based on parameters from the single species (independent) models and plausible values for interaction and joint production coefficients. Equilibria are compared, and management policies from a single- and multispecies perspective are explored.     

Incorporating natural enemies in an economic threshold for dynamically optimal pest management

The control of pests by their natural enemies represents an important regulating ecosystem service that helps maintain the stability of crop ecosystems. These services, however, are often ignored in pest management decision making. In addition, the use of broad-spectrum insecticides can damage the populations of natural enemies, reducing the cost-effectiveness of insecticide investment if unaccounted for in treatment decisions.The existing literature on modeling of biological control of insect pests has generally focused on simulations of the population dynamics of pest and natural enemy species and the processes underlying pest control. But agriculture is a managed ecosystem where predator–prey relationships are heavily influenced by human managers. In modeling managerial choices, this study develops an intra-seasonal dynamic bioeconomic optimization model for insecticide-based pest management that explicitly takes into account both the biological control effect of natural enemies on pest density and the nontarget mortality effect of insecticides on the level of natural pest control supplied. The model captures predator–prey interactions, linking them to crop growth and yield damage functions, which in turn are evaluated in a dynamic optimization framework. We introduce a new decision rule for judicious insecticide decisions using a natural enemy-adjusted economic threshold. This threshold represents the pest population density at which insecticide control becomes optimal in spite of the opportunity cost of injury to natural enemies of the target pest. Using field data from Michigan, the model is applied to the case of soybean aphid (Aphis glycines, Matsumura), a recent invasive pest of soybean (Glycine max), whose management is of both economic and environmental importance to the North Central region of the United States. As illustrated by the numerical examples, such natural enemy-adjusted threshold is likely to lead to fewer recommendations for insecticide use than naïve models that ignore natural enemies, resulting in less insecticide use, while maintaining profitability for farmers that rely on chemical pest control methods.The bioeconomic model developed in this study can be used to conduct a wide variety of analyses such as identifying dynamically optimal spray strategies and estimating the implied economic value of natural control services. Furthermore, with the incorporation of inter-year carry-over factors, such as overwintering of pests and natural enemies, the current model can contribute to building multi-year models for studying long-term pest management.     

Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.     

Dependence of the Endangered Black‐Capped Vireo on Sustained Cowbird Management

Conservation‐reliant species depend on active management, even after surpassing recovery goals, for protection from persistent threats. Required management may include control of another species, habitat maintenance, or artificial recruitment. Sometimes, it can be difficult to determine whether sustained management is required. We used nonspatial stochastic population projection matrix simulation and a spatially explicit population model to estimate the effects of parasitism by a brood parasite, the Brown‐headed Cowbird (Moluthrus ater), on a population of endangered Black‐capped Vireos (Vireo atricapilla). We simulated parasitism as a percentage of breeding vireo pairs experiencing decreased fecundity due to cowbirds. We estimated maximum sustainable parasitism (i.e., highest percentage of parasitized vireo breeding pairs for which population growth is ≥1) with the nonspatial model under multiple scenarios designed to assess sensitivity to assumptions about population growth rate, demographic effects of parasitism, and spatial distribution of parasitism. We then used the spatially explicit model to estimate cumulative probabilities of the population falling below the population recovery target of 1000 breeding pairs for a range of parasitism rates under multiple scenarios. We constructed our models from data on vireos collected on the Fort Hood Military Reservation, Texas (U.S.A.). Estimates of maximum sustainable parasitism rates ranged from 9–12% in scenarios with a low (6%) vireo population growth rate to 49–60% in scenarios with a high (24%) growth rate. Sustained parasitism above 45–85%, depending on the scenario, would likely result in the Fort Hood Vireo population dropping below its recovery goal within the next 25 years. These estimates suggest that vireos, although tolerant of low parasitism rates, are a conservation‐reliant species dependent on cowbird management. Dependencia de Vireo atricapilla, Especie en Peligro, hacia el Manejo Sostenido de Moluthurs ater     

Combining multistate capture-recapture data with tag recoveries to estimate demographic parameters

Matrix population models that allow an animal to occupy more than one state over time are important tools for population and evolutionary ecologists. Definition of state can vary, including location for metapopulation models and breeding state for life history models. For populations whose members can be marked and subsequently reencountered, multistate mark-recapture models are available to estimate the survival and transition probabilities needed to construct population models. Multistate models have proved extremely useful in this context, but they often require a substantial amount of data and restrict estimation of transition probabilities to those areas or states subjected to formal sampling effort. At the same time, for many species, there are considerable tag recovery data provided by the public that could be modeled in order to increase precision and to extend inference to a greater number of areas or states. Here we present a statistical model for combining multistate capture-recapture data (e.g., from a breeding ground study) with multistate tag recovery data (e.g., from wintering grounds). We use this method to analyze data from a study of Canada Geese (Branta canadensis) in the Atlantic Flyway of North America. Our analysis produced marginal improvement in precision, due to relatively few recoveries, but we demonstrate how precision could be further improved with increases in the probability that a retrieved tag is reported.     

Analytical Solutions to Trade‐Offs between Size of Protected Areas and Land‐Use Intensity

Abstract: Land‐use change is affecting Earth's capacity to support both wild species and a growing human population. The question is how best to manage landscapes for both species conservation and economic output. If large areas are protected to conserve species richness, then the unprotected areas must be used more intensively. Likewise, low‐intensity use leaves less area protected but may allow wild species to persist in areas that are used for market purposes. This dilemma is present in policy debates on agriculture, housing, and forestry. Our goal was to develop a theoretical model to evaluate which land‐use strategy maximizes economic output while maintaining species richness. Our theoretical model extends previous analytical models by allowing land‐use intensity on unprotected land to influence species richness in protected areas. We devised general models in which species richness (with modified species‐area curves) and economic output (a Cobb–Douglas production function) are a function of land‐use intensity and the proportion of land protected. Economic output increased as land‐use intensity and extent increased, and species richness responded to increased intensity either negatively or following the intermediate disturbance hypothesis. We solved the model analytically to identify the combination of land‐use intensity and protected area that provided the maximum amount of economic output, given a target level of species richness. The land‐use strategy that maximized economic output while maintaining species richness depended jointly on the response of species richness to land‐use intensity and protection and the effect of land use outside protected areas on species richness within protected areas. Regardless of the land‐use strategy, species richness tended to respond to changing land‐use intensity and extent in a highly nonlinear fashion.     

Predicting Recovery Criteria for Threatened and Endangered Plant Species on the Basis of Past Abundances and Biological Traits

Recovery plans for species listed under the U.S. Endangered Species Act are required to specify measurable criteria that can be used to determine when the species can be delisted. For the 642 listed endangered and threatened plant species that have recovery plans, we applied recursive partitioning methods to test whether the number of individuals or populations required for delisting can be predicted on the basis of distributional and biological traits, previous abundance at multiple time steps, or a combination of traits and previous abundances. We also tested listing status (threatened or endangered) and the year the recovery plan was written as predictors of recovery criteria. We analyzed separately recovery criteria that were stated as number of populations and as number of individuals (population‐based and individual‐based criteria, respectively). Previous abundances alone were relatively good predictors of population‐based recovery criteria. Fewer populations, but a greater proportion of historically known populations, were required to delist species that had few populations at listing compared with species that had more populations at listing. Previous abundances were also good predictors of individual‐based delisting criteria when models included both abundances and traits. The physiographic division in which the species occur was also a good predictor of individual‐based criteria. Our results suggest managers are relying on previous abundances and patterns of decline as guidelines for setting recovery criteria. This may be justifiable in that previous abundances inform managers of the effects of both intrinsic traits and extrinsic threats that interact and determine extinction risk. Predicción de Criterios de Recuperación para Especies de Plantas en Peligro y Amenazadas con Base en Abundancias Pasadas y Atributos Biológicos     

Pollution,habitat loss,fishing, and climate change as critical threats to penguins

Cumulative human impacts across the world's oceans are considerable. We therefore examined a single model taxonomic group, the penguins (Spheniscidae), to explore how marine species and communities might be at risk of decline or extinction in the southern hemisphere. We sought to determine the most important threats to penguins and to suggest means to mitigate these threats. Our review has relevance to other taxonomic groups in the southern hemisphere and in northern latitudes, where human impacts are greater. Our review was based on an expert assessment and literature review of all 18 penguin species; 49 scientists contributed to the process. For each penguin species, we considered their range and distribution, population trends, and main anthropogenic threats over the past approximately 250 years. These threats were harvesting adults for oil, skin, and feathers and as bait for crab and rock lobster fisheries; harvesting of eggs; terrestrial habitat degradation; marine pollution; fisheries bycatch and resource competition; environmental variability and climate change; and toxic algal poisoning and disease. Habitat loss, pollution, and fishing, all factors humans can readily mitigate, remain the primary threats for penguin species. Their future resilience to further climate change impacts will almost certainly depend on addressing current threats to existing habitat degradation on land and at sea. We suggest protection of breeding habitat, linked to the designation of appropriately scaled marine reserves, including in the High Seas, will be critical for the future conservation of penguins. However, large‐scale conservation zones are not always practical or politically feasible and other ecosystem‐based management methods that include spatial zoning, bycatch mitigation, and robust harvest control must be developed to maintain marine biodiversity and ensure that ecosystem functioning is maintained across a variety of scales. Contaminación, Pérdida de Hábitat, Pesca y Cambio Climático como Amenazas Críticas para los Pingüinos     

Testing the effectiveness of capture mark recapture population estimation techniques using a computer simulation with known population size

Estimation of small mammal population sizes is important for monitoring ecosystem condition and for conservation. Here, we test the accuracy of standard methods of population size estimation using Capture-Mark-Recapture (CMR) on a simulated population of agents. The use of a computer simulation allows complete control of population sizes and behaviors, thereby avoiding assumptions that may be violated in real populations. We find that the recommended protocol for CMR sampling, using uniformly distributed traps, consistently overestimates population sizes by as much as 100% when studies are conducted over only two trapping periods. More than 20 trapping periods are required before this method, or that of placing traps randomly, gives an accurate estimation of population size (i.e., within a 95% confidence limit of the actual value). Non-random sampling, by placing traps on runways used by small mammals, produces the most accurate, and least variable, estimates of population. However, we show that around 10 trapping periods are still required to produce an accurate population estimate using this method. Given that most real populations do not comply with the ‘ideal’ assumptions made by CMR, we suggest that population estimates based on CMR may be fundamentally flawed, and recommend that protocols for CMR population estimation methods may need revising.     

Effects of uncertainty and variability on population declines and IUCN Red List classifications

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age‐structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.     

From Population Viability Analysis to Coviability of Farmland Biodiversity and Agriculture

Substantial declines in farmland biodiversity have been reported in Europe for several decades. Agricultural changes have been identified as a main driver of these declines. Although different agrienvironmental schemes have been implemented, their positive effect on biodiversity is relatively unknown. This raises the question as to how to reconcile farming production and biodiversity conservation to operationalize a sustainable and multifunctional agriculture. We devised a bioeconomic model and conducted an analysis based on coviability of farmland biodiversity and agriculture. The coviability approach extended population viability analyses by including bioeconomic risk. Our model coupled stochastic dynamics of both biodiversity and farming land‐uses selected at the microlevel with public policies at the macrolevel on the basis of financial incentives (taxes or subsidies) for land uses. The coviability approach made it possible for us to evaluate bioeconomic risks of these public incentives through the probability of satisfying a mix of biodiversity and economic constraints over time. We calibrated the model and applied it to a community of 34 common birds in metropolitan France at the small agricultural regions scale. We identified different public policies and scenarios with tolerable (0–0%) agroecological risk and modeled their outcomes up to 2050. Budgetary, economic, and ecological (based on Farmland Bird Index) constraints were essential to understanding the set of viable public policies. Our results suggest that some combinations of taxes on cereals and subsidies on grasslands could be relevant to develop a multifunctional agriculture. Moreover, the flexibility and multicriteria viewpoint underlying the coviability approach may help in the implementation of adaptive management. Del Análisis de Viabilidad Poblacional a la Co‐Viabilidad de la Agricultura y la Biodiversidad de las Tierras de Cultivo     

Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments

This article reports on the application of dasymetric mapping techniques to interpolate and disaggregate block group population counts to smaller areal units (i.e., tax parcels) and derive surface population models with more realistic representations of population distributions in our residential study area in Miami-Dade, Florida. Three methods of dasymetric interpolation were tested: (i) binary, (ii) three-class, and (iii) limiting variable. Our enhanced limiting variable approach introduced an adjustment factor for parcel vacancy rates in the dasymetric calculations, and applied dasymetric mapping techniques to disaggregate future population projections to the tax lot level of analysis. The limiting variable interpolation generated the lowest coefficient of variation (0.188), followed by the three-class interpolation (0.645). We also found that population densities vary substantially within land use classes of single family, medium density and high density classes, and these variations also highlighted the importance of incorporating vacancy rates when interpolating population counts to categorical land use data. Overall, the enhanced dasymetric mapping technique is particularly useful for examining the impact of sea-level rise as its derivatives are compatible with high resolution LiDAR and orthoimagery data. Coastal counties can also benefit from such high resolution surface population models to enhance the accuracy of hazard-related vulnerability assessments and to guide the development of relevant shore zone conservation and adaptation strategies.     

Setting Realistic Recovery Targets for Two Interacting Endangered Species,Sea Otter and Northern Abalone

Failure to account for interactions between endangered species may lead to unexpected population dynamics, inefficient management strategies, waste of scarce resources, and, at worst, increased extinction risk. The importance of species interactions is undisputed, yet recovery targets generally do not account for such interactions. This shortcoming is a consequence of species‐centered legislation, but also of uncertainty surrounding the dynamics of species interactions and the complexity of modeling such interactions. The northern sea otter (Enhydra lutris kenyoni) and one of its preferred prey, northern abalone (Haliotis kamtschatkana), are endangered species for which recovery strategies have been developed without consideration of their strong predator–prey interactions. Using simulation‐based optimization procedures from artificial intelligence, namely reinforcement learning and stochastic dynamic programming, we combined sea otter and northern abalone population models with functional‐response models and examined how different management actions affect population dynamics and the likelihood of achieving recovery targets for each species through time. Recovery targets for these interacting species were difficult to achieve simultaneously in the absence of management. Although sea otters were predicted to recover, achieving abalone recovery targets failed even when threats to abalone such as predation and poaching were reduced. A management strategy entailing a 50% reduction in the poaching of northern abalone was a minimum requirement to reach short‐term recovery goals for northern abalone when sea otters were present. Removing sea otters had a marginally positive effect on the abalone population but only when we assumed a functional response with strong predation pressure. Our optimization method could be applied more generally to any interacting threatened or invasive species for which there are multiple conservation objectives. Definición de Metas de Recuperación Realistas para Dos Especies en Peligro Interactuantes, Enhydra lutris y Haliotis kamtschatkana     

Using global sensitivity analysis of demographic models for ecological impact assessment

Population viability analysis (PVA) is widely used to assess population‐level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input‐parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input‐parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea‐level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions.     

Identifying key species in ecosystems with stochastic sensitivity analysis

The development of approaches to estimate the vulnerability of biological communities and ecosystems to extirpations and reductions of species is a central challenge of conservation biology. One key aim of this challenge is to develop quantitative approaches to estimate and rank interaction strengths and keystoneness of species and functional groups, i.e. to quantify the relative importance of species. Network analysis can be a powerful tool for this because certain structural aspects of ecological networks are good indicators of the mechanisms that maintain co-evolved, biotic interactions. A static view of ecological networks would lead us to focus research on highly-central species in food webs (topological key players in ecosystems). There are a variety of centrality indices, developed for several types of ecological networks (e.g. for weighted and un-weighted webs). However, truly understanding extinction and its community-wide effects requires the use of dynamic models. Deterministic dynamic models are feasible when population sizes are sufficiently large to minimize noise in the overall system. In models with small population sizes, stochasticity can be modelled explicitly. We present a stochastic simulation-based ecosystem model for identification of “dynamic key species” in situations where stochastic models are appropriate. To demonstrate this approach, we simulated ecosystem dynamics and performed sensitivity analysis using data from the Prince William Sound, Alaska ecosystem model. We then compare these results to those of purely topological analyses and deterministic dynamic (Ecosim) studies. We present the relationships between various topological and dynamic indices and discuss their biological relevance. The trophic group with the largest effect on others is nearshore demersals, the species mostly sensitive to others is halibut, and the group of both considerable effect on and sensitivity to others is juvenile herring. The most important trophic groups in our dynamical simulations appear to have intermediate trophic levels.     

Econometric modeling of fisheries with complex life histories: Avoiding biological management failures

Economics of the fishery has focused on the wastefulness of common pool resource exploitation. Pure open access fisheries dissipate economic rents and degrade biological stocks. Biologically managed fisheries also dissipate rents but are thought to hold biological stocks at desired levels. We develop and estimate an empirical bioeconomic model of the Gulf of Mexico gag fishery that questions the presumptive success of biological management. Unlike previous bioeconomic life history studies, we provide a way to circumvent calibration problems by embedding our estimation routine directly in the dynamic bioeconomic model. We nest a standard biological management model that accounts for complex life history characteristics of the gag. Biological intuition suggests that a spawning season closure will reduce fishing pressure and increase stocks, and simulations of the biological management model confirm this finding. However, simulations of the empirical bioeconomic model suggest that these intended outcomes of the spawning closure do not materialize. The behavioral response to the closure appears to be so pronounced that it offsets the restriction in allowable fishing days. Our results indicate that failure to account for fishing behavior may play an important role in fishery management failures.     

Single-species versus multiple-species models: the economic implications

Ecologists frequently note the importance of modelling entire ecosystems rather than individual species, but most bioeconomic models in the current literature focus on a single species. While the mathematical difficulty of modelling multiple species may be substantial, it is important to recognise the implications of the single-species assumption to a model’s results. In this paper, the authors address the economic significance of this assumption through the development of an analytical multiple-species model and demonstrate the importance of ecological interrelationships and economic values to the survival of endangered species.     

Predicting [corrected] extinction risk in spite of predator-prey oscillations.

Most population viability analyses (PVA) assume that the effects of species interactions are subsumed by population-level parameters. We examine how robust five commonly used PVA models are to violations of this assumption. We develop a stochastic, stage-structured predator-prey model and simulate prey population vital rates and abundance. We then use simulated data to parameterize and estimate risk for three demographic models (static projection matrix, stochastic projection matrix, stochastic vital rate matrix) and two time series models (diffusion approximation [DA], corrupted diffusion approximation [CDA]). Model bias is measured as the absolute deviation between estimated and observed quasi-extinction risk. Our results highlight three generalities about the application of single-species models to multi-species conservation problems. First, our collective model results suggest that most single-species PVA models overestimate extinction risk when species interactions cause periodic variation in abundance. Second, the DA model produces the most (conservatively) biased risk forecasts. Finally, the CDA model is the most robust PVA to population cycles caused by species interactions. CDA models produce virtually unbiased and relatively precise risk estimates even when populations cycle strongly. High performance of simple time series models like the CDA owes to their ability to effectively partition stochastic and deterministic sources of variation in population abundance.