Evaluating Habitat as a Surrogate for Population Viability Using a Spatially Explicit Population Model

Because data for conservation planning are always limited, surrogates are often substituted for intractable measurements such as species richness or population viability. We examined the ability of habitat quality to act as a surrogate for population performance for both Red-shouldered Hawks (Buteo lineatus) and Northern Goshawks (Accipiter gentilis). We compared simple measures of habitat quality to estimates of population growth rates obtained from a spatially explicit model of population dynamics. We found that habitat quality was a relatively poor predictor of simulated population growth rates for several reasons. First, a relatively small proportion of the potential habitat for each species served as population sources in our simulations--15% for Red-shouldered Hawks and 2% for Goshawks. Second, when habitat quality correctly predicted demographic sources on the landscape, it consistently underestimated the contribution of these areas to the population. In areas where habitat quality correctly anticipated the presence of demographic sinks, we found no useful quantitative relationship between the two measures. Our simulation model captured the influence of habitat quality on the hawk populations, but it also incorporated interactions between dispersing individuals and landscape patterns. Thus, the discrepancies we observed likely reflected the influence of forest fragmentation and the spatial arrangement of forest patches on the populations. We conclude that simple measures of habitat quality will often be poor surrogates for population persistence, but that spatially explicit population models can help inform the development of better indices.     

Development and Parameterization of a Rain- and Fire-driven Model for Exploring Elephant Effects in African Savannas

We describe the development and parameterization of a grid-based model of African savanna vegetation processes. The model was developed with the objective of exploring elephant effects on the diversity of savanna species and structure, and in this formulation concentrates on the relative cover of grass and woody plants, the vertical structure of the woody plant community, and the distribution of these over space. Grid cells are linked by seed dispersal and fire, and environmental variability is included in the form of stochastic rainfall and fire events. The model was parameterized from an extensive review of the African savanna literature; when available, parameter values varied widely. The most plausible set of parameters produced long-term coexistence between woody plants and grass, with the tree–grass balance being more sensitive to changes in parameters influencing demographic processes and drought incidence and response, while less sensitive to fire regime. There was considerable diversity in the woody structure of savanna systems within the range of uncertainty in tree growth rate parameters. Thus, given the paucity of height growth data regarding woody plant species in southern African savannas, managers of natural areas should be cognizant of different tree species growth and damage response attributes when considering whether to act on perceived elephant threats to vegetation.     

Population Modeling of the Fish Species Roach (<Emphasis Type="BoldItalic">Rutilus rutilus</Emphasis>) to Investigate How Latitudinal Variations in Life History Traits Affect the Sensitivity to Anthropogenic Stress

Anthropogenic stressors, such as pollution and habitat degradation, have affected natural fish populations (and other species) by increased mortality and/or impaired reproduction. Depending on the life history of a population, the sensitivity to a particular stress varies. Demographic population models can be used to evaluate these differences and determine which populations are most sensitive to a specific impact. In the present study, five populations of roach (Rutilus rutilus) with different life histories were modeled and compared for sensitivities to reductions in fecundity or survival. The five populations reflected a north–south gradient with increasing life span, delayed maturity, and reduced fecundity at higher latitudes. Furthermore, three types of population models were used and compared. The population models were an unstructured model, a two-stage model with juveniles and adults and an age-structured model with age-dependent fecundity. The results of the population models showed that populations at higher latitudes are more sensitive to a reduction in survival but less sensitive to a reduction in fecundity. Current methods in ecological risk assessment are based on oversimplifications due to a lack of ecological theory. The use of population models may increase the ecological relevance and help produce better risk assessments. The two-stage model provided results that were similar to the results of the more complex age-structured model in most cases. The unstructured model, however, deviated significantly from the other two models.     

Rabbit Population Landscape-Scale Simulation to Investigate the Relevance of Using Rabbits in Regulatory Environmental Risk Assessment

This paper describes the development and testing of the ALMaSS rabbit model and its baseline, and subsequently its application to the question of lagomorph population vulnerability in environmental risk assessment (ERA). Development and testing following a pattern-oriented modelling protocol resulted in a model able to replicate local and landscape-level rabbit population patterns. We then tested how robust rabbit populations are to an (imaginary) extreme toxic stressor at a landscape level in a variety of landscapes, and to what extremes key uncertain model parameters must be pushed to cause extinctions. This was contrasted with the same (imaginary) toxic stressor applied to the already existing ALMaSS hare model. For EU risk assessment of plant protection products, these results clearly indicate that if the protection goal is population-level impacts, either in abundance and/or distribution, then the hare is a much more vulnerable species than the rabbit under all the conditions tested. Rabbits would only be more vulnerable than hares if the entire population were to be exposed simultaneously, when lower body mass would then be a critical factor. This did not occur even though the toxicant and exposure scenarios tested here were extreme and, in fragmented landscapes at scales used here, will not occur in reality from the use of plant protection products on crop fields. As well as specifically answering the question on rabbit versus hare vulnerability, this study generally illustrates the potential application of models for setting focal species for risk assessments.     

Atmospheric Change and the Diversity of Aquatic Invertebrates: Are We Missing the Boat?

The response of natural systems to atmospheric change may depend critically on species diversity and on the genetic diversity (variability) found within their respective populations. Yet, most surveys of aquatic invertebrates account for neither. This may be of particular concern for benthic populations in running waters because of the considerable variability and the fragmentary nature of these habitats (e.g. isolated watersheds). In such habitats, species with limited genetic variability and/or limited dispersal capabilities (genetically differentiated populations) may be unable to track rapid environmental change, and may be more susceptible to climatic perturbations. We present a conceptual framework to illustrate some of the potential problems of ignoring population genetics when considering the impacts of global atmospheric change. We then review a simple method to assess population genetic structure and we evaluate available data on the genetic structure of North American stream invertebrates. These data indicate that benthic taxa often consist of genetically differentiated local populations, or even previously unknown species. Accordingly, our limited knowledge of population structure among benthic invertebrates may result in the unwitting loss of genetic and/or species diversity. Enhanced taxonomic research incorporating molecular techniques is clearly warranted. Conservation strategies based on the preservation and remediation of a diversity of aquatic habitats are likely to be our best means of ensuring species and genetic diversity of invertebrate taxa.     

Impact of Channelization on Oyster Production: A Hydrodynamic-Oyster Population Model for Galveston Bay, Texas

A hydrodynamic-oyster population dynamics model was developed to assess the effect of a change in ship channel configuration under different freshwater inflow regimes and different future hydrologies on oyster (Crassostrea virginica) populations in Galveston Bay, Texas. The population dynamics model includes the effects of environmental conditions, predators, and the oyster parasite Perkinsus marinus on oyster populations. The hydrodynamic model includes the effects of wind stress, river runoff, tides, and oceanic exchange on the circulation of the Bay. Simulations were run for low, mean, and high freshwater inflow conditions under the present (1993) hydrology and predicted hydrologies for 2024 and 2049 that include anticipated water diversion projects to satisfy the freshwater demands of population growth in metropolitan Houston, Texas. Simulation results show that oyster biomass was predicted to increase after enlargement of the ship channel. Oyster biomass is expected to increase on about 53% of total reef acreage when averaged over a 50-yr time span. Oyster reef acreage characterized by increased biomass after channel enlargement increases moderately under the present hydrology and the 2049 hydrology, but decreases slightly in 2024. Lower biomass in 2024 is due to reduced freshwater inflow and increased saltwater intrusion that pushes the optimal areas for oyster growth somewhat farther upbay than in 2049. Declines in oyster biomass, noted in most simulations in downbay reaches, were more than balanced by increased oyster biomass upbay. The differential between upbay and downbay reefs can be explained by an increase in mortality from Perkinsus marinus downbay and saltwater intrusion upbay that expands the area characterized by moderate salinities. The 20th century history of Galveston Bay is one of expansion of isohaline structure and increased oyster production as a result of anthropogenic modification of bay physiography. The salinity gradient of the 1990s, however, is not in equilibrium with the distribution of hard substrate required for oyster growth, that reflects an earlier equilibrium with the pre-1900s hydrodynamics. Increased saltwater intrusion is normally disadvantageous to oyster populations; but, in this case, channel enlargement further expands the salinity gradient upbay and outward (east and west) from the channel. As a result, in most years, oyster biomass is increased because moderate salinities cover more of the pre-1900s reef tracts where hard substrate is plentiful.     

Health impact assessment – A survey on quantifying tools

Integrating human health into prospective impact assessments is known to be challenging. This is true for both approaches: dedicated health impact assessments (HIA) as well as inclusion of health into more general impact assessments. Acknowledging the full range of participatory, qualitative, and quantitative approaches, this study focuses on the latter, especially on computational tools for quantitative health modelling. We conducted a survey among tool developers concerning the status quo of development and availability of such tools; experiences made with model usage in real-life situations; and priorities for further development. Responding toolmaker groups described 17 such tools, most of them being maintained and reported as ready for use and covering a wide range of topics, including risk & protective factors, exposures, policies, and health outcomes. In recent years, existing models have been improved and were applied in new ways, and completely new models emerged. There was high agreement among respondents on the need to further develop methods for assessment of inequalities and uncertainty. The contribution of quantitative modeling to health foresight would benefit from building joint strategies of further tool development, improving the visibility of quantitative tools and methods, and engaging continuously with actual and potential users.     

Genetic Patterns as a Tool for Monitoring and Assessment of Environmental Impacts: the Example of Genetic Ecotoxicology

Genetic techniques are widely applied to assess the effects of environmental variation or exogenous impacts on populations. Many studies fail to provide convincing evidence that genetic patterns are attributable to the factors proposed. We assert that a rigorous approach must be followed to distinguish patterns of natural genetic variation from genetic change. We review the principles of natural genetic variation and population structure and present them in the context of their interaction with biological and stochastic sources of genetic change. Key steps are articulated which are often overlooked when applying genetic techniques. These are consideration of population structure when comparing populations, developing a specific test against a model of genetic change, and testing for evidence of direct effects and mechanisms of impact. Use of these steps in genetic ecotoxicology is described in detail and includes three primary methods of linking genetic patterns to the effects of contaminants. We propose that this combined approach is critical to the use of genetic techniques to assess and predict long-term effects of environmental impacts on populations or ecosystems.     

Optimal Sampling and Problematic Likelihood Functions in a Simple Population Model

Markov chains provide excellent statistical models for studying many natural phenomena that evolve with time. One particular class of continuous-time Markov chain, called birth–death processes, can be used for modelling population dynamics in fields such as ecology and microbiology. The challenge for the practitioner when fitting these models is to take measurements of a population size over time in order to estimate the model parameters, such as per capita birth and death rates. In many biological contexts, it is impractical to follow the fate of each individual in a population continuously in time, so the researcher is often limited to a fixed number of measurements of population size over the duration of the study. We show that, for a simple birth–death process, with positive Malthusian growth rate, subject to common practical constraints, there is an optimal schedule for measuring the population size that minimises the expected confidence region of the parameter estimates. Throughout our exposition of the optimal experimental design, we compare it to a simpler equidistant design, where the population is sampled at regular intervals. This is an experimental design worthy of comparison since it can represent a much simpler design to implement in practice. In order to find optimal experimental designs for our population model, we make use of a combination of useful statistical machinery. Firstly, we use a Gaussian diffusion approximation of the underlying discrete-state Markov process, which allows us to obtain analytical expressions for Fisher’s information matrix (FIM), which is crucial to optimising the experimental design. We also make use of the cross-entropy method of stochastic optimisation for the purpose of maximising the determinant of FIM to obtain the optimal experimental designs. Our results show that the optimal schedule devised by others for a simple model of population growth without death can be extended, for large populations, to the two-parameter model that incorporates both birth and death. For the simple birth–death process, we find that the likelihood surface is also problematic and poses serious problems for point estimation and easily defining confidence regions. A Bayesian approach to inference is proposed as a way in which these problems could be circumvented.     

A comparison of fixed-site and non-fixed-site approaches for species protection

The efficacy of simultaneously advancing two distinct conceptual designs (referred to here as fixed-site and non-fixed-site) for species conservation and protection is addressed. In the literature, numerous models can be found that typically stem from a particular design, but rarely are comparisons made between approaches. This paper presents a more integrated optimization framework that models landowner behavior and species viabilities at a landscape scale. Regional demand for resource extraction is used as the economic driver, a variant of simulated annealing is used to solve the model under different species protection approaches, and a detailed species population simulator is utilized to measure biological responses. When directly comparing the outcomes of different species protection strategies from a case study in Oregon (USA), it was found that neither approach was universally superior in terms of financial value or degree of protection for two late seral forest dependent species.     

Application of a Life Cycle Simulation Model to Evaluate Impacts of Water Management and Conservation Actions on an Endangered Population of Chinook Salmon

Fisheries and water resource managers are challenged to maintain stable or increasing populations of Chinook salmon in the face of increasing demand on the water resources and habitats that salmon depend on to complete their life cycle. Alternative management plans are often selected using professional opinion or piecemeal observations in place of integrated quantitative information that could reduce uncertainty in the effects of management plans on population dynamics. We developed a stochastic life cycle simulation model for an endangered population of winter-run Chinook salmon in the Sacramento River, California, USA with the goal of providing managers a tool for more effective decision making and demonstrating the utility of life cycle models for resource management. Sensitivity analysis revealed that the input parameters that influenced variation in salmon escapement were dependent on which age class was examined and their interactions with other inputs (egg mortality, Delta survival, ocean survival). Certain parameters (river migration survival, harvest) that were hypothesized to be important drivers of population dynamics were not identified in sensitivity analysis; however, there was a large amount of uncertainty in the value of these inputs and their error distributions. Thus, the model also was useful in identifying future research directions. Simulation of variation in environmental inputs indicated that escapement was significantly influenced by a 10% change in temperature whereas larger changes in other inputs would be required to influence escapement. The model presented provides an effective demonstration of the utility of life cycle simulation models for decision making and provides fisheries and water managers in the Sacramento system with a quantitative tool to compare the impact of different resource use scenarios.     

Assessing national livestock populations for the production of methane emission inventories

In an effort to accurately assess national methane (CH₄) production levels, many African countries are producing inventories of their national livestock populations. Conducting accurate livestock surveys is an expensive and time-consuming process, but is necessary because the most readily available data on livestock production are often incomplete or unsatisfactory. This paper looks at practical approaches to collecting data on livestock production and on the reproductive parameters for livestock, with special reference to African pastoralist populations, and makes qualified recommendations as to how inventory teams might be able to produce more accurate assessments of their total livestock populations. The methodology used during Nigeria's National Livestock Census (1990–92) serves as a basis for a discussion of the issues.     

Landscape resistance to dispersal: simulating long-term effects of human disturbance on a small and isolated wolf population in southwestern Manitoba, Canada

Landscape fragmentation affects wildlife population viability, in part, through the effects it has on individual dispersal. In addition, some forms of human disturbance impinge on dispersal without physically fragmenting habitats. Here, we use the term "landscape resistance" to capture constraints to dispersal that cannot be linked directly to fragmentation. The extent to which landscape resistance can influence population persistence is not well understood. Agricultural development over the past 60?years has resulted in considerable habitat fragmentation in the Riding Mountain National Park (RMNP) region in southwestern Manitoba, Canada. We examined how park boundaries, roads outside park boundaries and negative human attitudes have altered dispersal success and population persistence. We examined whether stochastic disturbance, representing infectious disease epidemics, further reduced long-term population persistence for various scenarios. Finally, we assessed whether the simultaneous occurrence of the three features had additive effects. We simulated dispersal using HexSim, a spatially explicit individual-based population model, parameterised with data on wolves (Canis lupus) in the RMNP region. Simulations that separately accounted for negative human attitudes and roads outside the park boundaries exhibited lower mean population size than those that ignored these details. Increasing deflection from park boundaries did not appear to have significant impacts. Our results did not indicate the presence of additive effects, and scenarios incorporating all three features had similar results as that of roads. Stochastic disturbance further reduced mean population size. Our results do illustrate how less-visible human disturbances (i.e. those that do not clearly alter landscape characteristics) can significantly limit dispersal and population persistence.     

The role of interactions in long‐term population cycles

Changes in population size and structure depend not only on the internal dynamics of populations, but also on environmental coupling, human exploitation, and interaction with other species. Some of these factors are cyclic and predictable, while others are random. In some cases the effect of interactions is to destabilise the system and produce catastrophic changes, which are manifested as discontinuities. This paper reviews several models of marine populations which generate long‐term population changes, typically much longer than the generation time of the species involved. The output of these models is realistic and agrees with existing data, but in the absence of adequate time series it is not always possible to test or validate such models in a rigorous statistical sense. The paper concludes with a discussion of the role of such models in our understanding of long‐term changes in marine populations, and the practical implications of not always being able either to validate or falsify models needed for effective decisionmaking.     

Allozyme polymorphisms in horseshoe crabs, Carcinoscorpius rotundicauda, collected from polluted and unpolluted intertidal areas in Peninsular Malaysia

It has been widely reported that allozyme frequency variation is a potential indicator of heavy metal-induced impacts in aquatic populations. In the present study, wild populations of horseshoe crab (Carcinoscorpius rotundicauda) were collected from contaminated and uncontaminated sites of Peninsular Malaysia. By adopting horizontal starch gel electrophoresis, seven enzyme systems were used to study allozyme polymorphisms. Nine polymorphic loci were observed in C. rotundicauda. The relationships of allozyme variations with the concentrations of Cd, Cu, Ni, and Zn in sediments and in muscle tissues of horseshoe crabs were determined. Based on genetic distance, the lower mean value of Nei??s D (0.017) indicated that both of the contaminated populations of Kg. Pasir Puteh and Kuala Juru were very closely related when compared to the relatively uncontaminated Pantai Lido population. Higher heterozygosities were shown by the contaminated populations when compared to the uncontaminated population. Different allelic frequencies could be observed for the aldolase (ALD; E.C. 2.7.5.1) locus between the contaminated and uncontaminated populations of C. rotundicauda. The dendrogram of genetic relationships of the three populations of C. rotundicauda showed the same clustering pattern as the dendrograms are based on heavy metals in the sediments and in the horseshoe crabs?? abdominal muscles. From the F statistics, the present study showed that the three populations of horseshoe crabs were considered to have undergone moderate genetic differentiation with a mean F _ST value of 0.092 .The current results suggest that allozyme polymorphism in horseshoe crabs is a potential biomonitoring tool for metal contamination, although further validation is required.     

Assessing an exotic plant surveying program in the Mojave Desert, Clark County, Nevada, USA

Exotic species can threaten native ecosystems and reduce services that ecosystems provide to humans. Early detection of incipient populations of exotic species is a key step in containing exotics before explosive population growth and corresponding impacts occur. We report the results of the first three years of an exotic plant early detection and treatment program conducted along more than 3,000 km of transportation corridors within an area >1.5 million ha in the Mojave Desert, USA. Incipient populations of 43 exotic plant species were mapped using global positioning and geographic information systems. Brassica tournefortii (Sahara mustard) infested the most soil types (47% of 256) surveyed in the study area, while Nicotiana glauca (tree tobacco) and others currently occupy less than 5% of soil types. Malcolmia africana (African mustard) was disproportionately detected on gypsum soils, occurring on 59% of gypsum soil types compared to 27% of all surveyed soils. Gypsum soils constitute unique rare plant habitat in this region, and by conventional wisdom were not previously considered prone to invasion. While this program has provided an initial assessment of the landscape-scale distribution of exotic species along transportation corridors, evaluations of both the survey methods and the effectiveness of treating incipient populations are needed. An exotic plant information system most useful to resource mangers will likely include integrating planning oriented coarse-scale surveys, more detailed monitoring of targeted locations, and research on species life histories, community invasibility, and treatment effectiveness.     

Monitoring of Biodiversity Indicators in Boreal Forests: a Need for Improved Focus

The general principles of scale and coarse and fine filters have been widely accepted, but management agencies and industry are still grappling with the question of what to monitor to detect changes in forest biodiversity following forest management. Part of this problem can be attributed to the lack of focused questions for monitoring including absence of null models and predicted effects, a certain level of disconnect between research and management, and recognition that monitoring can be designed as a research question. Considerable research from the past decade has not been adequately synthesized to answer important questions, such as which species or forest attributes might be the best indicators of change. A disproportionate research emphasis has been placed on community ecology, and mostly on a few groups of organisms including arthropods, amphibians, migratory songbirds, and small mammals, while other species, including soil organisms, lichens, bats, raptors, some carnivores, and larger mammals remain less well-known. In most studies of community ecology, the question of what is the importance, if any, of the regularly observed subtle changes in community structures, and causes of observed changes is usually not answered. Hence, our ability to deal with questions of persistence is limited, and demographic research on regionally--defined key species (such as species linked to processes, species whose persistence may be affected, species with large home ranges, species already selected as indicators, and rare and threatened species) is urgently needed. Monitoring programs need to be designed to enable managers to respond to unexpected changes caused by forest management. To do this, management agencies need to articulate null models for monitoring that predict effects, focus fine--scale monitoring on key species (defined by local and regional research) in key habitats (rare, declining, important) across landscapes, and have a protocol in place to adapt management strategies to changes observed. Finally, agencies must have some way to determine and define when a significant change has occurred and to predict the persistence of species; this too should flow from a well--designed null model.     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

Preliminary Studies on the Population Genetics of the Central Stoneroller (Campostoma anomalum) from the Great Miami River Basin, Ohio

Molecular approaches are particularly useful for measuring genetic diversity and were applied to samples of central stonerollers obtained from sites along tributaries to the Great Miami River in Ohio. We used Random Amplified Polymorphic DNA (RAPD) analysis to assess the level of genetic diversity within and among these populations. RAPD analysis generates genetic profiles that were used to develop indices of genetic similarity. The RAPD method provides a cost effective means of generating an arbitrary sample of anonymous loci across the genome and generate a virtually unlimited set of loci for use in genetic analysis in the absence of specific sequence information. These attributes make RAPDs well suited for use in evaluating the diversity and assessing the potential vulnerability to exposure of populations across multiple spatial scales. The results demonstrate that a significant amount of structuring exists among populations analyzed to date and that a trend exists towards genetic diversity being an inverse function of site distance from the main stem as well as a being directly related to stream order. This indicates that populations farthest from main conduits or in lower order streams, and thereby most isolated, may be the most vulnerable populations to stressor exposure. It is hoped that information pertaining to genetic diversity, when integrated with other metrics of resource condition, will aid in making scientifically grounded decisions on resource management that enhance the probability of population survival and preserve natural evolutionary processes.     

Forecasting Daily Maximum Ozone Concentrations in the Athens Basin

In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.