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In this chapter, Corey J. A. Bradshaw and Barry W. Brook, discuss measures of biodiversity patterns followed by an overview of experimental design and associated statistical paradigms. Conservation biology is a highly multidisciplinary science employing methods from ecology, Earth systems science, genetics, physiology, veterinary science, medicine, mathematics, climatology, anthropology, psychology, sociology, environmental policy, geography, political science, and resource management. Here we focus primarily on ecological methods and experimental design. It is impossible to census all species in an ecosystem, so many different measures exist to compare biodiversity: these include indices such as species richness, Simpson's diversity, Shannon's index and Brouillin's index. Many variants of these indices exist. The scale of biodiversity patterns is important to consider for biodiversity comparisons: α (local), β (between‐site), and γ (regional or continental) diversity. Often surrogate species ‐ the number, distribution or pattern of species in a particular taxon in a particular area thought to indicate a much wider array of taxa ‐ are required to simplify biodiversity assessments. Many similarity, dissimilarity, clustering, and multivariate techniques are available to compare biodiversity indices among sites. Conservation biology rarely uses completely manipulative experimental designs (although there are exceptions), with mensurative (based on existing environmental gradients) and observational studies dominating. Two main statistical paradigms exist for comparing biodiversity: null hypothesis testing and multiple working hypotheses – the latter paradigm is more consistent with the constraints typical of conservation data and so should be invoked when possible. Bayesian inferential methods generally provide more certainty when prior data exist. Large sample sizes, appropriate replication and randomization are cornerstone concepts in all conservation experiments. Simple relative abundance time series (sequential counts of individuals) can be used to infer more complex ecological mechanisms that permit the estimation of extinction risk, population trends, and intrinsic feedbacks. The risk of a species going extinct or becoming invasive can be predicted using cross‐taxonomic comparisons of life history traits. Population viability analyses are essential tools to estimate extinction risk over defined periods and under particular management interventions. Many methods exist to implement these, including count‐based, demographic, metapopulation, and genetic. Many tools exist to examine how genetics affects extinction risk, of which perhaps the measurement of inbreeding depression, gene flow among populations, and the loss of genetic diversity with habitat degradation are the most important.

This chapter incorporates stochastic dynamics and statistical uncertainty into population viability analysis using population prediction intervals. For most species insufficient information exists to construct an accurate quantitative population model, but qualitative assessment of extinction risk can be performed using objective, population-based systems such as the International Union for Conservation of Nature and Natural Resources (IUCN) Red List criteria. IUCN lists substantial proportions of animal taxa as threatened with extinction during the next 100 years. Quantitative population viability analysis (PVA) uses a stochastic population model to estimate the probability of extinction of a population or species before a certain time. A population prediction interval (PPI) is the stochastic interval that includes the unknown population size at a specified future time with a given probability or confidence. The concept of population viability can be generalized to incorporate uncertainty and the Precautionary Principle, by employing the upper PPI with a specified probability. For example, the IUCN category of Vulnerable is defined by Criterion E as a 10% probability of extinction within 100 years. With uncertainty this naturally generalizes to categorizing a species as Vulnerable if the upper 90% PPI at 100 years includes extinction. As a direct statement about a future population size, a PPI is easier to interpret and communicate to population managers and decision makers than the alternative approach of estimating a confidence interval for the probability of extinction at a future time.

In this chapter, Thomas Brooks charts the history, state, and prospects of conservation planning and prioritization in terrestrial and aquatic habitats. Conservation planning and prioritization are essential, because both biodiversity and human population (and hence threats to biodiversity and costs and benefits of conservation) are distributed highly unevenly. Great attention has been invested in global biodiversity conservation prioritization on land over the last two decades, producing a broad consensus that reactive priority regions are concentrated in the tropical mountains and islands, and proactive priorities in the lowland tropical forests. Major remaining research fronts for global biodiversity conservation prioritization include the examination of cross‐taxon surrogacy, aquatic priorities, phylogenetic history, evolutionary process, ecosystem services, and costs of conservation. Maybe the most important tool for guiding conservation on the ground is the IUCN Red List of Threatened Species, which assesses the extinction risk of 41 415 species against quantitative categories and criteria, and provides data on their distributions, habitats, threats, and conservation responses. The predominant threat to biodiversity is the destruction of habitats, and so the primary conservation response must be to protect these areas through safeguarding key biodiversity areas. While protecting sites is essential for biodiversity conservation, persistence in the long term also requires the conservation of those landscape and seascape level ecological processes that maintain biodiversity.

This chapter discusses the use of niche models to help address the “what” and “where” questions in conservation biology as well as climate change effects. It first reviews the conceptual aspects of the “what” and “where” questions in conservation planning, focusing on topics such as inferences about extinction risk, identification of regions for species reintroductions, conservation reserve network planning, and considerations of how climate change may affect species distributions. Each of these conservation applications is then examined with respect to the conceptual framework laid out for ecological niche modeling. The chapter concludes by offering practical recommendations regarding calibration and evaluation of niche models.

Genome size varies widely among different organisms, and is not very closely correlated with complexity of the organism. In species with large genomes, most of the DNA does not code for genes. This chapter explores the “selfish DNA” hypothesis for genome size. It also discusses vertebrate cases of small genome size (e.g., pufferfish) and extraordinarily large genome size (e.g., species of salamanders). A consequence of the huge genome in these salamanders is that their brains are less complex. Michael Lynch has proposed that much of the variation in genome size may be explained by variation in effective population size. The chapter concludes with a discussion of how genome size may be related to extinction risk and hence, conservation biology.

This chapter reviews recent analytical models of sustainable harvesting of fluctuating populations without age structure that incorporate the risk of population collapse or extinction. Using diffusion theory it compares three classical harvesting strategies and one new strategy in terms of their harvest statistics and the mean time to population collapse or extinction. It uses simple analytical models to derive general principles and then apply these principles to more realistic, age-structured models of particular species to derive by simulation the optimal harvesting strategies.

This chapter concentrates the life-history patterns to show which traits of giant pandas (e.g., birth weight, growth rate) are significantly different from reproductive rates in related terrestrial carnivores. It also addresses how phylogenies or evolutionary trees can lend insight into what biological characteristics contribute to extinction risk. Characteristics that are indicative of population decline and small geographic ranges (rarity) are most salient. A phylogenetic analysis combined with a database of biological traits associated with extinction risk can be used in a predictive manner for explaining why some species are at risk and why some are not. Using phylogenies to examine the comparative biology of threatened species such as the giant panda will not only help this magnificent species but also leave a legacy to protect many other similar species. Furthermore, a panel reports updates criteria used in the consideration of previous reintroduction proposals.

Many species are going extinct, mainly due to anthropogenic activities and climate forcing. The ecological significance of extinction has been linked to the sequential order of species loss and whether the extinction risk of each species is associated with the life-history traits that play an important role in ecosystem functioning. Trait-based extinction scenarios have been applied to studies on the consequences of possible biodiversity-environment futures for ecosystem functioning across a range of freshwater, terrestrial, and marine habitats, and for a variety of ecosystem functions. This chapter proposes a model code for a range of extinction scenarios and illustrates its application at local and regional scales. It explains how non-random extinction scenarios can be implemented and presents a case study that demonstrates the implications of regional biodiversity loss on carbon cycling in the shelf sea sediments of the North Sea. More specifically, it examines the per capita effect of each macrofaunal species on sediment mixing using an index of benthic bioturbation.

The chapter “Stochastic Population Models” introduces the concept of stochasticity, why it is sometimes incorporated into models, the consequences of stochasticity for population models, and how these types of models are used to evaluate extinction risk. Ecological systems are (seemingly) governed by randomness, or “stochasticity.” A stochastic model is one that explicitly includes randomness in the prediction of state variable dynamics. Because these models have a random component, each model run will be unique and will rarely look like a deterministic simulation. In this chapter, simple unstructured and density-dependent models are presented to show core concepts, and extensions to structured and density-dependent models are given.

The harmful impacts of inbreeding are generally greater in species that naturally outbreed compared to those in inbreeding species, greater in stressful than benign environments, greater for fitness than peripheral traits, and greater for total fitness compared to its individual components. Inbreeding reduces survival and reproduction (i.e., it causes inbreeding depression), and thereby increases the risk of extinction. Inbreeding depression is due to increased homozygosity for harmful alleles and at loci exhibiting heterozygote advantage. Natural selection may remove (purge) the alleles that cause inbreeding depression, especially following inbreeding or population bottlenecks, but it has limited effects in small populations and usually does not completely eliminate inbreeding depression. Inbreeding depression is nearly universal in sexually reproducing organisms that are diploid or have higher ploidies.

Inbreeding reduces survival and reproduction (i.e. it causes inbreeding depression), and thereby increases extinction risk. Inbreeding depression is due to increased homozygosity for harmful alleles and at loci exhibiting heterozygote advantage. Inbreeding depression is nearly universal in sexually reproducing organisms that are diploid or have higher ploidies. Impacts of inbreeding are generally greater in species that naturally outbreed than those that inbreed, in stressful than benign environments, and for fitness than peripheral traits. Harmful effects accumulate across the life cycle, resulting in devastating effects on total fitness in outbreeding species.Species face ubiquitous environmental change and must adapt or they will go extinct. Genetic diversity is the raw material required for evolutionary adaptation. However, loss of genetic diversity is unavoidable in small isolated populations, diminishing their capacity to evolve in response to environmental changes, and thereby increasing extinction risk.

The inherent stochastic nature of individual life histories implies that finite populations always carry a risk of going extinct, even if their long-term growth potential is positive. The consequences of demographic stochasticity are explored in this chapter, using individual-based computer simulations and simple discrete- and continuous-state stochastic models (branching processes and diffusion models). Simple examples of population viability analysis (PVA) are presented. Demographic stochasticity on the allele level is genetic drift—which leads to reduced viability and evolutionary potential of small populations via inbreeding depression. The ecological pendant of genetic drift is in the focus of the ‘ecological neutral theory’ of Hubbell. Some conceptual and methodological problems of the neutral theory in the context of species abundance distributions are discussed at the end of the chapter.

Eight types of justifications for conserving primates are discussed. The chapter begins by considering relatively anthropocentric reasons to conserve them, including their role in biomedical research, the benefits they can provide to local communities, their provision of crucial ecosystem services, the insights they provide into human evolution, and their role of advancing general biological understanding, especially of the poorly known tropics. Next, more biocentric reasons are discussed, including their potential role as surrogate species that promote the conservation of other taxa and their particular susceptibility to population decline and extinction. Ethical arguments in favour of primate conservation are also briefly covered. The chapter ends with consideration of some complications attendant to these justifications and highlights the need to be strategic when applying them.

This chapter addresses three questions for assessing carnivore conservation status and units for conservation: how to assess the risk of extinction for carnivore populations; how to identify and delineate carnivore conservation units; and how to assess, monitor, and manage carnivore conservation units. Topics discussed include population viability analysis, minimum viable population size, biological entities, evolutionary significant units, inbreeding depression, genetic drift, taxonomy, phylogeography, local adaptations, adaptive genetic variation, population-level management, geographic conservation, multiple-use landscapes, managed meta-population, and conservation planning.

Most species now have fragmented distributions, often with adverse genetic consequences. The genetic impacts of population fragmentation depend critically upon gene flow among fragments and their effective sizes. Fragmentation with cessation of gene flow is highly harmful in the long term, leading to greater inbreeding, increased loss of genetic diversity, decreased likelihood of evolutionary adaptation and elevated extinction risk, when compared to a single population of the same total size. The consequences of fragmentation with limited gene flow typically lie between those for a large population with random mating and isolated population fragments with no gene flow.

Most species now have fragmented distributions, often with adverse genetic consequences. The genetic impacts of population fragmentation depend critically upon gene flow among fragments and their effective sizes. Fragmentation with cessation of gene flow is highly harmful in the long term, leading to greater inbreeding, increased loss of genetic diversity, decreased likelihood of evolutionary adaptation and elevated extinction risk, when compared to a single population of the same total size. The consequences of fragmentation with limited gene flow typically lie between those for a large population with random mating and isolated population fragments with no gene flow.

Genetic management of fragmented populations is one of the major, largely unaddressed issues in biodiversity conservation. Many species across the planet have fragmented distributions with small isolated populations that are potentially suffering from inbreeding and loss of genetic diversity (genetic erosion), leading to elevated extinction risk. Fortunately, genetic deterioration can usually be remedied by augmenting gene flow (crossing between populations within species), yet this is rarely done, in part because of fears that crossing may be harmful (but it is possible to predict when this will occur). Benefits and risks of genetic problems are sometimes altered in species with diverse mating systems and modes of inheritance. Adequate genetic management depends on appropriate delineation of species. We address management of gene flow between previously isolated populations and genetic management under global climate change.

Genetic management of fragmented populations is one of the major, largely unaddressed issues in biodiversity conservation. Many species across the planet have fragmented distributions with small isolated populations that are potentially suffering from inbreeding and loss of genetic diversity (genetic erosion), leading to elevated extinction risk. Fortunately, genetic deterioration can usually be remedied by gene flow from another population (crossing between populations within species), yet this is rarely done, in part because of fears that crossing may be harmful (but we can predict when this will occur). We address management of gene flow between previously isolated populations and genetic management under global climate change.

Although the changes in the chemistry of seawater driven by the uptake of CO2 by the oceans have been known for decades, research addressing the effects of elevated CO2 on marine organisms and ecosystems has only started recently (see Chapter 1). The first results of deliberate experiments on organisms were published in the mid 1980s (Agegian 1985) and those on communities in 2000 (Langdon et al. 2000; Leclercq et al. 2000 ). In contrast, studies focusing on the response of terrestrial plant communities began much earlier, with the first results of free-air CO2 enrichment experiments (FACE) being published in the late 1960s (see Allen 1992 ). Not surprisingly, knowledge about the effects of elevated CO2 on the marine realm lags behind that concerning the terrestrial realm. Yet ocean acidification might have significant biological, ecological, biogeochemical, and societal implications and decision-makers need to know the extent and severity of these implications in order to decide whether they should be considered, or not, when designing future policies. The goals of this chapter are to summarize key information provided in the preceding chapters by highlighting what is known and what is unknown, identify and discuss the ecosystems that are most at risk, as well as discuss prospects and recommendation for future research. The chemical, biological, ecological, biogeochemical, and societal implications of ocean acidification have been comprehensively reviewed in the previous chapters with one minor exception. Early work has shown that ocean acidification significantly affects the propagation of sound in seawater and suggested possible consequences for marine organisms sensitive to sound (Hester et al . 2008). However, sub sequent studies have shown that the changes in the upper-ocean sound absorption coefficient at future pH levels will have no or a small impact on ocean acoustic noise (Joseph and Chiu 2010; Udovydchenkov et al . 2010). The goal of this section is to condense the current knowledge about the consequences of ocean acidification in 15 key statements. Each statement is given levels of evidence and, when possible, a level of confidence as recommended by the Intergovernmental Panel on Climate Change (IPCC) for use in its 5th Assessment Report (Mastrandrea et al. 2010).

Environmental change is a ubiquitous feature of the conditions faced by species, so they must either evolve, move to avoid threats, or perish. Species require genetic diversity to evolve to cope with environmental change through natural selection (adaptive evolution). The ability of populations to undergo adaptive evolution depends upon the strength of selection, genetic diversity, effective population size, mutation rates and number of generations. Loss of genetic diversity in small populations reduces their ability to evolve to cope with environmental change, thus increasing their extinction risk. Adaptive evolution in the short to medium term predominantly utilizes pre-existing genetic diversity, but new mutations make increasing contributions in later generations. Evolutionary potential can be estimated from the heritability of fitness in the environment of interest, or by extrapolation from genomic diversity.