To what extent can ecosystem services motivate protecting biodiversity?

Society increasingly focuses on managing nature for the services it provides people rather than for the existence of particular species. How much biodiversity protection would result from this modified focus? Although biodiversity contributes to ecosystem services, the details of which species are critical, and whether they will go functionally extinct in the future, are fraught with uncertainty. Explicitly considering this uncertainty, we develop an analytical framework to determine how much biodiversity protection would arise solely from optimising net value from an ecosystem service. Using stochastic dynamic programming, we find that protecting a threshold number of species is optimal, and uncertainty surrounding how biodiversity produces services makes it optimal to protect more species than are presumed critical. We define conditions under which the economically optimal protection strategy is to protect all species, no species, and cases in between. We show how the optimal number of species to protect depends upon different relationships between species and services, including considering multiple services. Our analysis provides simple criteria to evaluate when managing for particular ecosystem services could warrant protecting all species, given uncertainty. Evaluating this criterion with empirical estimates from different ecosystems suggests that optimising some services will be more likely to protect most species than others.     

Modelling the unpredictability of future biodiversity in ecological networks

We consider the question of how accurately we can hope to predict future biodiversity in a world in which many interacting species are at risk of extinction. Simple models assuming that species’ extinctions occur independently are easily analysed, but do not account for the fact that many species depend on or otherwise interact with each other. In this paper we evaluate the effect of explicitly incorporating ecological dependencies on the predictive ability of models of extinction. In particular, we compare a model in which species’ extinction rates increase because of the extinction of their prey to a model in which the same average rate increase takes place, but in which extinctions occur independently from species to species. One might expect that including this ecological information would make the prediction of future biodiversity more accurate, but instead we find that accounting for food web dependencies reveals greater uncertainty. The expected loss of biodiversity over time is similar between the two models, but the variance in future biodiversity is considerably higher in the model that includes species interactions. This increased uncertainty is because of the non-independence of species—the tendency of two species to respond similarly to the loss of a species on which both depend. We use simulations to show that this increase in variance is robust to many variations of the model, and that its magnitude should be largest in food webs that are highly dependent on a few basal species. Our results should hold whenever ecological dependencies cause most species’ extinction risks to covary positively, and illustrate how more information does not necessarily improve our ability to predict future biodiversity loss.     

Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation

Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within‐habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual‐based species abundance data or replicated, sample‐based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within‐habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.     

Cryptic species as a window into the paradigm shift of the species concept

The species concept is the cornerstone of biodiversity science, and any paradigm shift in the delimitation of species affects many research fields. Many biologists now are embracing a new “species” paradigm as separately evolving populations using different delimitation criteria. Individual criteria can emerge during different periods of speciation; some may never evolve. As such, a paradigm shift in the species concept relates to this inherent heterogeneity in the speciation process and species category—which is fundamentally overlooked in biodiversity research. Cryptic species fall within this paradigm shift: they are continuously being reported from diverse animal phyla but are poorly considered in current tests of ecological and evolutionary theory. The aim of this review is to integrate cryptic species in biodiversity science. In the first section, we address that the absence of morphological diversification is an evolutionary phenomenon, a “process” counterpart to the long‐studied mechanisms of morphological diversification. In the next section regarding taxonomy, we show that molecular delimitation of cryptic species is heavily biased towards distance‐based methods. We also stress the importance of formally naming of cryptic species for better integration into research fields that use species as units of analysis. Finally, we show that incorporating cryptic species leads to novel insights regarding biodiversity patterns and processes, including large‐scale biodiversity assessments, geographic variation in species distribution and species coexistence. It is time for incorporating multicriteria species approaches aiming to understand speciation across space and taxa, thus allowing integration into biodiversity conservation while accommodating for species uncertainty.     

Designing nature reserves in the face of uncertainty

Conservation reserves are a fundamental tool for managing biodiversity. The so-called SLOSS debate--should we have a Single Large Or Several Small reserves - is central to conservation theory. Population dynamic models suggest that the design that minimizes the risk of extinction of a species is case-specific, with the optimal number of reserves ranging between one and very many. Uncertainty is pervasive in ecology, but, the previous analyses of the SLOSS debate have not considered how uncertainty in the model of extinction risk might influence the optimal design. Herein, we show that when uncertainty is considered, the SLOSS problem is simplified and driven more by the aspirations of the manager than the population dynamics of the species. In this case, the optimal solution is to have in the order of twenty or fewer reserves for any species. This result shows counter-intuitively that considering uncertainty actually simplifies rather than complicates decisions about designing nature reserves.     

A return-on-investment approach for prioritization of rigorous taxonomic research needed to inform responses to the biodiversity crisis

Global biodiversity loss is a profound consequence of human activity. Disturbingly, biodiversity loss is greater than realized because of the unknown number of undocumented species. Conservation fundamentally relies on taxonomic recognition of species, but only a fraction of biodiversity is described. Here, we provide a new quantitative approach for prioritizing rigorous taxonomic research for conservation. We implement this approach in a highly diverse vertebrate group—Australian lizards and snakes. Of 870 species assessed, we identified 282 (32.4%) with taxonomic uncertainty, of which 17.6% likely comprise undescribed species of conservation concern. We identify 24 species in need of immediate taxonomic attention to facilitate conservation. Using a broadly applicable return-on-investment framework, we demonstrate the importance of prioritizing the fundamental work of identifying species before they are lost.

In order to inform conservation effort, there is urgent need for rigorous taxonomic research to describe species under threat of extinction. Implementation of a new prioritization method identified 282 Australian reptile species needing taxonomic research, of which 17.6% represent undescribed species of conservation concern; this approach could be readily implemented across many faunal groups.     

Land use impacts on biodiversity in LCA: a global approach

Purpose

Land use is a main driver of global biodiversity loss and its environmental relevance is widely recognized in research on life cycle assessment (LCA). The inherent spatial heterogeneity of biodiversity and its non-uniform response to land use requires a regionalized assessment, whereas many LCA applications with globally distributed value chains require a global scale. This paper presents a first approach to quantify land use impacts on biodiversity across different world regions and highlights uncertainties and research needs.

Methods

The study is based on the United Nations Environment Programme (UNEP)/Society of Environmental Toxicology and Chemistry (SETAC) land use assessment framework and focuses on occupation impacts, quantified as a biodiversity damage potential (BDP). Species richness of different land use types was compared to a (semi-)natural regional reference situation to calculate relative changes in species richness. Data on multiple species groups were derived from a global quantitative literature review and national biodiversity monitoring data from Switzerland. Differences across land use types, biogeographic regions (i.e., biomes), species groups and data source were statistically analyzed. For a data subset from the biome (sub-)tropical moist broadleaf forest, different species-based biodiversity indicators were calculated and the results compared.

Results and discussion

An overall negative land use impact was found for all analyzed land use types, but results varied considerably. Different land use impacts across biogeographic regions and taxonomic groups explained some of the variability. The choice of indicator also strongly influenced the results. Relative species richness was less sensitive to land use than indicators that considered similarity of species of the reference and the land use situation. Possible sources of uncertainty, such as choice of indicators and taxonomic groups, land use classification and regionalization are critically discussed and further improvements are suggested. Data on land use impacts were very unevenly distributed across the globe and considerable knowledge gaps on cause–effect chains remain.

Conclusions

The presented approach allows for a first rough quantification of land use impact on biodiversity in LCA on a global scale. As biodiversity is inherently heterogeneous and data availability is limited, uncertainty of the results is considerable. The presented characterization factors for BDP can approximate land use impacts on biodiversity in LCA studies that are not intended to directly support decision-making on land management practices. For such studies, more detailed and site-dependent assessments are required. To assess overall land use impacts, transformation impacts should additionally be quantified. Therefore, more accurate and regionalized data on regeneration times of ecosystems are needed.     

Phylogenetic diversity and its conservation in the presence of phylogenetic uncertainty: a case study of cladoceran communities in urban waterbodies

The need to protect and preserve biodiversity is a pressing issue and requires that conservation projects be based on solid foundations. Knowledge of species evolutionary history can serve as a tool to help guide conservation projects on the basis of evolutionary heritage. We used communities of Cladocera (Crustacea, Branchiopoda) in urban waterbodies to identify which sites should be prioritized for phylogenetic diversity conservation. Phylogenetic trees were inferred using DNA sequences from two mitochondrial genes. Furthermore, we also evaluated the consequences of phylogenetic uncertainty for identifying sites for conservation priority. Using results from Bayesian analyses, we considered the effect of uncertainty in the phylogenetic tree on phylogenetic diversity (PD) estimation. When phylogenetic uncertainty was taken into account, the conservation value of individual sites became uncertain and several potential comparisons between sites could not be supported. Consequently prioritization of one site over the other could not be defended in biodiversity conservation projects. Our study highlights the fact that accounting for phylogenetic uncertainty can alter the relative conservation priority of sites, as assessed by their phylogenetic diversity. Therefore, variability in the phylogenetic estimates should be consistently considered and integrated into estimates of phylogenetic diversity and conservation decisions to avoid making suboptimal choices.     

The Value of Learning about Natural History in Biodiversity Markets

Markets for biodiversity have generated much controversy because of the often unstated and untested assumptions included in transactions rules. Simple trading rules are favored to reduce transaction costs, but others have argued that this leads to markets that favor development and erode biodiversity. Here, I describe how embracing complexity and uncertainty within a tradable credit system for the Red-cockaded Woodpecker (Picoides borealis) creates opportunities to achieve financial and conservation goals simultaneously. Reversing the effects of habitat fragmentation is one of the main reasons for developing markets. I include uncertainty in habitat fragmentation effects by evaluating market transactions using five alternative dispersal models that were able to approximate observed patterns of occupancy and movement. Further, because dispersal habitat is often not included in market transactions, I contrast how changes in breeding versus dispersal habitat affect credit values. I use an individually-based, spatially-explicit population model for the Red-cockaded Woodpecker (Picoides borealis) to predict spatial- and temporal- influences of landscape change on species occurrence and genetic diversity. Results indicated that the probability of no net loss of abundance and genetic diversity responded differently to the transient dynamics in breeding and dispersal habitat. Trades that do not violate the abundance cap may simultaneously violate the cap for the erosion of genetic diversity. To highlight how economic incentives may help reduce uncertainty, I demonstrate tradeoffs between the value of tradable credits and the value of information needed to predict the influence of habitat trades on population viability. For the trade with the greatest uncertainty regarding the change in habitat fragmentation, I estimate that the value of using 13-years of data to reduce uncertainty in dispersal behaviors is $6.2 million. Future guidance for biodiversity markets should at least encourage the use of spatially- and temporally-explicit techniques that include population genetic estimates and the influence of uncertainty.     

A Monte Carlo method to account for sampling error in multi-species indicators

The usefulness of biodiversity indicators strongly increases if accompanied by measures of uncertainty. In the case of indicators that combine population indices of species, however, the inclusion of the uncertainty of the species indices has shown to be hard to realize, usually due to imperfections in monitoring programmes. Missing values and time series of different lengths preclude the use of analytical approaches, whereas bootstrapping across sites requires the raw abundance data on the site level, which may not always be available. Sometimes bootstrapping across species rather than sites is opted for, but this approach ignores the uncertainty attached to species indices. We developed a method to account for sampling error of species indices in the calculation of multi-species indicators based on Monte Carlo simulation of annual species indices. The construction of confidence intervals enables various trend assessments, like testing for linear or smooth trends, testing for changes between two time points, testing the significance of a suspected change-point and testing for differences between two multi-species indicators. Here, we compare our method with conventional methods and illustrate the benefits of our approach using Dutch breeding bird indicators.     

A multigene molecular assessment of cryptic biodiversity in the iconic freshwater blackfishes (Teleostei: Percichthyidae: Gadopsis) of south‐eastern Australia

Freshwater biodiversity is under ever increasing threat from human activities, and its conservation and management require a sound knowledge of species‐level taxonomy. Cryptic biodiversity is a common feature for aquatic systems, particularly in Australia, where recent genetic assessments suggest that the actual number of freshwater fish species may be considerably higher than currently listed. The freshwater blackfishes (genus Gadopsis) are an iconic group in south‐eastern Australia and, in combination with their broad, naturally divided distribution and biological attributes that might limit dispersal, as well as ongoing taxonomic uncertainty, they comprise an ideal study group for assessing cryptic biodiversity. We used a multigene molecular assessment including both nuclear (51 allozyme loci; two S7 introns) and matrilineal markers (cytb) to assess species boundaries and broad genetic substructure within freshwater blackfishes. Range‐wide examination demonstrates the presence of at least six candidate species across two nominal taxa, Gadopsis marmoratus and Gadopsis bispinosus. Phylogeographical patterns often aligned to purported biogeographical provinces but occasionally reflected more restricted and unexpected relationships. We highlight key issues with taxonomy, conservation, and management for a species group in a highly modified region. © 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 111 , 521–540.     

An operational workflow for producing periodic estimates of species occupancy at national scales

Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined ‘regions’ (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the ‘ideal’ and ‘minimal’ criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.     

Elevational gradient analyses and the use of historical museum specimens: a cautionary tale

Aim The value of biodiversity informatics rests upon the capacity to assess data quality. Yet as these methods have developed, investigating the quality of the underlying specimen data has largely been neglected. Using an exceptionally large, densely sampled specimen data set for non‐flying small mammals of Utah, I evaluate measures of uncertainty associated with georeferenced localities and illustrate the implications of uncritical incorporation of data in the analysis of patterns of species richness and species range overlap along elevational gradients. Location Utah, USA, with emphasis on the Uinta Mountains. Methods Employing georeferenced specimen data from the Mammal Networked Information System (MaNIS), I converted estimates of areal uncertainty into elevational uncertainty using a geographic information system (GIS). Examining patterns in both areal and elevational uncertainty measures, I develop criteria for including localities in analyses along elevational gradients. Using the Uinta Mountains as a test case, I then examine patterns in species richness and species range overlap along an elevational gradient, with and without accounting for data quality. Results Using a GIS, I provide a framework for post‐hoc 3‐dimensional georeferencing and demonstrate collector‐recorded elevations as a valuable technique for detecting potential errors in georeferencing. The criteria established for evaluating data quality when analysing patterns of species richness and species range overlap in the Uinta Mountains test case reduced the number of localities by 44% and the number of associated specimens by 22%. Decreasing the sample size in this manner resulted in the subsequent removal of one species from the analysis. With and without accounting for data quality, the pattern of species richness along the elevational gradient was hump‐shaped with a peak in richness at about mid‐elevation, between 2300 and 2600 m. In contrast, the frequencies of different pair‐wise patterns of elevational range overlap among species differed significantly when data quality was and was not accounted for. Main conclusions These results indicate that failing to assess spatial error in data quality did not alter the shape of the observed pattern in species richness along the elevational gradient nor the pattern of species’ first and last elevational occurrences. However, it did yield misleading estimates of species richness and community composition within a given elevational interval, as well as patterns of elevational range overlap among species. Patterns of range overlap among species are often used to infer processes underlying species distributions, suggesting that failure to account for data quality may alter interpretations of process as well as perceived patterns of distribution. These results illustrate that evaluating the quality of the underlying specimen data is a necessary component of analyses incorporating biodiversity informatics.     

Taxonomic inflation: its influence on macroecology and conservation

Species numbers are increasing rapidly. This is due mostly to taxonomic inflation, where known subspecies are raised to species as a result in a change in species concept, rather than to new discoveries. Yet macroecologists and conservation biologists depend heavily on species lists, treating them as accurate and stable measures of biodiversity. Deciding on a standardized, universal species list might ameliorate the mismatch between taxonomy and the uses to which it is put. However, taxonomic uncertainty is ultimately due to the evolutionary nature of species, and is unlikely to be solved completely by standardization. For the moment, at least, users must acknowledge the limitations of taxonomic species and avoid unrealistic expectations of species lists.     

Identifying biases at different spatial and temporal scales of diversification: a case study in the Neotropical parrotlet genus Forpus

The temporal origins of the extraordinary biodiversity of the Neotropical region are highly debated. Recent empirical work has found support for alternative models on the tempo of speciation in Neotropical species further fuelling the debate. However, relationships within many Neotropical lineages are poorly understood, and it is unclear how this uncertainty impacts inferences on the evolution of taxa in the region. We examined the robustness of diversification patterns in the avian genus Forpus by testing whether the use of different units of biodiversity (i.e. biological species and statistically inferred species) impacted diversification rates and inferences regarding important biogeographic breaks in the genus. We found that the best‐fit model of diversification for the biological species data set was a declining rate of diversification; whereas a model of constant diversification was the best‐fit model for statistically inferred species or subspecies. Moreover, the relative importance of different landscape features in delimiting genetic structure across the landscape varied across data sets with differing units of biodiversity. Patterns based on divergence times among biological species indicated old speciation events across major geographic and river barriers. In contrast, data sets more inclusive of the diversity in Forpus illustrate the role of both old divergence across major landscape features and more recent divergences that are possibly attributed to Pleistocene climatic changes. Overall, these results indicate that conflicting models on the temporal origins of Neotropical birds may be attributable to sampling biases.     

Evaluating the effectiveness of conservation site networks under climate change: accounting for uncertainty

We forecasted potential impacts of climate change on the ability of a network of key sites for bird conservation (Important Bird Areas; IBAs) to provide suitable climate for 370 bird species of current conservation concern in two Asian biodiversity hotspots: the Eastern Himalaya and Lower Mekong. Comparable studies have largely not accounted for uncertainty, which may lead to inappropriate conclusions. We quantified the contribution of four sources of variation (choice of general circulation models, emission scenarios and species distribution modelling methods and variation in species distribution data) to uncertainty in forecasts and tested if our projections were robust to these uncertainties. Declines in the availability of suitable climate within the IBA network by 2100 were forecast as ‘extremely likely’ for 45% of species, whereas increases were projected for only 2%. Thus, we predict almost 24 times as many ‘losers’ as ‘winners’. However, for no species was suitable climate ‘extremely likely’ to be completely lost from the network. Considerable turnover (median = 43%, 95% CI = 35–69%) in species compositions of most IBAs were projected by 2100. Climatic conditions in 47% of IBAs were projected as ‘extremely likely’ to become suitable for fewer priority species. However, no IBA was forecast to become suitable for more species. Variation among General Circulation Models and Species Distribution Models contributed most to uncertainty among forecasts. This uncertainty precluded firm conclusions for 53% of species and IBAs because 95% confidence intervals included projections of no change. Considering this uncertainty, however, allows robust recommendations concerning the remaining species and IBAs. Overall, while the IBA network will continue to sustain bird conservation, climate change will modify which species each site will be suitable for. Thus, adaptive management of the network, including modified site conservation strategies and facilitating species' movement among sites, is critical to ensure effective future conservation.     

Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models

Concern over rapid global changes and the potential for interactions among multiple threats are prompting scientists to combine multiple modelling approaches to understand impacts on biodiversity. A relatively recent development is the combination of species distribution models, land‐use change predictions, and dynamic population models to predict the relative and combined impacts of climate change, land‐use change, and altered disturbance regimes on species' extinction risk. Each modelling component introduces its own source of uncertainty through different parameters and assumptions, which, when combined, can result in compounded uncertainty that can have major implications for management. Although some uncertainty analyses have been conducted separately on various model components – such as climate predictions, species distribution models, land‐use change predictions, and population models – a unified sensitivity analysis comparing various sources of uncertainty in combined modelling approaches is needed to identify the most influential and problematic assumptions. We estimated the sensitivities of long‐run population predictions to different ecological assumptions and parameter settings for a rare and endangered annual plant species (Acanthomintha ilicifolia, or San Diego thornmint). Uncertainty about habitat suitability predictions, due to the choice of species distribution model, contributed most to variation in predictions about long‐run populations.     

Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Managing biodiversity under climate change: challenges,frameworks, and tools for adaptation

The myriad challenges facing biodiversity under climate change are reflected in the challenges facing managers planning for these impacts. An ever-expanding number of recommendations and tools for climate change adaptation exist to aid managers in these efforts, yet navigating these various resources can lead to information overload and paralysis in decision-making. Here we provide a synthesis of the key considerations, approaches, and available tools for integrating climate change adaptation into management plans. We discuss principal elements in climate change adaptation—incorporating uncertainty through scenario planning and adaptive management—and review three leading frameworks for incorporating climate change adaptation into place-based biodiversity conservation planning. Finally, we identify the following key questions needed for long-term conservation planning and review the associated tools and techniques needed to address them: (1) How is the climate projected to change in my study area?; (2) How are non-climatic stressors projected to change?; (3) How vulnerable are species to climate change impacts?; (4) How are species ranges likely to respond?; and (5) How are management strategies expected to affect outcomes? In doing so, we aim to aid natural resource managers in navigating the burgeoning field of climate change adaptation planning and provide managers a roadmap for managing biodiversity under climate change.