Richard Grenyer’s research while affiliated with University of Oxford and other places

Aim Geographic structure is a fundamental organising principle in ecological and Earth sciences, and our planet is conceptually divided into distinct geographic clusters (e.g. ecoregions and biomes) demarcating unique diversity patterns. Given recent advances in technology and data availability, however, we ask whether geographically clustering diversity time‐series should be the default framework to identify meaningful patterns of diversity change. Location North America. Taxon Aves. Methods We first propose a framework that recognises patterns of diversity change based on similarities in the behaviour of diversity time‐series, independent of their specific or relative spatial locations. Specifically, we applied an artificial neural network approach, the self‐organising map (SOM), to group time‐series of over 0.9 million observations from the North American Breeding Birds Survey (BBS) data from 1973 to 2016. We then test whether time‐series identified as having similar behaviour are geographically structured. Results We find little evidence of strong geographic structure in patterns of diversity change for North American breeding birds. The majority of the recognised diversity time‐series patterns tend to be indistinguishable from being independently distributed in space. Main Conclusions Our results suggest that geographic proximity may not correspond to shared temporal trends in diversity; assuming that geographic clustering is the basis for analysis may bias diversity trend estimation. We suggest that approaches that consider variability independently of geographic structure can serve as a useful addition to existing organising rules of biodiversity time‐series.

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning–based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles—the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision making. However, despite substantial effort, numerous species remain unassessed, or have insufficient data available to be assigned a Red List threat category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here we aimed to: 1) present a machine learning based automated threat assessment method that can be used on less known species; 2) offer provisional assessments for all reptiles - the only major tetrapod group without a comprehensive Red List assessment; and 3) evaluate potential effects of human decision biases on the outcome of assessments . We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. Our models range in accuracy from 88% to 93% for classifying species as threatened/non-threatened, and from 82% to 87% for predicting specific threat categories. Unassessed and Data Deficient reptiles were more likely to be threatened than assessed species, adding to mounting evidence that they should be considered threatened by default. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap on other less known taxa.

Recent studies have drawn contrasting conclusions about the extent to which local-scale measures of biodiversity are declining and whether such patterns conflict with the global-scale declines that have attracted much attention.¹ A key source of high-quality data for such analyses comes from longitudinal biodiversity studies, which sample a given taxon repeatedly over time at a specific location.² There has been relatively little consideration of how habitat change might lead to biases in the sampling and continuity of biodiversity time series data, and the consequent potential for bias in the biodiversity trends that result. Here, based on analysis of standardized routes from the North American Breeding Bird Survey (3,014 routes sampled over 18 years),³ we demonstrate that major local habitat change is associated with an increase in the rate of survey cessations. We further show that routes that were continued despite major habitat changes show reduced diversity. By simulating potential rates of loss, we show that the underlying real trends in taxonomic, functional, and phylogenetic diversity can even reverse in sign if more than a quarter of diversity is lost from routes that ceased and are thus no longer included in surveys. Our analyses imply that biodiversity loss can be underestimated by biases introduced if continued sampling in longitudinal studies is influenced by local change. We argue that researchers and conservation practitioners should be aware of the potential for bias in such data and seek to use more robust methods to evaluate biodiversity trends and make conservation decisions.

Phylogenetic diversity measures are increasingly used in conservation planning to represent aspects of biodiversity beyond that captured by species richness. Here we develop two new metrics that combine phylogenetic diversity and the extent of human pressure across the spatial distribution of species-one metric valuing regions and another prioritising species. We evaluate these metrics for reptiles, which have been largely neglected in previous studies, and contrast these results with equivalent calculations for all terrestrial vertebrate groups. We find that regions under high human pressure coincide with the most irreplaceable areas of reptilian diversity, and more than expected by chance. The highest priority reptile species score far above the top mammal and bird species, and reptiles include a disproportionate number of species with insufficient extinction risk data. Data Deficient species are, in terms of our species-level metric, comparable to Critically Endangered species and therefore may require urgent conservation attention.

Conservation strategies based on charismatic flagship species, such as tigers, lions, and elephants, successfully attract funding from individuals and corporate donors. However, critics of this species-focused approach argue it wastes resources and often does not benefit broader biodiversity. If true, then the best way of raising conservation funds excludes the best way of spending it. Here we show that this conundrum can be resolved, and that the flagship species approach does not impede cost-effective conservation. Through a tailored prioritization approach, we identify places containing flagship species while also maximizing global biodiversity representation (based on 19,616 terrestrial and freshwater species). We then compare these results to scenarios that only maximized biodiversity representation, and demonstrate that our flagship-based approach achieves 79−89% of our objective. This provides strong evidence that prudently selected flagships can both raise funds for conservation and help target where these resources are best spent to conserve biodiversity. Conservation actions focused on flagship species are effective at raising funds and awareness. Here, McGowan et al. show that prioritizing areas for conservation based on the presence of flagship species results in the selection of areas with ~ 79-89% of the total species that would be selected by maximizing biodiversity representation only.

This article presents a quantitative description of the plant communities of the Eastern Hajar Mountains of Oman and the primary environmental variables affecting their composition and distribution. Species inventories and environmental data were collected from 110 vegetation plots covering elevation from 1000 m to the highest point in the mountains at about 2200 m. Measurements of 12 environmental variables were used in the analysis: aspect, slope, annual rainfall, soil pH, salinity, soil nutrients (N, P, K, Ca) and soil texture (clay and silt). Two Way Indicator Species Analysis (TWINSPAN) classification, Detrended Correspondence Analysis (DCA) and Canonical Correspondence Analysis (CCA) ordinations showed four well-distinguished plant communities. Although some species overlap occurred in the transition zones between the communities, these plant communities were characteristic for their zones. Annual rainfall, total nitrogen and total calcium were the most important environmental factors affecting the distribution of plant communities. The high-altitude community was always distinctly identified using all different data treatments, while the lower-altitude communities tended mostly to overlap. Any grazing effect on plant community distribution appears to be overridden by the other environmental variables.

Phylogenetic Diversity (PD) is increasingly recognised as an important measure that can provide information on evolutionary and functional aspects of biodiversity for conservation planning that are not readily captured by species diversity. Here we develop and analyse two new metrics that combine the effects of PD and human encroachment on species range size - one metric valuing regions and another enabling species prioritisation. We evaluate these metrics for reptiles, which have been largely neglected in previous studies, and contrast these results with equivalent calculations for all terrestrial vertebrate groups. We find that high human impacted areas unfortunately coincide with the most valuable areas of reptilian diversity, more than expected by chance. We also find that, under our species-level metric, the highest priority reptile species score far above the top mammal and bird species, and they include a disproportionate number of species with insufficient information on potential threats. Such Data Deficient species are, in terms of our metric, comparable to Critically Endangered species and may require urgent conservation attention.

It is often claimed that conserving evolutionary history is more efficient than species‐based approaches for capturing the attributes of biodiversity that benefit people. This claim underpins academic analyses and recommendations about the distribution and prioritization of species and areas for conservation, but evolutionary history is rarely considered in practical conservation activities. One impediment to implementation is that arguments related to the human‐centric benefits of evolutionary history are often vague and the underlying mechanisms poorly explored. Herein we identify the arguments linking the prioritization of evolutionary history with benefits to people, and for each we explicate the purported mechanism, and evaluate its theoretical and empirical support. We find that, even after 25 years of academic research, the strength of evidence linking evolutionary history to human benefits is still fragile. Most – but not all – arguments rely on the assumption that evolutionary history is a useful surrogate for phenotypic diversity. This surrogacy relationship in turn underlies additional arguments, particularly that, by capturing more phenotypic diversity, evolutionary history will preserve greater ecosystem functioning, capture more of the natural variety that humans prefer, and allow the maintenance of future benefits to humans. A surrogate relationship between evolutionary history and phenotypic diversity appears reasonable given theoretical and empirical results, but the strength of this relationship varies greatly. To the extent that evolutionary history captures unmeasured phenotypic diversity, maximizing the representation of evolutionary history should capture variation in species characteristics that are otherwise unknown, supporting some of the existing arguments. However, there is great variation in the strength and availability of evidence for benefits associated with protecting phenotypic diversity. There are many studies finding positive biodiversity–ecosystem functioning relationships, but little work exists on the maintenance of future benefits or the degree to which humans prefer sets of species with high phenotypic diversity or evolutionary history. Although several arguments link the protection of evolutionary history directly with the reduction of extinction rates, and with the production of relatively greater future biodiversity via increased adaptation or diversification, there are few direct tests. Several of these putative benefits have mismatches between the relevant spatial scales for conservation actions and the spatial scales at which benefits to humans are realized. It will be important for future work to fill in some of these gaps through direct tests of the arguments we define here.

Additional information on methods to establish thresholds for seasonality, assign latitude to Wikipedia languages, and compare eBird frequency and Wikipedia pageview data. (DOCX)