Incorporating knowledge uncertainty into species distribution modelling

Monitoring progress towards global goals and biodiversity targets require reliable descriptions of species distributions over time and space. Current gaps in accessible information on species distributions urges the need for integrating all available data and knowledge sources, and intensifying cooperations to more effectively support global environmental governance. For many areas and species groups, experts can constitute a valuable source of information to fill the gaps by offering their knowledge on species-environment interactions. However, expert knowledge is always subject to uncertainty, and incorporating that into species distribution mapping poses a challenge. We propose the use of the dempster–shafer theory of evidence (DST) as a novel approach in this field to extract expert knowledge, to incorporate the associated uncertainty into the procedure, and to produce reliable species distribution maps. We applied DST to model the distribution of two species of eagle in Spain. We invited experts to fill in an online questionnaire and express their beliefs on the habitat of the species by assigning probability values for given environmental variables, along with their confidence in expressing the beliefs. We then calculated evidential functions, and combined them using Dempster’s rules of combination to map the species distribution based on the experts’ knowledge. We evaluated the performances of our proposed approach using the atlas of Spanish breeding birds as an independent test dataset, and further compared the results with the outcome of an ensemble of conventional SDMs. Purely based on expert knowledge, the DST approach yielded similar results as the data driven SDMs ensemble. Our proposed approach offers a strong and practical alternative for species distribution modelling when species occurrence data are not accessible, or reliable, or both. The particular strengths of the proposed approach are that it explicitly accounts for and aggregates knowledge uncertainty, and it capitalizes on the range of data sources usually considered by an expert

Finessing atlas data for species distribution models

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli’s eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data. A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 · 1 km) than the original atlas data (10 · 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli’s eagle at a local scale from a national level occurrence data set; however, the usefulness of this approach may be limited to well-known species

Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales

Protected and conserved areas are a key area-based strategy to address the biodiversity and climate change crises. Indicators are fundamental to understanding performance over time. The Digital Observatory for Protected Areas (DOPA) was born in 2013 as a set of open-access web services and applications to be used to assess, monitor, and report on protected and conserved areas. For over a decade, it has delivered over 50 indicators to support policy processes, national and regional governments, and practitioners. DOPA has also developed a versatile and efficient back-end approach that is widely applicable in other area-based conservation contexts. Here, we describe the methods and workflows behind DOPA’s back end and provide examples of policy relevant questions it can answer. We discuss the key advantages and limitations of this systematic and replicable approach and explore the use of this back-end architecture to inform progress in area-based conservation targets for the following decades. This approach, embedded in multiple services provided by the Knowledge Centre for Biodiversity of the European Commission (KCBD), can also support the implementation and monitoring of area-based targets of the Kunming Montreal Global Biodiversity Framework at international, regional, and national levels

Beyond Academia: A case for reviews of gray literature for science-policy processes and applied research

Gray literature is increasingly considered to complement evidence and knowledge from peer-reviewed literature for science-policy processes and applied research. On the one hand, science-policy assessments need to both consider a diversity of worldviews, knowledge types and values from a variety of sectors and actor groups, and synthesize policy-relevant findings that are salient, legitimate and credible. On the other hand, practitioners and scholars conducting applied research, especially in environmental and health-related fields, are affected by the time lag and documented biases of academic publication processes. While gray literature holds diverse perspectives that need to be integrated in science-policy processes as well as practical evidence unfiltered by commercial publication processes, its heterogeneity has made it challenging to access through conventional means for a literature review. This paper details one endeavor within the Values Assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) to review gray literature using Google’s Programmable Search Engine. In the absence of a standardized approach, we build on a limited experiential knowledge base for reviewing gray literature and report on the potential applicability of our strategy for future reviews. Our results contrast the findings of our parallel review of academic literature, underlining the importance of mobilizing different knowledge bases in science-policy assessments, evidence-based practices, and applied research

Holocene wildfire regimes in forested peatlands in western Siberia: interaction between peatland moisture conditions and the composition of plant functional types

Wildfire is the most common disturbance type inboreal forests and can trigger significant changes in forestcomposition. Waterlogging in peatlands determines the degree of tree cover and the depth of the burnt horizon associated with wildfires. However, interactions between peatland moisture, vegetation composition and flammability, andfire regime in forest and forested peatland in Eurasia remain largely unexplored, despite their huge extent in borealregions. To address this knowledge gap, we reconstructedthe Holocene fire regime, vegetation composition, and peatland hydrology at two sites located in predominantly lighttaiga (Pinus sylvestris Betula) with interspersed dark taigacommunities (Pinus sibirica, Picea obovata, Abies sibirica)in western Siberia in the Tomsk Oblast, Russia. We foundmarked shifts in past water levels over the Holocene. Theprobability of fire occurrence and the intensification of firefrequency and severity increased at times of low water table(drier conditions), enhanced fuel dryness, and an intermediate dark-to-light taiga ratio. High water level, and thus wetpeat surface conditions, prevented fires from spreading onpeatland and surrounding forests. Deciduous trees (i.e. Betula) and Sphagnum were more abundant under wetter peatland conditions, and conifers and denser forests were moreprevalent under drier peatland conditions. On a Holocenescale, severe fires were recorded between 7.5 and 4.5 ka withan increased proportion of dark taiga and fire avoiders (Pinussibirica at Rybnaya and Abies sibirica at Ulukh–Chayakh)in a predominantly light taiga and fire-resister communitycharacterised by Pinus sylvestris and lower local water level.Severe fires also occurred over the last 1.5 kyr and were associated with a declining abundance of dark taiga and fireavoiders, an expansion of fire invaders (Betula), and fluctuating water tables. These findings suggest that frequent,high-severity fires can lead to compositional and structuralchanges in forests when trees fail to reach reproductive maturity between fire events or where extensive forest gaps limitseed dispersal. This study also shows prolonged periods ofsynchronous fire activity across the sites, particularly duringthe early to mid-Holocene, suggesting a regional imprint ofcentennial- to millennial-scale Holocene climate variabilityon wildfire activity. Humans may have affected vegetationand fire from the Neolithic; however, increasing human presence in the region, particularly at the Ulukh–Chayakh Mireover the last 4 centuries, drastically enhanced ignitions compared to natural background levels. Frequent warm and dryspells predicted by climate change scenarios for Siberia inthe future will enhance peatland drying and may convey acompetitive advantage to conifer taxa. However, dry conditions will probably exacerbate the frequency and severityof wildfire, disrupt conifers’ successional pathway, and accelerate shifts towards deciduous broadleaf tree cover. Furthermore, climate–disturbance–fire feedbacks will acceleratechanges in the carbon balance of boreal peatlands and affecttheir overall future resilience to climate chang

Beyond Academia: a case for reviews of gray literature for science-policy processes and applied research

Gray literature is increasingly considered to complement evidence and knowledge from peer-reviewed literature for science-policy processes and applied research. On the one hand, science-policy assessments need to consider a diversity of worldviews, knowledge types and values from a variety of sectors and actor groups, and synthesize policy-relevant findings that are salient, legitimate and credible. On the other hand, practitioners and scholars conducting applied research are affected by the time lag and biases of academic publication processes. Gray literature holds diverse perspectives informative for science-policy processes as well as practical evidence unfiltered by commercial publication processes. However, its heterogeneity has made it challenging to access through conventional means for a literature review. This paper details one endeavor within the Values Assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) to review gray literature using Google’s Programmable Search Engine. In the absence of a standardized approach, we build on the limited experiential knowledge base for reviewing gray literature and report on the potential applicability of our strategy for future reviews. Gray literature review results contrast findings of our parallel review of academic literature, underlining the importance of mobilizing different knowledge bases in science-policy assessments, evidence-based practices, and applied research

Utilizing multi-objective decision support tools for protected area selection

Establishing and maintaining protected areas (PAs) is a key action in delivering post-2020 biodiversity targets. PAs often need to meet multiple objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation, but available land and conservation funding is limited. Therefore, optimizing resources by selecting the most beneficial PAs is vital. Here, we advocate for a flexible and transparent approach to selecting PAs based on multiple objectives, and illustrate this with a decision support tool on a global scale. The tool allows weighting and prioritization of different conservation objectives according to user-specified preferences as well as real-time comparison of the outcome. Applying the tool across 1,346 terrestrial PAs, we demonstrate that decision makers frequently face trade-offs among conflicting objectives, e.g., between species protection and ecosystem integrity. Nevertheless, we show that transparent decision support tools can reveal synergies and trade-offs associated with PA selection, thereby helping to illuminate and resolve land-use conflicts embedded in divergent societal and political demands and values.publishedVersio

Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes

Abstract Climate change and other anthropogenic drivers of biodiversity change are unequally distributed across the world. Overlap in the distributions of different drivers have important implications for biodiversity change attribution and the potential for interactive effects. However, the spatial relationships among different drivers and whether they differ between the terrestrial and marine realm has yet to be examined. We compiled global gridded datasets on climate change, land-use, resource exploitation, pollution, alien species potential and human population density. We used multivariate statistics to examine the spatial relationships among the drivers and to characterize the typical combinations of drivers experienced by different regions of the world. We found stronger positive correlations among drivers in the terrestrial than in the marine realm, leading to areas with high intensities of multiple drivers on land. Climate change tended to be negatively correlated with other drivers in the terrestrial realm (e.g. in the tundra and boreal forest with high climate change but low human use and pollution), whereas the opposite was true in the marine realm (e.g. in the Indo-Pacific with high climate change and high fishing). We show that different regions of the world can be defined by Anthropogenic Threat Complexes (ATCs), distinguished by different sets of drivers with varying intensities. We identify 11 ATCs that can be used to test hypotheses about patterns of biodiversity and ecosystem change, especially about the joint effects of multiple drivers. Our global analysis highlights the broad conservation priorities needed to mitigate the impacts of anthropogenic change, with different priorities emerging on land and in the ocean, and in different parts of the world.Peer reviewe

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016