Multi-criterion trade-offs and synergies for spatial conservation planning

1. Nature conservation policies need to deliver on multiple criteria, including genetic diversity, population viability and species richness as well as ecosystem services. The challenge of integrating these may be addressed by simulation modelling. 2. We used four models (MetaConnect, SPOMSIM, a community model and InVEST) to assess a variety of spatial habitat patterns with two levels of total habitat cover and realised at two spatial scales, exploring which landscape structures performed best according to five different criteria assessed for four functional types of organisms (approximately representing trees, butterflies, small mammals and birds). 3. The results display both synergies and trade-offs: population size and pollination services generally benefitted more from fragmentation than did genetic heterozygosity, and species richness more than allelic richness, although the latter two varied considerably among the functional types. 4. No single landscape performed best across all criteria, but averaging over criteria and functional types, overall performance improved with greater levels of habitat cover and intermediate fragmentation (or less fragmentation in cases with lower habitat cover). 5. Synthesis and applications. Different conservation objectives must be traded off, and considering only a single taxon or criterion may result in sub-optimal choices when planning reserve networks. Nevertheless, heterogeneous spatial patterns of habitat can provide reasonable compromises for multiple criteria

Downscale: An R package for downscaling species occupancy from coarse-grain data to predict occupancy at fine-grain sizes

The geographical area occupied by a species is a valuable measure for assessing its conservation status. Coarse-grained occupancy maps are available for many taxa, e.g., as atlases, but often at spatial resolutions too coarse for conservation use. However, mapping occupancy at fine spatial resolution across the entire extent of the species’ distribution is often prohibitively expensive for the majority of species. Occupancy downscaling is a technique to estimate finer scale occupancy from coarse scale maps, by using the occupancy-area relationship (OAR) which reflects how the proportion of area occupied increases with spatial grain size. Models that describe the OAR are fitted to observed occupancies at the available coarse-grain sizes and then extrapolated to predict occupancy at the finer grain sizes required. The downscale package in the R programming environment provides users with easy-to-use functions for downscaling occupancy with ten published models. First, upgrain calculates occupancy for multiple grain sizes larger than the input data. Normal methods for aggregating raster data increase the extent of the focal area as grain size increases which is undesirable, so the function fixes the extent for all grain sizes, assigning unsampled cells as absences. Four suggested methods are provided to enable this and upgrain.threshold provides diagnostic plots that allow the user to explore the inherent trade-off between making assumptions about unsampled locations and discarding information from sampled locations. downscale fits nine possible models to the data generated from upgrain. hui.downscale fits the special case of the Hui model. predict and plot extrapolate the fitted models to predict and plot occupancy at finer grain sizes. Finally, ensemble.downscale simultaneously fits two or more of the downscaling models and calculates mean predicted occupancy across all selected models. Here we describe the package and apply the functions to atlas data of a hypothetical UK species

SDM profiling: A tool for assessing the information-content of sampled and unsampled locations for species distribution models

Species distribution models (SDMs) are key tools in biodiversity and conservation, but assessing their reliability in unsampled locations is difficult, especially where there are sampling biases. We present a spatially-explicit sensitivity analysis for SDMs – SDM profiling – which assesses the leverage that unsampled locations have on the overall model by exploring the interaction between the effect on the variable response curves and the prevalence of the affected environmental conditions. The method adds a ‘pseudo-presence’ and ‘pseudo-absence’ to unsampled locations, re-running the SDM for each, and measuring the difference between the probability surfaces of the original and new SDMs. When the standardised difference values are plotted against each other (a ‘profile plot’), each point's location can be summarized by four leverage measures, calculated as the distances to each corner. We explore several applications: visualization of model certainty; identification of optimal new sampling locations and redundant existing locations; and flagging potentially erroneous occurrence records

Accounting for biotic interactions through alpha-diversity constraints in stacked species distribution models

1. Species Distribution Models (SDM) are widely used to predict occupancy patterns at fine resolution over wide extents. However, SDMs generally ignore the effect of biotic interactions and tend to overpredict the number of species that can coexist at a given location and time (hereafter, the alpha-capacity). We developed an extension of SDMs that integrates species-level and community-level modelling to account for the above drivers. 2. The alpha-adjusted SDM takes the Probabilities of Occurrence (PoO) for all species of a community and the site’s alpha-capacity and adjusts the PoO, such that: a. their sum will equal the alpha-capacity as predicted by probability theory; and b. the adjusted PoO are dependent upon the relative suitability of each species for that site. The new method was tested using community data comprising 87 freshwater invertebrate species in an LTER watershed in Germany. We explored the ability of the method to predict alpha and beta-diversity patterns. We further focused on the effect on model performance at the species-level of the error associated with modelling alpha-capacity, of differences in gamma diversity (the size of the community) and of the type of community (random or guild-based). 3. The models that predicted alpha-capacity contained considerable error, and thus adjusting the PoO according to the modelled alpha-capacity resulted with decreased performance at the species level. However, when using the observed alpha-capacity to mimic a good alpha-capacity model, the alpha-adjusted SDMs usually resulted in increased performance. We further found that the alpha-adjusted SDM was better than the original SDM at predicting beta-diversity patterns, especially when using similarity indices that are sensitive to double absences. 4. Using the alpha-adjusted SDM approach may increase the predictive performance at the species and community levels if alpha-capacity can be assessed or modelled with sufficient accuracy, especially in relatively small communities of closely interacting species. With better models to predict alpha-capacity being developed, alpha-adjusted SDM has considerable potential to provide more realistic predictions of species-distribution patterns

The effect of sampling effort and methodology on range size estimates of poorly-recorded species for IUCN Red List assessments

Geographic range size is the most commonly implemented criterion of species’ extinction risk used in IUCN Red List assessments, especially for poorly-recorded species. IUCN applies two contrasting range size measures to capture different facets of a species’ distribution: Extent of Occurrence (EOO; Criterion B1) is the area bounding all known occurrences and is a proxy for the spatial autocorrelation of risk, while the Area of Occupancy (AOO; Criterion B2) is the area occupied within this boundary and is related to population size at finer grains. Various methods have been proposed to measure both EOO and AOO. We evaluate the impact of applying four methods for each of Criterion B1 and of B2, as well as key parameter choices, on the Red List status of 227 poorly-recorded neotropical pteridophyte species. Between 2 and 100% of species would be considered threatened depending on methodology. The minimum convex polygon method of estimating EOO was relatively robust to sampling effort for all but the least-recorded species. The IUCN-recommended method for estimating AOO of summing occupied 2 × 2 km grid cells was very strongly correlated with the total number of records. It is likely that only a small fraction of species can be adequately assessed using this method, and we recommend caution applying the method to poorly-recorded species in particular, where models predicting occupancy in unsampled areas (e.g. species distribution models) may provide more accurate assessments. It is vital that methodological information is retained with assessments, and comparisons should only be made between assessments utilising equivalent methods

Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre‐defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2–3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into “black-box” based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps

Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre‐defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2–3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into “black-box” based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps

Cancer pharmacogenetics

The large number of active combination chemotherapy regimens for most cancers has led to the need for better information to guide the \u27standard\u27 treatment for each patient. In an attempt to individualise therapy, pharmacogenetics and pharmacogenomics (a polygenic approach to pharmacogenetic studies) encompass the search for answers to the hereditary basis for interindividual differences in drug response. This review will focus on the results of studies assessing the effects of polymorphisms in drug-metabolising enzymes and drug targets on the toxicity and response to commonly used chemotherapy drugs. In addition, the need for polygenic pharmacogenomic strategies to identify patients at risk for adverse drug reactions will be highlighted

Metabolomics demonstrates divergent responses of two Eucalyptus species to water stress

Past studies of water stress in Eucalyptus spp. generally highlighted the role of fewer than five “important” metabolites, whereas recent metabolomic studies on other genera have shown tens of compounds are affected. There are currently no metabolite profiling data for responses of stress-tolerant species to water stress. We used GC–MS metabolite profiling to examine the response of leaf metabolites to a long (2 month) and severe (Ψpredawn < −2 MPa) water stress in two species of the perennial tree genus Eucalyptus (the mesic Eucalyptus pauciflora and the semi-arid Eucalyptus dumosa). Polar metabolites in leaves were analysed by GC–MS and inorganic ions by capillary electrophoresis. Pressure–volume curves and metabolite measurements showed that water stress led to more negative osmotic potential and increased total osmotically active solutes in leaves of both species. Water stress affected around 30–40% of measured metabolites in E. dumosa and 10–15% in E. pauciflora. There were many metabolites that were affected in E. dumosa but not E. pauciflora, and some that had opposite responses in the two species. For example, in E. dumosa there were increases in five acyclic sugar alcohols and four low-abundance carbohydrates that were unaffected by water stress in E. pauciflora. Re-watering increased osmotic potential and decreased total osmotically active solutes in E. pauciflora, whereas in E. dumosa re-watering led to further decreases in osmotic potential and increases in total osmotically active solutes. This experiment has added several extra dimensions to previous targeted analyses of water stress responses in Eucalyptus, and highlights that even species that are closely related (e.g. congeners) may respond differently to water stress and re-waterin

The factor structure of the Forms of Self-Criticising/Attacking & Self-Reassuring Scale in thirteen distinct populations

There is considerable evidence that self-criticism plays a major role in the vulnerability to and recovery from psychopathology. Methods to measure this process, and its change over time, are therefore important for research in psychopathology and well-being. This study examined the factor structure of a widely used measure, the Forms of Self-Criticising/Attacking & Self-Reassuring Scale in thirteen nonclinical samples (N = 7510) from twelve different countries: Australia (N = 319), Canada (N = 383), Switzerland (N = 230), Israel (N = 476), Italy (N = 389), Japan (N = 264), the Netherlands (N = 360), Portugal (N = 764), Slovakia (N = 1326), Taiwan (N = 417), the United Kingdom 1 (N = 1570), the United Kingdom 2 (N = 883), and USA (N = 331). This study used more advanced analyses than prior reports: a bifactor item-response theory model, a two-tier item-response theory model, and a non-parametric item-response theory (Mokken) scale analysis. Although the original three-factor solution for the FSCRS (distinguishing between Inadequate-Self, Hated-Self, and Reassured-Self) had an acceptable fit, two-tier models, with two general factors (Self-criticism and Self-reassurance) demonstrated the best fit across all samples. This study provides preliminary evidence suggesting that this two-factor structure can be used in a range of nonclinical contexts across countries and cultures. Inadequate-Self and Hated-Self might not by distinct factors in nonclinical samples. Future work may benefit from distinguishing between self-correction versus shame-based self-criticism.Peer reviewe