Outstanding challenges in the transferability of ecological models

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.Katherine L. Yates ... Alice R. Jones ... et al

A Comparison of Climatic Niches of the Same Alpine Plant Species in the Central Caucasus and the Alps

It has been known for years that the elevational and latitudinal range limits of plant taxa are likely to be correlated (e.g. Humboldt 1817; Pellissier et al. 2013; Randin et al. 2013) and the elevation-for-latitude correspondence model has for long attracted ecologists and biogeographers. However, comparisons of the environmental niche of a common set of native plant species between geographically isolated regions but sharing similar climatic conditions have rarely been achieved (but see Randin et al. 2006 in the Alps). The large number of shared alpine plant species between the Alps and the Caucasus and the increasing availability of georeferenced occurrences and climatic data offer now opportunities to perform such across-mountain range comparisons

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline

Niche dynamics in space and time.

Niche conservatism, the tendency of a species niche to remain unchanged over time, is often assumed when discussing, explaining or predicting biogeographical patterns. Unfortunately, there has been no basis for predicting niche dynamics over relevant timescales, from tens to a few hundreds of years. The recent application of species distribution models (SDMs) and phylogenetic methods to analysis of niche characteristics has provided insight to niche dynamics. Niche shifts and conservatism have both occurred within the last 100 years, with recent speciation events, and deep within clades of species. There is increasing evidence that coordinated application of these methods can help to identify species which likely fulfill one key assumption in the predictive application of SDMs: an unchanging niche. This will improve confidence in SDM-based predictions of the impacts of climate change and species invasions on species distributions and biodiversity

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline

Very high-resolution environmental predictors in species distribution models: moving beyond topography?

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species' micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions - and therefore local management - compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management

Do floral and niche shifts favour the establishment and persistence of newly arisen polyploids? A case study in an Alpine primrose.

Background and Aims Polyploidization plays a key role in plant evolution. Despite the generally accepted \u2018minority-cytotype exclusion\u2019 theory, the specific mechanisms leading to successful establishment and persistence of new polyploids remain controversial. The majority of newly arisen polyploids are expected to quickly disappear, because they are less common, have fewer potential mates, or may not be able to successfully compete with co-occurring progenitors at lower ploidy levels. Changes in floral traits and ecological niches have been proposed as important mechanisms to overcome this initial frequency-dependent disadvantage. We aimed at determining whether dodecaploids of the heterostylous P. marginata differ from their hexaploid progenitors in P. marginata and P. allionii for selected floral traits and ecological preferences that might be involved in establishment and persistence, providing a possible explanation for the origin of polyploidized populations. Methods We quantified and compared floral morphological traits and ecological niche preferences among dodecaploids and their hexaploid progenitors in P. marginata and P. allionii, all restricted to the southwestern Alps. Key Results We detected differences in floral traits between dodecaploids and their closest relatives, but such differences seem too weak to counter the strength of minority cytotype disadvantage and are unlikely to enable the co-existence of different cytotypes. Furthermore, our results suggest no transition to selfing and preservation of full distyly in dodecaploids. Finally, dodecaploids occurred almost exclusively in environments that were predicted to be suitable also for their closest hexaploid relatives. Conclusions In light of our results, P. marginata dodecaploids have probably been able to establish and persist by occupying geographical areas not yet filled by their closest relatives without significant evolution in their climatic and pollination niches. Dispersal limitation and minority-cytotype exclusion probably maintain their current range disjunct from those of its close relatives

A quantitative assessment of rockfall influence on forest structure in the Swiss Alps

Forests below rocky cliffs often play a very important role in protecting settlements against rockfall. The structure and development of these forests is expected to be substantially affected by the disturbance of the falling rocks. Knowing about this effect is important to predict the development of protection forests and consider potential effects of the falling blocks in management strategies. The goal of this study is to quantify differences in forest structure depending on rockfall activity in four different sites in the Swiss Alps. For this, we collected data on forest structure in zones of different rockfall activity and derived rockfall impact probabilities based on rockfall simulations. We assessed whether differences in forest structure and signs of rockfall disturbance could be observed between the rockfall zones. We additionally built mixed effects models to identify the key variables explaining the forest characteristics described by diameter (DBH) and basal area (bA). The forest structure differs between the rockfall zones, however, with varying effects amongst the sites. DBH tends to decrease with increasing rockfall activity, whereas tree density appears to be little impacted by rockfall. For most sites, the number of deposited blocks and the simulated tree impact probability have a significant effect in the models along with the species, whereas for one site, hardly any effect of rockfall was found. Our results, obtained either from direct measurements or modeling, show that rockfall can locally influence the structure of forests, whereas the influence depends on the frequency and intensity of the rockfall disturbance. Impact probabilities obtained by simulations can serve as a good proxy for rockfall disturbances

Using georeferenced databases to assess the effect of climate change on alpine plant species and diversity

Past and current climate change has already induced drastic biological changes. We need projections of how future climate change will further impact biological systems. Modeling is one approach to forecast future ecological impacts, but requires data for model parameterization. As collecting new data is costly, an alternative is to use the increasingly available georeferenced species occurrence and natural history databases. Here, we illustrate the use of such databases to assess climate change impacts on mountain flora. We show that these data can be used effectively to derive dynamic impact scenarios, suggesting upward migration of many species and possible extinctions when no suitable habitat is available at higher elevations. Systematically georeferencing all existing natural history collections data in mountain regions could allow a larger assessment of climate change impact on mountain ecosystems in Europe and elsewhere

Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach

Protecting native biodiversity against alien invasive species requires powerful methods to anticipate these invasions and to protect native species assumed to be at risk. Here, we describe how species distribution models (SDMs) can be used to identify areas predicted as suitable for rare native species and also predicted as highly susceptible to invasion by alien species, at present and under future climate and land-use scenarios. To assess the condition and dynamics of such conflicts, we developed a combined predictive modelling (CPM) approach, which predicts species distributions by combining two SDMs fitted using subsets of predictors classified as acting at either regional or local scales. We illustrate the CPM approach for an alien invader and a rare species associated to similar habitats in northwest Portugal. Combined models predict a wider variety of potential species responses, providing more informative projections of species distributions and future dynamics than traditional, non-combined models. They also provide more informative insight regarding current and future rare-invasive conflict areas. For our studied species, conflict areas of highest conservation relevance are predicted to decrease over the next decade, supporting previous reports that some invasive species may contract their geographic range and impact due to climate change. More generally, our results highlight the more informative character of the combined approach to address practical issues in conservation and management programs, especially those aimed at mitigating the impact of invasive plants, land-use and climate changes in sensitive region