Comparison of climate envelope models developed using expert-selected variables versus statistical selection

Diversity of European habitat types is correlated with geography more than climate and human pressure

Habitat richness, that is, the diversity of ecosystem types, is a complex, spatially explicit aspect of biodiversity, which is affected by bioclimatic, geographic, and anthropogenic variables. The distribution of habitat types is a key component for understanding broad-scale biodiversity and for developing conservation strategies. We used data on the distribution of European Union (EU) habitats to answer the following questions: (i) how do bioclimatic, geographic, and anthropogenic variables affect habitat richness? (ii) Which of those factors is the most important? (iii) How do interactions among these variables influence habitat richness and which combinations produce the strongest interactions? The distribution maps of 222 terrestrial habitat types as defined by the Natura 2000 network were used to calculate habitat richness for the 10 km × 10 km EU grid map. We then investigated how environmental variables affect habitat richness, using generalized linear models, generalized additive models, and boosted regression trees. The main factors associated with habitat richness were geographic variables, with negative relationships observed for both latitude and longitude, and a positive relationship for terrain ruggedness. Bioclimatic variables played a secondary role, with habitat richness increasing slightly with annual mean temperature and overall annual precipitation. We also found an interaction between anthropogenic variables, with the combination of increased landscape fragmentation and increased population density strongly decreasing habitat richness. This is the first attempt to disentangle spatial patterns of habitat richness at the continental scale, as a key tool for protecting biodiversity. The number of European habitats is related to geography more than climate and human pressure, reflecting a major component of biogeographical patterns similar to the drivers observed at the species level. The interaction between anthropogenic variables highlights the need for coordinated, continental-scale management plans for biodiversity conservation.

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of “decomposition and ensemble”, a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction.

Diversity and Distribution of the Dominant Ant Genus Anonychomyrma (Hymenoptera: Formicidae) in the Australian Wet Tropics

Anonychomyrma is a dolichoderine ant genus of cool-temperate Gondwanan origin with a current distribution that extends from the north of southern Australia into the Australasian tropics. Despite its abundance and ecological dominance, little is known of its species diversity and distribution throughout its range. Here, we describe the diversity and distribution of Anonychomyrma in the Australian Wet Tropics bioregion, where only two of the many putative species are described. We hypothesise that the genus in tropical Australia retains a preference for cool wet rainforests reminiscent of the Gondwanan forests that once dominated Australia, but now only exist in upland habitats of the Wet Tropics. Our study was based on extensive recent surveys across five subregions and along elevation and vertical (arboreal) gradients. We integrated genetic (CO1) data with morphology to recognise 22 species among our samples, 20 of which appeared to be undescribed. As predicted, diversity and endemism were concentrated in uplands above 900 m a.s.l. Distribution modelling of the nine commonest species identified maximum temperature of the warmest month, rainfall seasonality, and rainfall of the wettest month as correlates of distributional patterns across subregions. Our study supported the notion that Anonychomyrma radiated from a southern temperate origin into the tropical zone, with a preference for areas of montane rainforest that were stably cool and wet over the late quaternary.

Climate change has different predicted effects on the range shifts of two hybridizing ambush bug (Phymata, Family Reduviidae, Order Hemiptera) species

AIM

A universal attribute of species is that their distributions are limited by numerous factors that may be difficult to quantify. Furthermore, climate change-induced range shifts have been reported in many taxa, and understanding the implications of these shifts remains a priority and a challenge. Here, we use Maxent to predict current suitable habitat and to project future distributions of two closely related, parapatrically distributed Phymata species in light of anthropogenic climate change.

LOCATION

North America.

TAXON

Phymata americana Melin 1930 and Phymata pennsylvanica Handlirsch 1897, Family: Reduviidae, Order: Hemiptera.

METHODS

We used the maximum entropy modeling software Maxent to identify environmental variables maintaining the distribution of two Phymata species, Phymata americana and Phymata pennsylvanica. Species occurrence data were collected from museum databases, and environmental data were collected from WorldClim. Once we gathered distribution maps for both species, we created binary suitability maps of current distributions. To predict future distributions in 2050 and 2070, the same environmental variables were used, this time under four different representative concentration pathways: RCP2.6, RCP4.5, RCP6.0, and RCP8.5; as well, binary suitability maps of future distributions were also created. To visualize potential future hybridization, the degree of overlap between the two Phymata species was calculated.

RESULTS

The strongest predictor to P. americana ranges was the mean temperature of the warmest quarter, while precipitation of the driest month and mean temperature of the warmest quarter were strong predictors of P. pennsylvanica ranges. Future ranges for P. americana are predicted to increase northwestward at higher CO2 concentrations. Suitable ranges for P. pennsylvanica are predicted to decrease with slight fluctuations around range edges. There is an increase in overlapping ranges of the two species in all future predictions.

MAIN CONCLUSIONS

These evidences for different environmental requirements for P. americana and P. pennsylvanica account for their distinct ranges. Because these species are ecologically similar and can hybridize, climate change has potentially important eco-evolutionary ramifications. Overall, our results are consistent with effects of climate change that are highly variable across species, geographic regions, and over time.

Phylogenetics and population structure of the steppe species Hycleus polymorphus (Coleoptera: Meloidae: Mylabrini) reveal multiple refugia in Mediterranean mountain ranges

Many continental species distributed in the Eurasian steppe occur as relict populations in the mountains of Western Europe. Their biogeographical responses to Quaternary climate changes have been poorly studied; however, they could have responded as cold-adapted species. We investigated the biogeographic history of a steppe beetle, Hycleus polymorphus, using mitochondrial and nuclear DNA sequences (COI, CAD, ITS2), and species distribution modelling (SDM) under present and past bioclimatic envelopes. We first performed a phylogenetic assessment to define species boundaries within the H. polymorphus species group. Specimens previously treated as Hycleus humerosus on morphological grounds are assigned to H. polymorphus, and those identified as Hycleus zebraeus assigned to Hycleus atratus. ITS2 data analyses revealed a strong phylogeographical structure of H. polymorphus populations, with four haplogroups corresponding to the (i) Italian Alps, (ii) French Alps and Pyrenees, (iii) South Balkan and Pontic mountains, and (iv) North Dinaric Alps. Based on these analyses and the SDM, we propose that during a glacial period, following the spread of steppic habitat, H. polymorphus underwent a range expansion from Asia to South-West Europe. Within the Mediterranean area, during the last interglacial the climatic suitability for the species was limited to mountains that acted as refugia and prompted allopatric divergence into four main lineages.

Expert-Informed Habitat Suitability Analysis for At-Risk Species Assessment and Conservation Planning

The U.S. Fish and Wildlife Service (USFWS) is responsible for reviewing the biological status of hundreds of species to determine federal status designations under the Endangered Species Act. The longleaf pine Pinus palustris ecological system supports many priority at-risk species designated for review, including five species of herpetofauna: gopher tortoise Gopherus polyphemus, southern hognose snake Heterodon simus, Florida pine snake Pituophis melanoleucus mugitus, gopher frog Lithobates (Rana) capito, and striped newt Notophthalmus perstriatus. To inform status decisions and conservation planning, we developed habitat suitability models to 1) identify habitat features that best predict species presence and 2) estimate the amount and distribution of suitable habitat across each species' range under current conditions. We incorporated expert judgment from federal, state, and other partners to capture variation in ecological settings across species' ranges, prioritize predictor variables to test in models, mitigate data limitations by informing the selection of pseudoabsence points, qualitatively evaluate model estimates, and improve the likelihood that experts will trust and use model predictions for conservation. Soil characteristics, land cover, and fire interval strongly influenced habitat suitability for all species. Suitable habitat was distributed on known species strongholds, as well as private lands without known species records. Between 4.7% (gopher frog) and 14.6% (gopher tortoise) of the area in a species' range was classified as suitable habitat, and between 28.1% (southern hognose snake) and 47.5% (gopher frog) of suitable habitat was located in patches larger than 1 km2 (100 ha) on publicly owned lands. By overlaying predictions for each species, we identified areas of suitable habitat for multiple species on protected and unprotected lands. These results have direct applications to management and conservation planning: partners can tailor site-level management based on attributes associated with high habitat suitability for species of concern; allocate survey effort in areas with suitable habitat but no known species records; and identify priority areas for management, land acquisitions, or other strategies based on the distribution of species records, suitable habitat, and land protection status. These results can aid regional partners in implementing effective conservation strategies and inform status designation decisions of the USFWS.

Exploring the Interplay Between Local and Regional Drivers of Distribution of a Subterranean Organism

Caves are excellent model systems to study the effects of abiotic factors on species distributions due to their selective conditions. Different ecological factors have been shown to affect species distribution depending on the scale of analysis, whether regional or local. The interplay between local and regional factors in explaining the spatial distribution of cave-dwelling organisms is poorly understood. Using the troglophilic subterranean spider Artema nephilit (Araneae: Pholcidae) as a model organism, we investigated whether similar environmental predictors drive the species distribution at these two spatial scales. At the local scale, we monitored the abundance of the spiders and measured relevant environmental features in 33 caves along the Jordan Rift Valley. We then extended the analysis to a regional scale, investigating the drivers of the distribution using species distribution models. We found that similar ecological factors determined the distribution at both local and regional scales for A. nephilit. At a local scale, the species was found to preferentially occupy the outermost, illuminated, and warmer sectors of caves. Similarly, mean annual temperature, annual temperature range, and solar radiation were the most important drivers of its regional distribution. By investigating these two spatial scales simultaneously, we showed that it was possible to achieve an in-depth understanding of the environmental conditions that governs subterranean species distribution.

Analysis of intraspecific seed diversity in Astragalus aquilanus (Fabaceae), an endemic species of Central Apennine

This work aims to study seeds of the endemic species Astragalus aquilanus from four different populations of central Italy. We investigated seed morpho-colorimetric features (shape and size) and chemical differences (through infrared spectroscopy) among populations and between dark and light seeds. Seed morpho-colorimetric quantitative variables, describing shape, size and colour traits, were measured using image analysis techniques. Fourier transform infrared (FT-IR) spectroscopy was used to attempt seed chemical characterisation. The measured data were analysed by step-wise linear discriminant analysis (LDA). Moreover, we analysed the correlation between the four most important traits and six climatic variables extracted from WorldClim 2.0. The LDA on seeds traits shows clear differentiation of the four populations, which can be attributed to different chemical composition, as confirmed by Wilk's lambda test (P < 0.001). A strong correlation between morphometric traits and temperature (annual mean temperature, mean temperature of the warmest and coolest quarter), colorimetric traits and precipitation (annual precipitation, precipitation of wettest and driest quarter) was observed. The characterisation of A. aquilanus seeds shows large intraspecific plasticity both in morpho-colorimetric and chemical composition. These results confirm the strong relationship between the type of seed produced and the climatic variables.

Climate Change, Bioclimatic Models and the Risk to Lichen Diversity

This paper provides an overview of bioclimatic models applied to lichen species, supporting their potential use in this context as indicators of climate change risk. First, it provides a brief summary of climate change risk, pointing to the relevance of lichens as a topic area. Second, it reviews the past use of lichen bioclimatic models, applied for a range of purposes with respect to baseline climate, and the application of data sources, statistical methods, model extents and resolution and choice of predictor variables. Third, it explores additional challenges to the use of lichen bioclimatic models, including: 1. The assumption of climatically controlled lichen distributions, 2. The projection to climate change scenarios, and 3. The issue of nonanalogue climates and model transferability. Fourth, the paper provides a reminder that bioclimatic models estimate change in the extent or range of a species suitable climate space, and that an outcome will be determined by vulnerability responses, including potential for migration, adaptation, and acclimation, within the context of landscape habitat quality. The degree of exposure to climate change, estimated using bioclimatic models, can help to inform an understanding of whether vulnerability responses are sufficient for species resilience. Fifth, the paper draws conclusions based on its overview, highlighting the relevance of bioclimatic models to conservation, support received from observational data, and pointing the way towards mechanistic approaches that align with field-scale climate change experiments.

Biodiversity monitoring, earth observations and the ecology of scale

Human activity and land-use change are dramatically altering the sizes, geographical distributions and functioning of biological populations worldwide, with tremendous consequences for human well-being. Yet our ability to measure, monitor and forecast biodiversity change - crucial to addressing it - remains limited. Biodiversity monitoring systems are being developed to improve this capacity by deriving metrics of change from an array of in situ data (e.g. field plots or species occurrence records) and Earth observations (EO; e.g. satellite or airborne imagery). However, there are few ecologically based frameworks for integrating these data into meaningful metrics of biodiversity change. Here, I describe how concepts of pattern and scale in ecology could be used to design such a framework. I review three core topics: the role of scale in measuring and modelling biodiversity patterns with EO, scale-dependent challenges linking in situ and EO data and opportunities to apply concepts of pattern and scale to EO to improve biodiversity mapping. From this analysis emerges an actionable approach for measuring, monitoring and forecasting biodiversity change, highlighting key opportunities to establish EO as the backbone of global-scale, science-driven conservation.

Coupling GIS spatial analysis and Ensemble Niche Modelling to investigate climate change-related threats to the Sicilian pond turtle Emys trinacris, an endangered species from the Mediterranean

The pond turtle Emys trinacris is an endangered endemic species of Sicily showing a fragmented distribution throughout the main island. In this study, we applied "Ensemble Niche Modelling", combining more classical statistical techniques as Generalized Linear Models and Multivariate Adaptive Regression Splines with machine-learning approaches as Boosted Regression Trees and Maxent, to model the potential distribution of the species under current and future climatic conditions. Moreover, a "gap analysis" performed on both the species' presence sites and the predictions from the Ensemble Models is proposed to integrate outputs from these models, in order to assess the conservation status of this threatened species in the context of biodiversity management. For this aim, four "Representative Concentration Pathways", corresponding to different greenhouse gases emissions trajectories were considered to project the obtained models to both 2050 and 2070. Areas lost, gained or remaining stable for the target species in the projected models were calculated. E. trinacris' potential distribution resulted to be significantly dependent upon precipitation-linked variables, mainly precipitation of wettest and coldest quarter. Future negative effects for the conservation of this species, because of more unstable precipitation patterns and extreme meteorological events, emerged from our analyses. Further, the sites currently inhabited by E. trinacris are, for more than a half, out of the Protected Areas network, highlighting an inadequate management of the species by the authorities responsible for its protection. Our results, therefore, suggest that in the next future the Sicilian pond turtle will need the utmost attention by the scientific community to avoid the imminent risk of extinction. Finally, the gap analysis performed in GIS environment resulted to be a very informative post-modeling technique, potentially applicable to the management of species at risk and to Protected Areas' planning in many contexts.

Probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty from maximum temperature metric selection

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30-day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species' distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold-water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid-century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.