Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models

Aim

Climate is considered a major driver of species distributions. Long‐term climatic means are commonly used as predictors in correlative species distribution models (SDMs). However, this coarse temporal resolution does not reflect local conditions that populations experience, such as short‐term weather extremes, which may have a strong impact on population dynamics and local distributions. We here compare the performance of climate‐ and weather‐based predictors in regional SDMs and their influence on future predictions, which are increasingly used in conservation planning.

Location

South‐western Germany.

Methods

We built different SDMs for 20 Orthoptera species based on three predictor sets at a regional scale for current and future climate scenarios. We calculated standard bioclimatic variables and yearly and seasonal sets of climate change indicating variables of weather extremes. As the impact of extreme events may be stronger for habitat specialists than for generalists, we distinguished species’ degrees of specialization. We computed linear mixed‐effects models to identify significant effects of algorithm, predictor set and specialization on model performance and calculated correlations and geographical niche overlap between spatial predictions.

Results

Current predictions were rather similar among all predictor sets, but highly variable for future climate scenarios. Bioclimatic and seasonal weather predictors performed slightly better than yearly weather predictors, though performance differences were minor. We found no evidence that specialists are more sensitive to weather extremes than generalists.

Main conclusions

For future projections of species distributions, SDM predictor selection should not solely be based on current performances and predictions. As long‐term climate and short‐term weather predictors represent different environmental drivers of a species’ distribution, we argue to interpret diverging future projections as complements. Even if similar current performances and predictions might imply their equivalency, favouring one predictor set neglects important aspects of future distributions and might mislead conservation decisions based on them.

     

Modelling the distribution and compositional variation of plant communities at the continental scale

Aim

We investigate whether (1) environmental predictors allow to delineate the distribution of discrete community types at the continental scale and (2) how data completeness influences model generalization in relation to the compositional variation of the modelled entities.

Location

Europe.

Methods

We used comprehensive datasets of two community types of conservation concern in Europe: acidophilous beech forests and base‐rich fens. We computed community distribution models (CDMs) calibrated with environmental predictors to predict the occurrence of both community types, evaluating geographical transferability, interpolation and extrapolation under different scenarios of sampling bias. We used generalized dissimilarity modelling (GDM) to assess the role of geographical and environmental drivers in compositional variation within the predicted distributions.

Results

For the two community types, CDMs computed for the whole study area provided good performance when evaluated by random cross‐validation and external validation. Geographical transferability provided lower but relatively good performance, while model extrapolation performed poorly when compared with interpolation. Generalized dissimilarity modelling showed a predominant effect of geographical distance on compositional variation, complemented with the environmental predictors that also influenced habitat suitability.

Main conclusions

Correlative approaches typically used for modelling the distribution of individual species are also useful for delineating the potential area of occupancy of community types at the continental scale, when using consistent definitions of the modelled entity and high data completeness. The combination of CDMs with GDM further improves the understanding of diversity patterns of plant communities, providing spatially explicit information for mapping vegetation diversity and related habitat types at large scales.

     

Forecasting fish distribution along stream networks: brown trout (Salmo trutta) in Europe

Aim

Species inhabiting fresh waters are severely affected by climate change and other anthropogenic stressors. Effective management and conservation plans require advances in the accuracy and reliability of species distribution forecasts. Here, we forecast distribution shifts of Salmo trutta based on environmental predictors and examine the effect of using different statistical techniques and varying geographical extents on the performance and extrapolation of the models obtained.

Location

Watercourses of Ebro, Elbe and Danube river basins (c. 1,041,000 km²; Mediterranean and temperate climates, Europe).

Methods

The occurrence of S. trutta and variables of climate, land cover and stream topography were assigned to stream reaches. Data obtained were used to build correlative species distribution models (SDMs) and forecasts for future decades (2020s, 2050s and 2080s) under the A1b emissions scenario, using four statistical techniques (generalised linear models, generalised additive models, random forest, and multivariate adaptive regression).

Results

The SDMs showed an excellent performance. Climate was a better predictor than stream topography, while land cover characteristics were not necessary to improve performance. Forecasts predict the distribution of S. trutta to become increasingly restricted over time. The geographical extent of data had a weak impact on model performance and gain/loss values, but better species response curves were generated using data from all three basins collectively. By 2080, 64% of the stream reaches sampled will be unsuitable habitats for S. trutta, with Elbe basin being the most affected, and virtually no new habitats will be gained in any basin.

Main conclusions

More reliable predictions are obtained when the geographical data used for modelling approximate the environmental range where the species is present. Future research incorporating both correlative and mechanistic approaches may increase robustness and accuracy of predictions.

     

Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions

Aim

Assessing the influence of land cover in species distribution modelling is limited by the availability of fine‐resolution land‐cover data appropriate for most species responses. Remote‐sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote‐sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon.

Location

Central Coast Range, Cascade and Klamath Mountains Oregon, USA.

Methods

Information on bird observations was collected from 4598 fixed‐radius point counts. Reflectance data were obtained using 30‐m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree (BRT) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve (AUC) values.

Results

Prediction success of models using all reflectance values was high (mean AUC = 0.79 ± 0.10 SD). Further, model performance using individual reflectance bands exceeded those that used only Normalized Difference Vegetation Index (NDVI). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest.

Main Conclusions

We demonstrate that unclassified remote‐sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high‐resolution predictions are required, including habitat change detection studies, identification of fine‐scale biodiversity hotspots and reserve design.

     

Does local habitat fragmentation affect large‐scale distributions? The case of a specialist grassland bird

Aim

Although the negative effects of habitat fragmentation have been widely documented at the landscape scale, much less is known about its impacts on species distributions at the biogeographical scale. We hypothesize that fragmentation influences the large‐scale distribution of area‐ and edge‐sensitive species by limiting their occurrence in regions with fragmented habitats , despite otherwise favourable environmental conditions. We test this hypothesis by assessing the interplay of climate and landscape factors influencing the distribution of the calandra lark, a grassland specialist that is highly sensitive to habitat fragmentation.

Location

Iberia Peninsula, Europe.

Methods

Ecological niche modelling was used to investigate the relative influence of climate/topography, landscape fragmentation and spatial structure on calandra lark distribution. Modelling assumed explicitly a hierarchically structured effect among explanatory variables, with climate/topography operating at broader spatial scales than landscape variables. An eigenvector‐based spatial filtering approach was used to cancel bias introduced by spatial autocorrelation. The information theoretic approach was used in model selection, and variation partitioning was used to isolate the unique and shared effects of sets of explanatory variables.

Results

Climate and topography were the most influential variables shaping the distribution of calandra lark, but incorporating landscape metrics contributed significantly to model improvement. The probability of calandra lark occurrence increased with total habitat area and declined with the number of patches and edge density. Variation partitioning showed a strong overlap between variation explained by climate/topography and landscape variables. After accounting for spatial structure in species distribution, the explanatory power of environmental variables remained largely unchanged.

Main conclusions

We have shown here that landscape fragmentation can influence species distributions at the biogeographical scale. Incorporating fragmentation metrics into large‐scale ecological niche models may contribute for a better understanding of mechanism driving species distributions and for improving predictive modelling of range shifts associated with land use and climate changes.

     

Improved spatial estimates of climate predict patchier species distributions

Aim

Correlative species distribution models (SDMs) combined with spatial layers of climate and species' localities represent a frequently utilized and rapid method for generating spatial estimates of species distributions. However, an SDM is only as accurate as the inputs upon which it is based. Current best‐practice climate layers commonly utilized in SDM (e.g. ANUCLIM) are frequently inaccurate and biased spatially. Here, we statistically downscale 30 years of existing spatial weather estimates against empirical weather data and spatial layers of topography and vegetation to produce highly accurate spatial layers of weather. We proceed to demonstrate the effect of inaccurately quantified spatial data on SDM outcomes.

Location

The Australian Wet Tropics.

Methods

We use Boosted Regression Trees (BRTs) to generate 30 years of spatial estimates of daily maximum and minimum temperature for the study region and aggregate the resultant weather layers into ‘accuCLIM’ climate summaries, comparable with those generated by current best‐practice climate layers. We proceed to generate for seven species of rainforest skink comparable SDMs within species; one model based on ANUCLIM climate estimates and another based on accuCLIM climate estimates.

Results

Boosted Regression Trees weather layers are more accurate with respect to empirically measured temperature, particularly for maximum temperature, when compared to current best‐practice weather layers. ANUCLIM climate layers are least accurate in heavily forested upland regions, frequently over‐predicting empirical mean maximum temperature by as much as 7°. Distributions of the focal species as predicted by accuCLIM were more fragmented and contained less core distributional area.

Conclusion

Combined these results reveal a source of bias in climate‐based SDMs and indicate a solution in the form of statistical downscaling. This technique will allow researchers to produce fine‐grained, ground‐truthed spatial estimates of weather based on existing estimates, which can be aggregated in novel ways, and applied to correlative or process‐based modelling techniques.

     

Assessing habitat loss and fragmentation and their effects on population viability of forest specialist birds: Linking biogeographical and population approaches

Aim

Biogeographic approaches usually have been developed apart from population ecology, resulting in predictive models without key parameters needed to account for reproductive and behavioural limitations on dispersal. Our aim was to incorporate fully spatially explicit population traits into a classic species distribution model (SDM) using Geographic Information Systems (GIS), aiming at conservation purposes.

Location

Southern South America.

Methods

Our analysis incorporates the effects of habitat loss and fragmentation on population viability and therefore provides insights into how much spatially explicit population traits can improve the SDM prediction of habitable habitat. We utilized a well‐studied focal endemic bird of South American temperate rainforests (Scelorchilus rubecula). First, at a large scale, we assessed the historical extent habitat based on climate envelopes in an SDM. Second, we used a land cover change analysis at a regional scale to account for recent habitat loss and fragmentation. Third, we used empirically derived criteria to predict population responses to fragmented forest landscapes to identify actual losses of habitat and population. Then we selected three sites of high conservation value in southern Chile and applied our population model. Finally, we discuss the degree to which spatially explicit population traits can improve the SDM output without intervening in the modelling process itself.

Results

We found a historical habitat loss of 39.12% and an additional forest cover loss of 3.03% during 2000–2014; the latter occurred with a high degree of fragmentation, reducing the overall estimation of (1) carrying capacity by ?82.4%, ?33.1% and ?45.1% and (2) estimated number of pairs on viable populations by ?84.1%, ?33.0% and ?54.6% on the three selected sites.

Main conclusion

We conclude that our approach sharpened the SDM prediction on environmental suitability by 54.4%, adjusting the habitable area by adding population parameters through GIS, and allowing to incorporate other phenomena as fragmentation and habitat loss.

     

Contrasting climate niches among co‐occurring subdominant forbs of the sagebrush steppe

Aim

Abiotic conditions are key components that determine the distribution of species. However, co‐occurring species can respond differently to the same factors, and determining which climate components are most predictive of geographic distributions is important for understanding community response to climate change. Here, we estimate and compare climate niches of ten subdominant, herbaceous forb species common in sagebrush steppe systems, asking how niches differ among co‐occurring species and whether more closely related species exhibit higher niche overlap.

Location

Western United States.

Methods

We used herbarium records and ecological niche modelling to estimate area of occupancy, niche breadth and overlap, and describe characteristics of suitable climate. We compared mean values and variability in summer precipitation and minimum temperatures at occurrence locations among species, plant families, and growth forms, and related estimated phylogenetic distances to niche overlap.

Results

Species varied in the size and spatial distribution of suitable climate and in niche breadth. Species also differed in the variables contributing to their suitable climate and in mean values, spatial variation and interannual variation in highly predictive climate variables. Only two of ten species shared comparable climate niches. We found family‐level differences associated with variation in summer precipitation and minimum temperatures, as well as in mean minimum temperatures. Growth forms differed in their association with variability in summer precipitation and minimum temperatures. We found no relationship between phylogenetic distance and niche overlap among our species.

Main conclusions

We identified contrasting climate niches for ten Great Basin understorey forbs, including differences in both mean values and climate variability. These estimates can guide species selection for restoration by identifying species with a high tolerance for climate variability and large climatic niches. They can also help conservationists to understand which species may be least tolerant of climate variability, and potentially most vulnerable to climate change.

     

Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean

Aim

The distribution of marine predators is driven by the distribution and abundance of their prey; areas preferred by multiple marine predator species should therefore indicate areas of ecological significance. The Southern Ocean supports large populations of seabirds and marine mammals and is undergoing rapid environmental change. The management and conservation of these predators and their environment relies on understanding their distribution and its link with the biophysical environment, as the latter determines the distribution and abundance of prey. We addressed this issue using tracking data from 14 species of marine predators to identify important habitat.

Location

Indian Ocean sector of the Southern Ocean.

Methods

We used tracking data from 538 tag deployments made over a decade at the Subantarctic Prince Edward Islands. For each real track, we simulated a set of pseudo‐tracks that allowed a presence‐availability habitat modelling approach that estimates an animal's habitat preference. Using model ensembles of boosted regression trees and random forests, we modelled these tracks as a response to a set of 17 environmental variables. We combined the resulting species‐specific models to evaluate areas of mean importance.

Results

Real tracking locations covered 39.75 million km², up to 7,813 km from the Prince Edward Islands. Areas of high mean importance were located broadly from the Subtropical Zone to the Polar Frontal Zone in summer and from the Subantarctic to Antarctic Zones in winter. Areas of high mean importance were best predicted by factors including wind speed, sea surface temperature, depth and current speed.

Main conclusions

The models and predictions developed here identify important habitat of marine predators around the Prince Edward Islands and can support the large‐scale conservation and management of Subantarctic ecosystems and the marine predators they sustain. The results also form the basis of future efforts to predict the consequences of environmental change.

     

Use of predictive habitat modelling to assess the distribution and extent of the current protection of ‘listed’ deep‐sea habitats

Aim

To demonstrate the application of predictive species distribution modelling methods to habitat mapping and assessment of percentage area‐based conservation targets.

Location

The NE Atlantic deep sea (UK and Irish extended continental shelf limits).

Methods

MaxEnt modelling of three listed habitats (Lophelia pertusa (Linnaeus, 1758) reef (LpReef), Pheronema carpenteri (WyvilleThomson, 1869) aggregations (PcAggs) and Syringammina fragilissima (Brady, 1883) aggregations (SfAggs)), with some pre‐selection of variables by generalized additive modelling. Models are validated using repeated 70/30 build/test data splits using AUC and threshold‐dependent assessment methods. Predicted distribution maps are used to assess the adequacy of existing area closures for the protection of listed habitats and to assess percentage representation of each community within existing MPA networks.

Results

Model performances are rated as fair (LpReef), excellent (PcAggs) and good (SfAggs). Current closures are focused on the protection of cold‐water coral reef and incidentally capture some SfAggs suitable environments, but largely fail to protect PcAggs. Considering the wider network of MPAs in the study region, approximately 23% (LpReef), 2% (PcAggs) and 6% (SfAggs) of the area predicted as suitable for each habitat respectively is contained within an MPA.

Main conclusions

To date, decisions on area closures for the protection of ‘listed’ deep‐sea habitats have been based on maps of recorded presence of species that are taken as being indicative of that habitat. Predictive habitat modelling may provide a useful method of better estimating the extent of listed habitats, providing direction for future MPA establishment and a means of assessing MPA network effectiveness against politically set percentage targets. Given the coarse resolution of the model, percentages should be taken as maximal figures, with habitat occurrence likely to be less prevalent in reality.

     

Predicting global ascidian invasions

Aim

Many species of ascidians are invasive and can cause both ecological and economic losses. Here, we describe risk assessment for nineteen ascidian species and predict coastal regions that are more vulnerable to arrival and expansion.

Location

Global.

Methods

We used ensemble niche modelling with three algorithms (Random Forest, Support Vector Machine and MaxEnt) to predict ecologically suitable areas and evaluated our predictions using independent (area under the curve—AUC) and dependent thresholds (true skill statistics—TSS). Environmental variables were maximum and the range of sea surface temperature, mean salinity and maximum chlorophyll. We used our niche modelling results and a modified invasibility index to compare invasion risk among 15 coastal regions.

Results

Currently, the most invaded regions are in temperate latitudes of the Northern Hemisphere and Temperate Australasia, which are regions most prone for new invasions. In the tropics, the West and Central Indo‐Pacific are two regions of strong concern, the former with high risk of primary invasion by Botryllus schlosseri and Didemnum perlucidum. In the Southern Hemisphere, the Southwest and Southeast Atlantic are most at risk, both subject to invasion by Botrylloides violaceus, Didemnum vexillum, Molgula manhattensis and Styela clava among others. Regions most at risk of expansion of established invasive species are the Central Indo‐Pacific, Northwest Pacific, Mediterranean and West Indo‐Pacific.

Main conclusions

All regions studied have areas that are suitable and connected to receive new ascidian introductions or that may permit the spread of already established species. Risk comparison of primary introductions and expansion of established introduced ascidians among regions will allow managers to prioritize species of concern for each region both for monitoring future introductions or to enforce control actions towards established species to decrease the risk of regional expansion.

     

Predicting distributions,habitat preferences and associated conservation implications for a genus of rare fishes,seahorses (Hippocampus spp.)

Aim

To identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.

Location

The shallow seas.

Methods

We applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.

Results

We demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.

Main conclusions

We highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.

     

Species’ thermal ranges predict changes in reef fish community structure during 8 years of extreme temperature variation

Aim

To assess whether observed thermal bounds in species’ latitudinal ranges (i.e., realized thermal niches) can be used to predict patterns of occurrence and abundance changes observed during a marine heatwave, relative to other important life history and functional traits.

Location

Rottnest Island, Western Australia.

Methods

A time series of standardized quantitative surveys of reef fishes spanning 8 years of pronounced ocean temperature change is used to test whether accurate predictions on shifts in species occupancy and abundance are possible using species traits.

Results

Species‐level responses in occurrence and abundance were closely related to the mid‐point of their realized thermal niche, more so than body size, range size or trophic level. Most of the species that disappeared from survey counts during the heatwave were characterized by geographic ranges that did not extend to latitudes with temperatures equivalent to the ocean temperature peak during the heatwave. We thus find support for the hypothesis that current distribution limits are set directly or indirectly by temperature and are highly responsive to ocean temperature variability.

Main conclusions

Our study shows that reef fish community structure can change very quickly when exposed to extreme thermal anomalies, in directions predicted from the realized thermal niche of the species present. Such predictions can thus identify species that will be most responsive to changing ocean climate. Continued warming, coupled with periodic extreme heat events, may lead to the loss of ecosystem services and ecological functions, as mobile species relocate to more hospitable climes, while less mobile species may head towards extinction.

     

Projecting potential future shifts in species composition of European urban plant communities

Aim

Urban floras are composed of species of different origin, both native and alien, and with various traits and niches. It is likely that these species will respond to the ongoing climate change in different ways, resulting in future species compositions with no analogues in current European cities. Our goal was to estimate potential shifts in plant species composition in European cities under different scenarios of climate change for the 21st century.

Location

Europe.

Methods

Potential changes in the distribution of 375 species currently growing in 60 large cities in Southern, Central and Western Europe were modelled using generalized linear models and four climate change projections for two future periods (2041–2060 and 2061–2080). These projections were based on two global climate models (CCSM4 and MIROC‐ESM) and two Representative Concentration Pathways (2.6 and 8.5).

Results

Results were similar across all climate projections, suggesting that the composition of urban plant communities will change considerably due to future climate change. However, even under the most severe climate change scenario, native and alien species will respond to climate change similarly. Many currently established species will decline and others, especially annuals currently restricted to Southern Europe, will spread to northern cities. In contrast, perennial herbs, woody plants and most species with temperate continental and oceanic distribution ranges will make up a smaller proportion of future European urban plant communities in comparison with the present communities.

Main conclusions

The projected 21st century climate change will lead to considerable changes in the species composition of urban floras. These changes will affect the structure and functioning of urban plant communities.

     

Combining point‐process and landscape vegetation models to predict large herbivore distributions in space and time—A case study of Rupicapra rupicapra

Aim

When modelling the distribution of animals under current and future conditions, both their response to environmental constraints and their resources’ response to these environmental constraints need to be taken into account. Here, we develop a framework to predict the distribution of large herbivores under global change, while accounting for changes in their main resources. We applied it to Rupicapra rupicapra, the chamois of the European Alps.

Location

The Bauges Regional Park (French Alps).

Methods

We built sixteen plant functional groups (PFGs) that account for the chamois’ diet (estimated from sequenced environmental DNA found in the faeces), climatic requirements, dispersal limitations, successional stage and interaction for light. These PFGs were then simulated using a dynamic vegetation model, under current and future climatic conditions up to 2100. Finally, we modelled the spatial distribution of the chamois under both current and future conditions using a point‐process model applied to either climate‐only variables or climate and simulated vegetation structure variables.

Results

Both the climate‐only and the climate and vegetation models successfully predicted the current distribution of the chamois species. However, when applied into the future, the predictions differed widely. While the climate‐only models predicted an 80% decrease in total species occupancy, including vegetation structure and plant resources for chamois in the model provided more optimistic predictions because they account for the transient dynamics of the vegetation (?20% in species occupancy).

Main conclusions

Applying our framework to the chamois shows that the inclusion of ecological mechanisms (i.e., plant resources) produces more realistic predictions under current conditions and should prove useful for anticipating future impacts. We have shown that discounting the pure effects of vegetation on chamois might lead to overpessimistic predictions under climate change. Our approach paves the way for improved synergies between different fields to produce biodiversity scenarios.

     

Identifying the factors that determine the severity and type of alien bird impacts

Aim

To identify traits related to the severity and type of environmental impacts generated by alien bird species, in order to improve our ability to predict which species may have the most damaging impacts.

Location

Global.

Methods

Information on traits hypothesized to influence the severity and type of alien bird impacts was collated for 113 bird species. These data were analysed using mixed effects models accounting for phylogenetic non‐independence of species.

Results

The severity and type of impacts generated by alien bird species are not randomly distributed with respect to their traits. Alien range size and habitat breadth were strongly associated with impact severity. Predation impacts were strongly associated with dietary preference, but also with alien range size, relative brain size and residence time. Impacts mediated by interactions with other alien species were related to alien range size and diet breadth.

Main conclusions

Widely distributed generalist alien birds have the most severe environmental impacts. This may be because these species have greater opportunity to cause environmental impacts through their sheer number and ubiquity, but this could also be because they are more likely to be identified and studied. Our study found little evidence for an effect of per capita impact on impact severity.

     

Predicted effects of climate change on potential sources of non‐indigenous marine species

Aim

We compare the present‐day global ocean climate with future climatologies based on Intergovernmental Panel on Climate Change (IPCC) models and examine whether changes in global ocean climate will affect the environmental similarity of New Zealand's (NZ) coastal environments to those of the rest of the world. Our underlying rationale is that environmental changes to source and recipient regions may result in changes to the risk of non‐indigenous species survival and establishment.

Location

Coastlines of global continents and islands.

Methods

We determined the environmental similarity (Euclidean distance) between global coastlines and north‐east NZ for 2005 and 2050 using data on coastal seawater surface temperature and salinity. Anticipated climate models from the SRES A1B scenario family were used to derive coastal climatologies for 2050.

Results

During the next decades, most global regions will experience an increase in coastal seawater surface temperatures and a decline or increase in salinity. This will result in changes in the similarity of other coastal environments to north‐east NZ's coastal areas. Global regions that presently have high environmental similarity to north‐east NZ will variously retain this level of similarity, become more similar or decrease in environmental similarity. Some regions that presently have a low level of similarity will become more similar to NZ. Our models predict a widespread decrease in the seasonal variation in environmental similarity to NZ.

Main conclusions

Anticipated changes in the global ocean climate have the potential to change the risk of survival and establishment of non‐indigenous marine species arriving to NZ from some global regions. Predicted changes to global human transport networks over the coming decades highlight the importance of incorporating climate change into conservation planning and modelling.

     

Underestimated ranges and overlooked refuges from amphibian chytridiomycosis

Aim

Accurately documenting and predicting declines and shifts in species’ distributions is fundamental for implementing effective conservation strategies and directing future research; species distribution models (SDM) have become a powerful tool for such work. Nevertheless, much of the data used to create these models are opportunistic and often violate some of their basic assumptions. We use amphibian declines and extinctions linked to the fungus Batrachochytrium dendrobatidis (Bd) to examine how sampling biases in data collection can affect what we know of this disease and its effect on amphibians in the wild.

Location

Queensland, Australia.

Methods

We developed a distribution model for Bd incorporating known locality records for Bd and a subset of climatic variables that should correctly characterize its distribution. We tested this (original) model with additional surveys, recorded new Bd observations in novel environments and reran the distribution model. We then investigated the difference between the original and new models, and used frog abundance and infection status data from two of these new localities to look at the susceptibility of the torrent frog Litoria nannotis to chytridiomycosis.

Results

While largely correct, the original SDM underestimated the distribution of Bd; sampling in ‘unsuitable’ drier environments discovered abundant populations of susceptible frogs with pathogen prevalences of up to 100%. The validation surveys further uncovered a new population of the frog Litoria lorica coexisting with the pathogen; this species was previously believed to be an extinct rain forest endemic.

Main conclusion

Our results indicate that SDMs constructed using opportunistically collected data can be biased if species are not at equilibrium with their environment or because environmental gradients have not been adequately sampled. For disease ecology, the better estimations of pathogen distribution may lead to the discovery of new populations persisting at the edge of their range, which has important implications for the conservation of species threatened by chytridiomycosis.

     

Does one size fit all? A multispecies approach to regional landscape corridor planning

Aim

The practical value of the single‐species approach to conserve biodiversity could be minimal or negligible when sympatric species are limited by factors that are not relevant to the proposed umbrella species. In this study, we quantitatively evaluated as follows: (1) habitat suitability and potential movement corridors of a single umbrella species, giant panda (Ailuropoda melanoleuca); (2) habitat suitability of sympatric mammals; and (3) the potential effectiveness of the single‐species corridor planning to preserve suitable habitat and its connectivity of other focal species.

Location

Qinling Mountains, central part of China (15,000 km²).

Methods

We collected species distribution, environmental and anthropogenic data and conducted species occupancy modelling for giant panda and six other sympatric species (i.e., takin Budorcas taxicolor, tufted deer Elaphodus cephalophus, Chinese goral Naemorhedus griseus, Reeve's muntjac Muntiacus reevesi, leopard cat Prionailurus bengalensis and yellow‐throated marten Martes flavigula). We then conducted circuit models to identify potential corridors for each species and evaluated the effectiveness of giant panda corridors to restore the habitat connectivity for these sympatric mammals.

Results

Occupancy modelling revealed that each species had a unique set of environmental variables associated with its distribution in the Qinling Mountains. We found that giant panda and all other focal species had some degree of fragmentation to their suitable habitat that required restoring habitat connectivity. Among the eight potential giant panda corridors, conservation efforts to reduce anthropogenic impacts would significantly improve the effectiveness of six corridors, while the other two corridors would require altering the vegetation. Five proposed giant panda corridors had remarkable overlap with corridors proposed for other species. We suggest two giant panda corridors as a priority due to their potential to maximize the benefits to both giant panda and a broader suite of mammals.

Main conclusions

Corridor planning in this region of China will likely continue using the single‐species policy, but our results highlight that not all potential giant panda corridors have equal effectiveness for other wildlife species. When offered multiple alternative actions, conservation planners can prioritize corridor development based on a multispecies perspective without loss of connectivity for the priority species. This approach has strong implications to the conservation of wildlife communities in China, and elsewhere, where conservation plans developed for a single‐species garner most available funding and institutional support.

     

Multifaceted biodiversity modelling at macroecological scales using Gaussian processes

Aim

Modelling the response of β‐diversity (i.e., the turnover in species composition among sites) to environmental variation has wide‐ranging applications, including informing conservation planning, understanding community assembly and forecasting the impacts of climate change. However, modelling β‐diversity is challenging, especially for multiple diversity facets (i.e., taxonomic, functional and phylogenetic diversity), and current methods have important limitations. Here, we present a new approach for predicting the response of multifaceted β‐diversity to the environment, called Multifaceted Biodiversity Modelling (MBM). We illustrate the approach using both a plant diversity dataset from the French Alps and a set of simulated data. We also provide an implementation via an R package.

Location

French Alps.

Methods

For both the French Alps and the simulated communities, we compute β‐diversity indices (e.g., Sørensen dissimilarity, mean functional/phylogenetic pairwise distance) among site pairs. We then apply Gaussian process regression, a flexible nonlinear modelling technique, to predict β‐diversity in response to environmental distance among site pairs. For comparison, we also perform similar analyses using Generalized Dissimilarity Modelling (GDM), a well‐established method for modelling β‐diversity in response to environmental distance.

Results

In the Alps, we observed a general increase in taxonomic (TD) and functional (FD) β‐diversity (i.e., site pairs were more different from each other) as the climatic distance between site pairs increased. GDM performed better for TD and FD when fitting to calibration data, whereas MBM performed better for both when predicting to a validation dataset. For phylogenetic β‐diversity, MBM outperformed GDM in predicting the observed decrease in phylogenetic β‐diversity with increasing climatic distance.

Main conclusions

Multifaceted Biodiversity Modelling provides a flexible new approach that expands our capacity to model multiple facets of β‐diversity. Advantages of MBM over existing methods include simpler assumptions, more flexible modelling, potential to consider multiple facets of diversity across a range of diversity indices, and robust uncertainty estimation.