Predictions of establishment risk highlight biosurveillance priorities for invasive fish in New Zealand lakes

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Modelling spread of the invasive macrophyte Cabomba caroliniana

1. Predicting spread of non-indigenous species requires an understanding of where propagules are being transported, and whether these propagules can survive in the novel habitat and successfully integrate into the recipient community. In this study, we model potential spread of invading Cabomba caroliniana in Ontario, Canada, using a combination of passive and active dispersal models coupled with an environmental suitability model, thereby considering the first two stages of the invasion process.
2. Measures of propagule pressure incorporated both human-mediated dispersal via trailered boats, and advective flow from invaded to non-invaded systems, while habitat suitability was forecasted by combining native and global data sets and using boosted regression trees.
3. Risk of invasion differed depending on the combination of approaches used and the time period considered. Three lakes appear to be at greatest risk owing to a combination of high boater and water movement from invaded sources, and high environmental suitability. The best predictors of lake suitability were pH, mean lake temperature and dissolved calcium concentration. Hundreds of lakes in Ontario may be suitable for establishment of Cabomba , highlighting the need for vector management.     

Modeling the role of the close‐range effect and environmental variables in the occurrence and spread of Phragmites australis in four sites on the Finnish coast of the Gulf of Finland and the Archipelago Sea

Phragmites australis, a native helophyte in coastal areas of the Baltic Sea, has significantly spread on the Finnish coast in the last decades raising ecological questions and social interest and concern due to the important role it plays in the ecosystem dynamics of shallow coastal areas. Despite its important implications on the planning and management of the area, predictive modeling of Phragmites distribution is not well studied. We examined the prevalence and progression of Phragmites in four sites along the Southern Finnish coast in multiple time frames in relation to a number of predictors. We also analyzed patterns of neighborhood effect on the expansion and disappearance of Phragmites in a cellular data model. We developed boosted regression trees models to predict Phragmites occurrences and produce maps of habitat suitability. Various Phragmites spread figures were observed in different areas and time periods, with a minimum annual expansion rate of 1% and a maximum of 8%. The water depth, shore openness, and proximity to river mouths were found influential in Phragmites distribution. The neighborhood configuration partially explained the dynamics of Phragmites colonies. The boosted regression trees method was successfully used to interpolate and extrapolate Phragmites distributions in the study sites highlighting its potential for assessing habitat suitability for Phragmites along the Finnish coast. Our findings are useful for a number of applications. With variables easily available, delineation of areas susceptible for Phragmites colonization allows early management plans to be made. Given the influence of reed beds on the littoral species and ecosystem, these results can be useful for the ecological studies of coastal areas. We provide estimates of habitat suitability and quantification of Phragmites expansion in a form suitable for dynamic modeling, which would be useful for predicting future Phragmites distribution under different scenarios of land cover change and Phragmites spatial configuration.     

Thermogeography predicts the potential global range of the invasive European green crab (Carcinus maenas)

Aim The highly adaptable estuarine crab (Carcinus maenas) has successfully invaded five temperate geographic regions outside of its native Europe. Here, we determine which environmental factors predict the current distribution of C. maenas and what the potential geographic range of this species might be. We also investigated whether the invasion potential of C. maenas differs with respect to the origin of a native subpopulation. Location Models were developed using global observation records of C. maenas. Methods Boosted regression trees were used to model observations from the (1) native, (2) invasive, (3) southern European, (4) northern European and (5) the combined native and invasive geographic ranges of C. maenas. Results Most established invasions were predicted mainly based on temperature. Interestingly, the environment encountered by established invasions failed to predict the majority of northern European populations; suggesting that invasion potential may differ between distinct native populations. Supporting this suggestion, a model of northern European populations, distinguished from southern European populations based on genetic structure, only predicted established invasions south of Nova Scotia. By contrast, a model of southern European populations predicted most established invasions. Main conclusions These results suggest that invasion potential depends on the European origin of an invasive population and that most invasions have arisen from southern Europe. Finally, a model based on combined native and invasive ranges of C. maenas identified potential geographic range extension along many currently invaded coastlines and the potential invasion of countries like Chile, China, Russia, Namibia and New Zealand.     

Influence of landscape structure on endemic cicadas in New Zealand kiwifruit orchards

• 1 The endemic cicada species Amphipsalta cingulata (Fabricius) and Amphipsalta zelandica (Boisduval) are pests of New Zealand kiwifruit.
• 2 We determined the abundance of A. cingulata and A. zelandica by counting final‐instar exuviae in a block of ‘Hayward’ kiwifruit, the dominant cultivar, on each of 70 blocks on separate orchards in the Bay of Plenty, New Zealand.
• 3 We used a geographic information system and fragstats to generate predictive variables describing landscape structure in four nested landscapes ranging in size between 6.25 and 400 ha for each site. Other variables described the physical characteristics of the site and management practices. Data were analyzed by boosted regression trees, a method that combines the advantages of regression trees and machine learning.
• 4 The most influential variables differed for each species. Modified coastal landscapes with high densities of ‘Hayward’ kiwifruit were most favourable for A. cingulata. For A. zelandica, favourable landscapes contained significant areas of native forest. The 12 most influential variables accounted for 51% and 46% of the total influence of all variables measured for A. cingulata and A. zelandica, respectively.
• 5 Landscape structure was more influential than insecticide use and local site factors. Despite the apparent low vagility of cicadas, landscape structure at relatively large scales of ≥25 ha was influential for both A. cingulata and A. zelandica. The ability to use a wide range of hosts within the production landscape may account for this pattern. Key variables need to be confirmed by identifying the same patterns in other landscapes.

     

Predicting the distribution potential of an invasive frog using remotely sensed data in Hawaii

Aim Eleutherodactylus coqui (commonly known as the coqui) is a frog species native to Puerto Rico and non‐native in Hawaii. Despite its ecological and economic impacts, its potential range in Hawaii is unknown, making control and management efforts difficult. Here, we predicted the distribution potential of the coqui on the island of Hawaii. Location Puerto Rico and Hawaii. Methods We predicted its potential distribution in Hawaii using five biophysical variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) as predictors, presence/absence data collected from Puerto Rico and Hawaii and three classification methods – Classification Trees (CT), Random Forests (RF) and Support Vector Machines (SVM). Results Models developed separately using data from the native range and the invaded range predicted potential coqui habitats in Hawaii with high performance. Across the three classification methods, mean area under the ROC curve (AUC) was 0.75 for models trained using the native range data and 0.88 for models trained using the invaded range data. We achieved the highest AUC value of 0.90 using RF for models trained with invaded range data. Main conclusions Our results showed that the potential distribution of coquis on the island of Hawaii is much larger than its current distribution, with RF predicting up to 49% of the island as suitable coqui habitat. Predictions also show that most areas with an elevation between 0 and 2000 m are suitable coqui habitats, whereas the cool and dry high elevation areas beyond 2000 m elevation are unsuitable. Results show that MODIS‐derived biophysical variables are capable of characterizing coqui habitats in Hawaii.     

Life history influences the vulnerability of New Zealand galaxiids to invasive salmonids

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Geographic range determinants of two commercially important marine molluscs

Aim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood‐based generalized linear models and boosted regression trees. For each modelling technique, a two‐step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c. 1‐km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine‐resolution predictive range maps (c. 1 km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established.     

Predicting the distribution of invasive crayfish (Pacifastacus leniusculus) in a Kusiro Moor marsh (Japan) using classification and regression trees

Summer distributions of the invasive signal crayfish (Pacifastacus leniusculus) were investigated in relation to physicochemistry in a Kusiro Moor marsh and its inflows and outflows in northern Japan. Maximum crayfish abundance and biomass were 1.04 individuals/m² and 3.56 g dry mass (DM)/m² in littoral marsh habitats, and 5.84 individuals/m² and 13.48 g DM/m² in stream habitats. Classification tree analysis was used to predict crayfish occurrence at 102 sites from all habitats (i.e. littoral marsh, pelagic marsh and stream) while regression tree analyses were used to predict crayfish abundance at littoral marsh and stream sites separately. The classification tree showed that crayfish occurrence was primarily determined by undercut bank volume regardless of habitat identity. When undercut bank volume was <0.0054 m³, crayfish were predicted to be absent at marsh sites, but expected to occur at stream sites where pH and water temperature exceeded 6.5 and 14.3°C, respectively. The regression tree using only littoral marsh sites showed that undercut bank volume, followed by dissolved oxygen level, determined the splits of the tree. Crayfish abundance was highest when undercut bank volume was >0.61 m³, and moderately high when dissolved oxygen was >9.09 mg/l and undercut bank volume was <0.61 m³. On the other hand, the regression tree using only stream sites showed that water temperature was the major predictor that determined the splits. We discuss the roles of physicochemical factors as limiting factors of the distribution pattern of the invasive crayfish.     

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non‐native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species–environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our ‘best’ model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.     

Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant

Aim We demonstrate how to integrate two widely used tools for modelling the spread of invasive plants, and compare the performance of the combined model with that of its individual components using the recent range dynamics of the invasive annual weed Ambrosia artemisiifolia L. Location Austria. Methods Species distribution models, which deliver habitat‐based information on potential distributions, and interacting particle systems, which simulate spatio‐temporal range dynamics as dependent on neighbourhood configurations, were combined into a common framework. We then used the combined model to simulate the invasion of A. artemisiifolia in Austria between 1990 and 2005. For comparison, simulations were also performed with models that accounted only for habitat suitability or neighbourhood configurations. The fit of the three models to the data was assessed by likelihood ratio tests, and simulated invasion patterns were evaluated against observed ones in terms of predictive discrimination ability (area under the receiver operating characteristic curve, AUC) and spatial autocorrelation (Moran’s I). Results The combined model fitted the data significantly better than the single‐component alternatives. Simulations relying solely on parameterized spread kernels performed worst in terms of both AUC and spatial pattern formation. Simulations based only on habitat information correctly predicted infestation of susceptible areas but reproduced the autocorrelated patterns of A. artemisiifolia expansion less adequately than did the integrated model. Main conclusions Our integrated modelling approach offers a flexible tool for forecasts of spatio‐temporal invasion patterns from landscape to regional scales. As a further advantage, scenarios of environmental change can be incorporated consistently by appropriately updating habitat suitability layers. Given the susceptibility of many alien plants, including A. artemisiifolia, to both land use and climate changes, taking such scenarios into account will increasingly become relevant for the design of proactive management strategies.     

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Ecological diffusion is a theory that can be used to understand and forecast spatio‐temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white‐tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression‐based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.     

Seasonal habitat use indicates that depth may mediate the potential for invasive round goby impacts in inland lakes

1. The round goby (Neogobius melanostomus) is among the fastest-spreading introduced aquatic species in North America and is radiating inland from the Great Lakes into freshwater ecosystems across the landscape. Predicting and managing the impacts of round gobies requires information on the factors influencing their distribution in habitats along the invasion front, yet this information is not available for many recently invaded ecosystems. We evaluated the seasonal habitat use and biomass of round gobies in an inland temperate lake to define the spatiotemporal scope of biological interactions at the leading edge of the round goby invasion.
2. Using novel statistical approaches, we combined hierarchical models that control for imperfect species detection with flexible smooth terms to describe non-linear relationships between round goby abundance and environmental gradients. Subsequently, we generated accurate detection-corrected estimates of the standing stock biomass of round gobies.
3. Our results show seasonally differentiated habitat niches, where suitable round goby habitat in summer months is restricted to shallow depths (<18.4 m) with a mixture of vegetative and mussel cover. We found high round goby biomass of 122 kg/ha in occupied habitats during the summer, with a total lake-wide biomass of 766,000 kg. In winter, round gobies migrate to deep offshore habitats and disperse, dramatically altering their scope for biological interactions with resident aquatic species across summer and winter seasons.
4. The results of this study indicate that the scope of biological interactions in inland lakes may be seasonally variable, with potential for high round goby biomass in shallow lakes or at the periphery of deep lakes in the summer months. Such shallow-water habitats may therefore present higher risk of ecological impacts from round gobies in invaded lentic ecosystems. As round gobies expand inland, consideration of seasonal habitat use will be an important factor in predicting the impacts of this pervasive invader.

     

外来入侵物种的风险评估定量模型及应用

预防生物入侵的一个重要手段是对外来物种进行风险评估,应用模型则是定量评估的必备方法。本文简述了常用的适生性风险评估模型,概述了诸如遗传算法、模糊包络模型、自组织特征映射网络等较新的理论方法,它们使用环境变量和物种实际分布数据,利用不同的机理模型预测物种潜在分布区。本文还综述了适用于研究物种扩散性的模型,积分差分方程模型可以模拟物种扩散行为,元胞自动机模型可以揭示种间竞争关系,景观中性模型大多用于种群动态等生态过程的研究。     

Fine-scale spatial and temporal distribution patterns of large marine predators in a biodiversity hotspot

Aim

Large marine predators, such as cetaceans and sharks, play a crucial role in maintaining biodiversity patterns and ecosystem function, yet few estimates of their spatial distribution exist. We aimed to determine the species richness of large marine predators and investigate their fine-scale spatiotemporal distribution patterns to inform conservation management.

Location

The Hauraki Gulf/Tīkapa Moana/Te Moananui-ā-Toi, Aotearoa/New Zealand.

Methods

We conducted a replicate systematic aerial survey over 12 months. Flexible machine learning models were used to explore relationships between large marine predator occurrence (Bryde's whales, common and bottlenose dolphins, bronze whaler, pelagic and immature hammerhead sharks) and environmental and biotic variables, and predict their monthly distribution and associated spatially explicit uncertainty.

Results

We revealed that temporally dynamic variables, such as prey distribution and sea surface temperature, were important for predicting the occurrence of the study species and species groups. While there was variation in temporal and spatial distribution, predicted richness peaked in summer and was the highest in coastal habitats during that time, providing insight into changes in distributions over time and between species.

Main Conclusions

Temporal changes in distribution are not routinely accounted for in species distribution studies. Our approach highlights the value of multispecies surveys and the importance of considering temporally variable abiotic and biotic drivers for understanding biodiversity patterns when informing ecosystem-scale conservation planning and dynamic ocean management.     

Precipitation and winter temperature predict long‐term range‐scale abundance changes in Western North American birds

Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long‐term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32‐year period. Using boosted regression trees, we built presence‐absence and abundance models that related the presence and abundance of 132 bird species to spatial variation in climatic conditions. Presence/absence models built using 1970–1974 data forecast the distributions of the majority of species in the later time period, 1998–2002 (mean AUC = 0.79 ± 0.01). Hindcast models performed equivalently (mean AUC = 0.82 ± 0.01). Correlations between observed and predicted abundances were also statistically significant for most species (forecast mean Spearman′s ρ = 0.34 ± 0.02, hindcast = 0.39 ± 0.02). The most stringent test is to test predicted changes in geographic patterns through time. Observed changes in abundance patterns were significantly positively correlated with those predicted for 59% of species (mean Spearman′s ρ = 0.28 ± 0.02, across all species). Three precipitation variables (for the wettest month, breeding season, and driest month) and minimum temperature of the coldest month were the most important predictors of bird distributions and abundances in this region, and hence of abundance changes through time. Our results suggest that models describing associations between climatic variables and abundance patterns can predict changes through time for some species, and that changes in precipitation and winter temperature appear to have already driven shifts in the geographic patterns of abundance of bird populations in western North America.     

Predicting probability of occurrence and factors affecting distribution and abundance of three Ozark endemic crayfish species at multiple spatial scales

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Influence of patch size and connectivity on beach and dune species in land-uplift coasts

Background: Land-uplift beaches and adjacent dunes contribute considerably to natural diversity. In such fragmented habitat types, the size and connectivity of a habitat patch are hypothesised to strongly influence the distribution of species, particularly the most habitat-specific ones.

Aims: To test this hypothesis, our study compared the effects of habitat pattern (patch size and connectivity) and local environmental factors on the distribution and richness of beach species.

Methods: We collected extensive observational data on vegetation and environment from beach systems along a 600-km land-uplift gradient on the Baltic Sea coast. The analyses were repeated with three modelling methods to ensure that the results were independent of the selected method.

Results and conclusions: Our results indicate that patch size and connectivity influence the occurrence and richness of habitat specialists, while total beach species richness is less dependent on the habitat pattern. Patch size and connectivity are as influential on beach vegetation as local environmental drivers. Unexpectedly, largest patch size or highest connectivity does not appear to maximise species richness or the probability of species occurrence. Instead, the study highlights species-specific responses and the value of also relatively small and isolated habitat patches. Both the diverse network of habitat patches and local environmental variability should be accounted for to efficiently preserve beach species.