Accounting for imperfect detection of groups and individuals when estimating abundance

If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double‐observer models, distance sampling models and combined double‐observer and distance sampling models (known as mark‐recapture‐distance‐sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under‐counted, but not over‐counted. The estimator combines an MRDS model with an N‐mixture model to account for imperfect detection of individuals. The new MRDS‐Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS‐Nmix model to an MRDS model. Abundance estimates generated by the MRDS‐Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re‐allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.     

An evaluation of methods for modelling species distributions

Aim Various statistical techniques have been used to model species probabilities of occurrence in response to environmental conditions. This paper provides a comprehensive assessment of methods and investigates whether errors in model predictions are associated to specific kinds of geographical and environmental distributions of species. Location Portugal, Western Europe. Methods Probabilities of occurrence for 44 species of amphibians and reptiles in Portugal were modelled using seven modelling techniques: Gower metric, Ecological Niche Factor Analysis, classification trees, neural networks, generalized linear models, generalized additive models and spatial interpolators. Generalized linear and additive models were constructed with and without a term accounting for spatial autocorrelation. Model performance was measured using two methods: sensitivity and Kappa index. Species were grouped according to their spatial (area of occupancy and extent of occurrence) and environmental (marginality and tolerance) distributions. Two‐way comparison tests were performed to detect significant interactions between models and species groups. Results Interaction between model and species groups was significant for both sensitivity and Kappa index. This indicates that model performance varied for species with different geographical and environmental distributions. Artificial neural networks performed generally better, immediately followed by generalized additive models including a covariate term for spatial autocorrelation. Non‐parametric methods were preferred to parametric approaches, especially when modelling distributions of species with a greater area of occupancy, a larger extent of occurrence, lower marginality and higher tolerance. Main conclusions This is a first attempt to relate performance of modelling techniques with species spatial and environmental distributions. Results indicate a strong relationship between model performance and the kinds of species distributions being modelled. Some methods performed generally better, but no method was superior in all circumstances. A suggestion is made that choice of the appropriate method should be contingent on the goals and kinds of distributions being modelled.     

An integrated,spatio‐temporal modelling framework for analysing biological invasions

Aim

We develop a novel modelling framework for analysing the spatio‐temporal spread of biological invasions. The framework integrates different invasion drivers and disentangles their roles in determining observed invasion patterns by fitting models to historical distribution data. As a case study application, we analyse the spread of common ragweed (Ambrosia artemisiifolia).

Location

Central Europe.

Methods

A lattice system represents actual landscapes with environmental heterogeneity. Modelling covers the spatio‐temporal invasion sequence in this grid and integrates the effects of environmental conditions on local invasion suitability, the role of invaded cells and spatially implicit “background” introductions as propagule sources, within‐cell invasion level bulk‐up and multiple dispersal means. A modular framework design facilitates flexible numerical representation of the modelled invasion processes and customization of the model complexity. We used the framework to build and contrast increasingly complex models, and fitted them using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC).

Results

All modelled invasion drivers codetermined the A. artemisiifolia invasion pattern. Inferences about individual drivers depended on which processes were modelled concurrently, and hence changed both quantitatively and qualitatively between models. Among others, the roles of environmental variables were assessed substantially differently subject to whether models included explicit source‐recipient cell relationships, spatio‐temporal variability in source cell strength and human‐mediated dispersal means. The largest fit improvements were found by integrating filtering effects of the environment and spatio‐temporal availability of propagule sources.

Main conclusions

Our modelling framework provides a straightforward means to build integrated invasion models and address hypotheses about the roles and mutual relationships of different putative invasion drivers. Its statistical nature and generic design make it suitable for studying many observed invasions. For efficient invasion modelling, it is important to represent changes in spatio‐temporal propagule supply by explicitly tracking the species’ colonization sequence and establishment of new populations.

     

Behavioral aspects of biological invasions of alien fish species

The role of the behavior of animals determining the success of invasion to new areas and habitats is analyzed. The majority of examples are related to fish whose distribution attracts increasing attention from both scientists and persons involved in applied fisheries. The contribution of migratory, feeding, defensive, reproductive, and social behavior in “the invasive potential” of the species is considered. The success of the colonization of new biotopes by the species and expansion of its area may not depend so much on its properties as the ability to cover long distances, plasticity in mastering new resources, and stability to pressure from new competitors, predators, and parasites. Some properties of animals are stressed whose role in invasion may be important and is still to be demonstrated. Among them, a special interest for behaviorists is the ability of animals towards learning and towards “innovation activity”, the ability to switch rapidly from a cooperative mode of life to an individual mode of life, and to efficiently explore and to exploit heterogeneous microhabitats.     

Modelling biological invasions: species traits,species interactions,and habitat heterogeneity

In this paper we explore the integration of different factors to understand, predict and control ecological invasions, through a general cellular automaton model especially developed. The model includes life history traits of several species in a modular structure interacting multiple cellular automata. We performed simulations using field values corresponding to the exotic Gleditsia triacanthos and native co-dominant trees in a montane area. Presence of G. triacanthos juvenile bank was a determinant condition for invasion success. Main parameters influencing invasion velocity were mean seed dispersal distance and minimum reproductive age. Seed production had a small influence on the invasion velocity. Velocities predicted by the model agreed well with estimations from field data. Values of population density predicted matched field values closely. The modular structure of the model, the explicit interaction between the invader and the native species, and the simplicity of parameters and transition rules are novel features of the model.     

A comparison of statistical approaches for modelling fish species distributions

SUMMARY 1. The prediction of species distributions is of primary importance in ecology and conservation biology. Statistical models play an important role in this regard; however, researchers have little guidance when choosing between competing methodologies because few comparative studies have been conducted. 2. We provide a comprehensive comparison of traditional and alternative techniques for predicting species distributions using logistic regression analysis, linear discriminant analysis, classification trees and artificial neural networks to model: (1) the presence/absence of 27 fish species as a function of habitat conditions in 286 temperate lakes located in south‐central Ontario, Canada and (2) simulated data sets exhibiting deterministic, linear and non‐linear species response curves. 3. Detailed evaluation of model predictive power showed that approaches produced species models that differed in overall correct classification, specificity (i.e. ability to correctly predict species absence) and sensitivity (i.e. ability to correctly predict speciespresence) and in terms of which of the study lakes they correctly classified. Onaverage, neural networks outperformed the other modelling approaches, although all approaches predicted species presence/absence with moderate to excellent success. 4. Based on simulated non‐linear data, classification trees and neural networks greatly outperformed traditional approaches, whereas all approaches exhibited similar correct classification rates when modelling simulated linear data. 5. Detailed evaluation of model explanatory insight showed that the relative importance of the habitat variables in the species models varied among the approaches, where habitat variable importance was similar among approaches for some species and very different for others. 6. In general, differences in predictive power (both correct classification rate and identity of the lakes correctly classified) among the approaches corresponded with differences in habitat variable importance, suggesting that non‐linear modelling approaches (i.e. classification trees and neural networks) are better able to capture and model complex, non‐linear patterns found in ecological data. The results from the comparisons using simulated data further support this notion. 7. By employing parallel modelling approaches with the same set of data and focusing on comparing multiple metrics of predictive performance, researchers can begin to choose predictive models that not only provide the greatest predictive power, but also best fit the proposed application.     

Searching for phylogenetic pattern in biological invasions

It has been suggested that alien species with close indigenous relatives in the introduced range may have reduced chances of successful establishment and invasion (Darwin's naturalization hypothesis). Studies trying to test this have in fact been addressing four different hypotheses, and the same data can support some while rejecting others. In this paper, we argue that the phylogenetic pattern will change depending on the spatial and phylogenetic scales considered. Expectations and observations from invasion biology and the study of natural communities are that at the spatial scale relevant to competitive interactions, closely related species will be spatially separated, whereas at the regional scale, species in the same genera or families will tend to co-occur more often than by chance. We also argue that patterns in the relatedness of indigenous and naturalized plants are dependent on the continental/island setting, spatial occupancy levels, and on the group of organisms under scrutiny. Understanding how these factors create a phylogenetic pattern in invasions will help us predict which groups are more likely to invade where, and should contribute to general ecological theory.     

Chemical novelty facilitates herbivore resistance and biological invasions in some introduced plant species

Ecological release from herbivory due to chemical novelty is commonly predicted to facilitate biological invasions by plants, but has not been tested on a community scale. We used metabolomics based on mass spectrometry molecular networks to assess the novelty of foliar secondary chemistry of 15 invasive plant species compared to 46 native species at a site in eastern North America. Locally, invasive species were more chemically distinctive than natives. Among the 15 invasive species, the more chemically distinct were less preferred by insect herbivores and less browsed by deer. Finally, an assessment of invasion frequency in 2,505 forest plots in the Atlantic coastal plain revealed that, regionally, invasive species that were less preferred by insect herbivores, less browsed by white‐tailed deer, and chemically distinct relative to the native plant community occurred more frequently in survey plots. Our results suggest that chemically mediated release from herbivores contributes to many successful invasions.     

Filling in the gaps: modelling native species richness and invasions using spatially incomplete data

Detailed knowledge of patterns of native species richness, an important component of biodiversity, and non-native species invasions is often lacking even though this knowledge is essential to conservation efforts. However, we cannot afford to wait for complete information on the distribution and abundance of native and harmful invasive species. Using information from counties well surveyed for plants across the USA, we developed models to fill data gaps in poorly surveyed areas by estimating the density (number of species km⁻²) of native and non-native plant species. Here, we show that native plant species density is non-random, predictable, and is the best predictor of non-native plant species density. We found that eastern agricultural sites and coastal areas are among the most invaded in terms of non-native plant species densities, and that the central USA appears to have the greatest ratio of non-native to native species. These large-scale models could also be applied to smaller spatial scales or other taxa to set priorities for conservation and invasion mitigation, prevention, and control efforts.     

Evidence for non-uniform dispersal in the biological invasions of two naturalized North American bird species

Most previous attempts to model the geographical range expansion of an invading species assume random dispersal of organisms through a homogeneous environment. These models result in a series of uniformly increasing circles radiating out from the centre of origin over time. Although these models often give reasonable fits to available data, they do not typically include mechanisms of dispersal. Alternatively, models that include assumptions of non‐random dispersal and a heterogeneous environment inevitably result in an anisotropic or jagged invasion front. This front will include propagules of pioneer individuals for the expanding species. Existing data from biological invasions reveal that the spatial structure of an invading species usually exhibits these propagules. Using population data gathered from the past century, we investigated the propagules of two North American invading bird species: the European starling (Sturnus vulgaris Linnaeus), and the house finch (Carpodacus mexicanus Müller), and found a correlation between propagule location and habitat quality. These results suggest that dispersing individuals seek out favourable habitat and remain there, thus introducing a possible mechanism for explaining non‐uniform dispersal during invasions. When combined with results from other studies, our results suggest that propagules provide starting points for future population expansion of an invading species.     

Allee effects in biological invasions

Understanding the dynamics of small populations is obviously important for declining or rare species but is also particularly important for invading species. The Allee effect, where fitness is reduced when conspecific density is low, can dramatically affect the dynamics of biological invasions. Here, we summarize the literature of Allee effects in biological invasions, revealing an extensive theory of the consequences of the Allee effect in invading species and some empirical support for the theory. Allee effects cause longer lag times, slower spread and decreased establishment likelihood of invasive species. Expected spatial ranges, distributions and patterns of species may be altered when an Allee effect is present. We examine how the theory can and has been used to detect Allee effects in invasive species and we discuss how the presence of an Allee effect and its successful or unsuccessful detection may affect management of invasives. The Allee effect has been shown to change optimal control decisions, costs of control and the estimation of the risk posed by potentially invasive species. Numerous ways in which the Allee effect can influence the efficacy of biological control are discussed.     

Global change and biological invasions

Invasion by exotic species is one of the serious socio-economic, environmental and ecological problems currently faced by mankind. Biological invasions have changed the species composition, structure and function of ecosystems, and are seriously threatening global biodiversity, economy and human health (Iqbal et al. 2021; Wang et al. 2020; Yang et al. 2021; Zhao et al. 2020; Zheng et al. 2015). Biological invasions have resulted in an economic loss of at least US$ 1.288 trillion over the past few decades worldwide (Diagne et al. 2021). As a consequence of these far-reaching impacts, biological invasions have become a hot research topic in modern ecology, and attract major attention from international organizations, governments and scientists all over the world. There is a complex interaction between biological invasions and global environmental change. Biological invasions are not only passengers of global change, but can also be major drivers of global change (MacDougall and Turkington 2005). Other components of global change, such as atmospheric CO₂ enrichment, global warming, nitrogen deposition, changes in precipitation regimes, habitat fragmentation and land-use change, affect species distributions and resource dynamics of ecosystems, and consequently drive invasion success of many exotic species. On the other hand, invasion by exotic species can also alter basic ecosystem properties, which in turn affect many components of global change. Research on the patterns, processes and mechanisms of biological invasion can shed light on the drivers and consequences of biological invasions in the light of global change, and serve as a scientific basis for forward-thinking management plans. The overarching challenge is to understand the basic ecological interactions of, e.g., invasive and native species, plants and soil, and plants and animals.     

Cryptic species, native populations and biological invasions by a eucalypt forest pathogen

Human‐associated introduction of pathogens and consequent invasions is very evident in areas where no related organisms existed before. In areas where related but distinct populations or closely related cryptic species already exist, the invasion process is much harder to unravel. In this study, the population structure of the Eucalyptus leaf pathogen Teratosphaeria nubilosa was studied within its native range in Australia, including both commercial plantations and native forests. A collection of 521 isolates from across its distribution was characterized using eight microsatellite loci, resulting in 112 multilocus haplotypes (MLHs). Multivariate and Bayesian analyses of the population conducted in structure revealed three genetically isolated groups (A, B and C), with no evidence for recombination or hybridization among groups, even when they co‐occur in the same plantation. DNA sequence data of the ITS (n = 32), β‐tubulin (n = 32) and 27 anonymous loci (n = 16) were consistent with microsatellite data in suggesting that T. nubilosa should be considered as a species complex. Patterns of genetic diversity provided evidence of biological invasions by the pathogen within Australia in the states of Western Australia and New South Wales and helped unravel the pattern of invasion beyond Australia into New Zealand, Brazil and Uruguay. No significant genetic differences in pathogen populations collected in native forests and commercial plantations were observed. This emphasizes the importance of sanitation in the acquisition of nursery stock for the establishment of commercial plantations.     

Exploiting Allee effects for managing biological invasions

Biological invasions are a global and increasing threat to the function and diversity of ecosystems. Allee effects (positive density dependence) have been shown to play an important role in the establishment and spread of non-native species. Although Allee effects can be considered a bane in conservation efforts, they can be a benefit in attempts to manage non-native species. Many biological invaders are subject to some form of an Allee effect, whether due to a need to locate mates, cooperatively feed or reproduce or avoid becoming a meal, yet attempts to highlight the specific exploitation of Allee effects in biological invasions are surprisingly unprecedented. In this review, we highlight current strategies that effectively exploit an Allee effect, and propose novel means by which Allee effects can be manipulated to the detriment of biological invaders. We also illustrate how the concept of Allee effects can be integral in risk assessments and in the prioritization of resources allocated to manage non-native species, as some species beset by strong Allee effects could be less successful as invaders. We describe how tactics that strengthen an existing Allee effect or create new ones could be used to manage biological invasions more effectively.     

A proposed unified framework for biological invasions

There has been a dramatic growth in research on biological invasions over the past 20 years, but a mature understanding of the field has been hampered because invasion biologists concerned with different taxa and different environments have largely adopted different model frameworks for the invasion process, resulting in a confusing range of concepts, terms and definitions. In this review, we propose a unified framework for biological invasions that reconciles and integrates the key features of the most commonly used invasion frameworks into a single conceptual model that can be applied to all human-mediated invasions. The unified framework combines previous stage-based and barrier models, and provides a terminology and categorisation for populations at different points in the invasion process.     

The importance of biotic interactions for modelling species distributions under climate change

Aim There is a debate as to whether biotic interactions exert a dominant role in governing species distributions at macroecological scales. The prevailing idea is that climate is the key limiting factor; thus models that use present‐day climate–species range relationships are expected to provide reasonable means to quantify the impacts of climate change on species distributions. However, there is little empirical evidence that biotic interactions would not constrain species distributions at macroecological scales. We examine this idea, for the first time, and provide tests for two null hypotheses: (H₀ 1) – biotic interactions do not exert a significant role in explaining current distributions of a particular species of butterfly (clouded Apollo, Parnassius mnemosyne) in Europe; and (H₀ 2) – biotic interactions do not exert a significant role in predictions of altered species’ ranges under climate change. Location Europe. Methods Generalized additive modelling (GAM) was used to investigate relationships between species and climate; species and host plants; and species and climate + host plants. Because models are sensitive to the variable selection strategies utilised, four alternative approaches were used: AIC (Akaike's Information Criterion), BIC (Bayesian Information Criterion), BRUTO (Adaptive Backfitting), and CROSS (Cross Selection). Results In spite of the variation in the variables selected with different methods, both hypotheses (H₀ 1 and H₀ 2) were falsified, providing support for the proposition that biotic interactions significantly affect both the explanatory and predictive power of bioclimatic envelope models at macro scales. Main conclusions Our results contradict the widely held view that the effects of biotic interactions on individual species distributions are not discernible at macroecological scales. Results are contingent on the species, type of interaction and methods considered, but they call for more stringent evidence in support of the idea that purely climate‐based modelling would be sufficient to quantify the impacts of climate change on species distributions.