How Reliable is the Untrained Eye in the Identification of an Invasive Species? The Case of Alien Bee-Hawking Yellow-Legged Hornet in Iberian Peninsula

Biological invasions cause great damage to native ecosystems, therefore, it is extremely important to take measures to contain the progress of existing invasions and prevent new ones. Here, we used the Species Distribution Models approach to compare two independent datasets for the invasive alien species the Yellow-legged hornet in the Iberian Peninsula. One dataset compiles occurrence records gathered by expert people (e.g. environmental services’ technical staff and researchers); and the other compiles occurrence records gathered by non-expert people (e.g. amateur entomologists, beekeepers). The main aim is to assess the effectiveness and reliability of the dataset managed by non-experts when comparing it to the dataset managed by experts. Our results showed a high degree of concordance and similarity between models. Thus, both datasets would have the same reliability to be used in management strategies for this species.     

Testing Three Species Distribution Modelling Strategies to Define Fish Assemblage Reference Conditions for Stream Bioassessment and Related Applications

Species distribution models are widely used for stream bioassessment, estimating changes in habitat suitability and identifying conservation priorities. We tested the accuracy of three modelling strategies (single species ensemble, multi-species response and community classification models) to predict fish assemblages at reference stream segments in coastal subtropical Australia. We aimed to evaluate each modelling strategy for consistency of predictor variable selection; determine which strategy is most suitable for stream bioassessment using fish indicators; and appraise which strategies best match other stream management applications. Five models, one single species ensemble, two multi-species response and two community classification models, were calibrated using fish species presence-absence data from 103 reference sites. Models were evaluated for generality and transferability through space and time using four external reference site datasets. Elevation and catchment slope were consistently identified as key correlates of fish assemblage composition among models. The community classification models had high omission error rates and contributed fewer taxa to the ‘expected’ component of the taxonomic completeness (O/E₅₀) index than the other strategies. This potentially decreases the model sensitivity for site impact assessment. The ensemble model accurately and precisely modelled O/E₅₀ for the training data, but produced biased predictions for the external datasets. The multi-species response models afforded relatively high accuracy and precision coupled with low bias across external datasets and had lower taxa omission rates than the community classification models. They inherently included rare, but predictable species while excluding species that were poorly modelled among all strategies. We suggest that the multi-species response modelling strategy is most suited to bioassessment using freshwater fish assemblages in our study area. At the species level, the ensemble model exhibited high sensitivity without reductions in specificity, relative to the other models. We suggest that this strategy is well suited to other non-bioassessment stream management applications, e.g., identifying priority areas for species conservation.     

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change--particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km(2) study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.     

The Intersection of Independent Lies: Increasing Realism in Ecological Risk Assessment

In 1966, Levins presented a philosophical discussion on making inference about populations using clusters of models. In this article we provide an overview of model inference in ecological risk assessment, discuss the benefits and trade-offs of increasing model realism, show the similarities and differences between Levins' model clusters and those used in ecological risk assessment, and present how risk assessment models can incorporate Levins' ideas of truth through independent lies. Two aspects of Levins' philosophy are directly relevant to risk assessment. First, confidence in our interpretation of risk is increased when multiple risk assessments yield similar qualitative results. Second, model clusters should be evaluated to determine if they maximize precision, generality, or realism or a mix of the three. In the later case, the evaluation of each model will differ depending on whether it is more general, precise, or realistic relative to the other models used. We conclude that risk assessments can be strengthened using Levins' idea, but that Levins' caution that model outcome should not be mistaken for truth is still applicable.     

The effects of climate data precision on fitting and projecting species niche models

Biosecurity agencies are particularly concerned to know the potential distribution of invasive alien species under present, and to a lesser extent, future climates; expensive decisions can hinge upon the degree of perceived threat a pest species poses. Climate‐based niche modelling techniques are available to inform these decisions. These tools now regularly employ gridded climate datasets of moderate spatial resolution (0.5 degree), though biosecurity decision‐makers continually seek greater spatial precision in the risk map products. Various splining techniques are capable of generating gridded climate datasets approaching the precision limits imposed by the availability of digital elevation model data. As the spatial precision of climate datasets increases, more detailed effects of topographic relief become apparent in the climatic data. When these datasets are used to develop and apply species niche models, the climate data is spatially intersected with species location data to infer relationships between the climate and the species’ geographic distribution. Here we investigate the effect of changing climate precision on projections of species’ niche models developed with CLIMEX, including the effect of upscaling and downscaling the outputs. We found that there were noticeable increases in sensitivity in models developed using more precise climate datasets. The largest differences in projections were noted where species range limits coincided with regions of strong climatic gradients such as where there was marked topographic relief in relation to the spatial precision of the climatic dataset. Upscaling (fitting a model with a fine resolution dataset and then projecting the results with a coarser grid), tended to produce smaller potential ranges for a species, albeit at the cost of model sensitivity. Downscaling had the opposite effect, identifying additional, mostly marginally climatically suitable habitat. It remains unclear how sensitive the fine resolution results are to the number and spatial arrangement of input location records used to build the model. The results indicate some benefits of improving the spatial resolution of climate datasets, though not at the expense of climatic data accuracy. Decision‐makers should be mindful of the inherent uncertainties in these models, and modellers have a responsibility to identify and convey these uncertainties to their intended audience.     

Accounting for spatial autocorrelation from model selection to statistical inference: Application to a national survey of a diurnal raptor

Planning actions for species conservation involves working at both an ecologically meaningful spatial scale and a scale suitable for implementing management or conservation plans. Animal populations and conservation policies often operate across wide areas. Large-extent spatial datasets are thus often used, but their analyses rarely deal with problems inherent to spatial datasets such as residual spatial autocorrelation, which can bias or even reverse results. Here we propose a procedure for analysing a large-scale count dataset integrating residual spatial autocorrelation in a Generalized Linear Model framework by combining and extending previously published methods. The first step concerns the selection of the environmental variables by a modified cross-validation procedure allowing for residual spatial autocorrelation. Then the second step consists in evaluating the spatial effect of the model using a spatial filtering approach based on the variogram parameters. We apply this method to the Black kite (Milvus migrans) to estimate the distribution and population size of this species in France. We found some divergence in estimated population size between spatial and non spatial models, as well as in the distribution map. We also found that the uncertainty of the model was underestimated by the residual spatial autocorrelation. Our analysis confirms previous results, that residual spatial autocorrelation should be always accounted for, especially in conservation where false results may lead to poor management decisions.     

Non-intrusively estimating the live body biomass of Pintado Real® fingerlings: A feature selection approach

Aquaculture has undergone significant technological advances in recent decades, which has enabled the expansion of fish protein production worldwide. However, some conventional processes in fish farming facilities, such as the weighing of fingerlings, usually occur manually and laboriously, which can cause physical damage to the fingerlings, precision balance errors, and financial losses for fish farmers. The main contribution of this research is the creation of an automatic weighing application for fingerlings and juveniles, in contrast to the laborious manual weighing that occurs particularly in small and medium aquaculture facilities. This paper presents results for the prediction of biomass of moving live fingerlings using supervised learning algorithms. It is applied in two new datasets, the first with illumination and the second without illumination. For both datasets, the images are pre-processed and segmented to extract the characteristic vectors systematically trained with cross-validation by four regression algorithms. So, the selection of attributes was performed based on correlation and relative importance which allowed the removal of some attributes which implied null significance for the model. The best result was for the experiment with attribute and frame selection applied to the lighting dataset and the Linear Regressor obtained a R² = 0.76 and MAE = 0.83 g. The proposed model shows as promising in comparison with other approaches in the literature.     

Data-driven habitat analysis of the Ponto-Caspian amphipod Dikerogammarus villosus in two invaded regions in Europe

When forecasting invasions, models built on a dataset from a certain region often have to be used for simulations in another geographic region. Results on the reliability and usefulness of such models are missing in literature. The present study compares habitat suitability models for the invasive amphipod species Dikerogammarus villosus developed based on data gathered in recently invaded rivers and channels in Flanders (Belgium), with similar models developed on the basis of long-term colonised systems in Croatia. The models were tested on their reliability in both regions. Two techniques, logistic regressions (LR) and classification trees (CT) were used to analyse the habitat preference of this species based on physical–chemical and morphological habitat characteristics. It was found that in Flanders, D. villosus prefers rivers with a non-natural bank structure, high oxygen saturation, low conductivity and good chemical water quality, which could be related to its distribution in large rivers and canals. In Croatian rivers, high oxygen saturation was the most important prerequisite for the species to be present. Despite the longer history of invasion in Croatia, the species seemed to have similar habitat preferences in both invaded regions. Both data-driven approaches yielded similar results, but CT performed somewhat better based on the used performance criteria (% Correctly Classified Instances, Kappa and Area Under Curve) and were easier to interpret compared to the LR. The CT models developed based on the data of Flanders performed moderately when applying on the data of Croatia, but had a lower performance when applied vice versa. The LR models did not perform well when applying on a dataset of another geographic area. Extrapolation of the logistic regression model seemed to be more difficult compared to classification tree models. Our results indicate that it is possible to determine the habitat preference of an invasive species and that these models could be applied to other regions in Europe in order to take preventive measures to control the further spread of invasive species. However, a major concern is that the models are developed based on a representative range of all relevant variables reflecting the stream conditions and that accurate data are important.     

Fish Invasions in the World's River Systems: When Natural Processes Are Blurred by Human Activities

Because species invasions are a principal driver of the human-induced biodiversity crisis, the identification of the major determinants of global invasions is a prerequisite for adopting sound conservation policies. Three major hypotheses, which are not necessarily mutually exclusive, have been proposed to explain the establishment of non-native species: the “human activity” hypothesis, which argues that human activities facilitate the establishment of non-native species by disturbing natural landscapes and by increasing propagule pressure; the “biotic resistance” hypothesis, predicting that species-rich communities will readily impede the establishment of non-native species; and the “biotic acceptance” hypothesis, predicting that environmentally suitable habitats for native species are also suitable for non-native species. We tested these hypotheses and report here a global map of fish invasions (i.e., the number of non-native fish species established per river basin) using an original worldwide dataset of freshwater fish occurrences, environmental variables, and human activity indicators for 1,055 river basins covering more than 80% of Earth's surface. First, we identified six major invasion hotspots where non-native species represent more than a quarter of the total number of species. According to the World Conservation Union, these areas are also characterised by the highest proportion of threatened fish species. Second, we show that the human activity indicators account for most of the global variation in non-native species richness, which is highly consistent with the “human activity” hypothesis. In contrast, our results do not provide support for either the “biotic acceptance” or the “biotic resistance” hypothesis. We show that the biogeography of fish invasions matches the geography of human impact at the global scale, which means that natural processes are blurred by human activities in driving fish invasions in the world's river systems. In view of our findings, we fear massive invasions in developing countries with a growing economy as already experienced in developed countries. Anticipating such potential biodiversity threats should therefore be a priority.     

Models of lake invasibility by Bythotrephes longimanus, a non-indigenous zooplankton

We built a family of hierarchical risk models for the spread of invasions by the spiny waterflea (Bythotrephes longimanus) in lakes in Ontario, Canada. Knowledge of covariates determining lake invasibility and ability to predict risk of future invasions may help to develop management policy and slow the invasions in the future. The models are based on two component submodels. The first component was a stochastic gravity submodel for the propagule pressure between lakes via recreational boaters. The second component was a submodel for establishment risk, given that the invader has already been introduced to a lake. This component was a logistic regression model, incorporating up to 17 measured covariates that describe the physical and chemical condition of the lake. Variants of the risk model, each incorporating different subsets of the covariates, were calibrated using presence/absence data from a 300-lake survey conducted in 2005?C2006 by the Canadian Aquatic Invasive Species Network (CAISN). The predictive capacity of the best model was high, giving AUC values close to 0.94. Of the model covariates considered, the most important predictors of existing invasions were propagule pressure and lake pH, and, to lesser extents, phosphorus (P) and lake elevation. Our fitting of the propagule pressure submodel demonstrated a significant Allee effect for Bythotrephes. Our development of the establishment risk predictor showed that it is essential to account for temporal variability in lake physico-chemistry. We demonstrated that invasions of lake networks by the spiny waterflea follow highly predictable patterns which can be understood with a properly calibrated, hierarchical risk model.     

Using Non-Reversible Context-Dependent Evolutionary Models to Study Substitution Patterns in Primate Non-Coding Sequences

We discuss the importance of non-reversible evolutionary models when analyzing context-dependence. Given the inherent non-reversible nature of the well-known CpG-methylation-deamination process in mammalian evolution, non-reversible context-dependent evolutionary models may be well able to accurately model such a process. In particular, the lack of constraints on non-reversible substitution models might allow for more accurate estimation of context-dependent substitution parameters. To demonstrate this, we have developed different time-homogeneous context-dependent evolutionary models to analyze a large genomic dataset of primate ancestral repeats based on existing independent evolutionary models. We have calculated the difference in model fit for each of these models using Bayes Factors obtained via thermodynamic integration. We find that non-reversible context-dependent models can drastically increase model fit when compared to independent models and this on two primate non-coding datasets. Further, we show that further improvements are possible by clustering similar parameters across contexts.     

Fish Invasions in the World's River Systems: When Natural Processes Are Blurred by Human Activities

Because species invasions are a principal driver of the human-induced biodiversity crisis, the identification of the major determinants of global invasions is a prerequisite for adopting sound conservation policies. Three major hypotheses, which are not necessarily mutually exclusive, have been proposed to explain the establishment of non-native species: the “human activity” hypothesis, which argues that human activities facilitate the establishment of non-native species by disturbing natural landscapes and by increasing propagule pressure; the “biotic resistance” hypothesis, predicting that species-rich communities will readily impede the establishment of non-native species; and the “biotic acceptance” hypothesis, predicting that environmentally suitable habitats for native species are also suitable for non-native species. We tested these hypotheses and report here a global map of fish invasions (i.e., the number of non-native fish species established per river basin) using an original worldwide dataset of freshwater fish occurrences, environmental variables, and human activity indicators for 1,055 river basins covering more than 80% of Earth's surface. First, we identified six major invasion hotspots where non-native species represent more than a quarter of the total number of species. According to the World Conservation Union, these areas are also characterised by the highest proportion of threatened fish species. Second, we show that the human activity indicators account for most of the global variation in non-native species richness, which is highly consistent with the “human activity” hypothesis. In contrast, our results do not provide support for either the “biotic acceptance” or the “biotic resistance” hypothesis. We show that the biogeography of fish invasions matches the geography of human impact at the global scale, which means that natural processes are blurred by human activities in driving fish invasions in the world's river systems. In view of our findings, we fear massive invasions in developing countries with a growing economy as already experienced in developed countries. Anticipating such potential biodiversity threats should therefore be a priority.     

<Emphasis Type="Italic">Drosophila</Emphasis> as models to understand the adaptive process during invasion

The last few decades have seen a growing number of species invasions globally, including many insect species. In drosophilids, there are several examples of successful invasions, i.e. Zaprionus indianus and Drosophila subobscura some decades ago, but the most recent and prominent example is the invasion of Europe and North America by the pest species, Drosophila suzukii. During the invasive process, species often encounter diverse environmental conditions that they must respond to, either through rapid genetic adaptive shifts or phenotypic plasticity, or by some combination of both. Consequently, invasive species constitute powerful models for investigating various questions related to the adaptive processes that underpin successful invasions. In this paper, we highlight how Drosophila have been and remain a valuable model group for understanding these underlying adaptive processes, and how they enable insight into key questions in invasion biology, including how quickly adaptive responses can occur when species are faced with new environmental conditions.     

Predicting local fish species richness in the garonne river basin

The aim of this work was to predict local fish species richness in the Garonne river basin using three environmental variables (distance from the source, elevation and catchment area J. Commonly, patterns of fish species richness have been investigated using simple or multi-linear statistical models. Here, we used backpropagation of artificial neural networks (ANNs) to develop stochastic models of local fish diversity. Two independent data collections were used, the first one to build and test the model; the second one to validate the model. Correlation coefficients between observed values and predicted values both in the testing and the validation procedures were highly significant (r = 0.904, P< 0.001 and r = 0.822, P< 0.001, respectively J. The ANN model obtained using only three environmental variables succeeded in explaining ca 70 % of the total variation in local fish species richness. Through these findings, ANNs can be seen as a powerful predictive tool compared to traditional modelling approaches.     

YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment

Over the last few years, several research works have been performed to monitor fish in the underwater environment aimed for marine research, understanding ocean geography, and primarily for sustainable fisheries. Automating fish identification is very helpful, considering the time and cost of the manual process. However, it can be challenging to differentiate fish from the seabed and fish types from each other due to environmental challenges like low illumination, complex background, high variation in luminosity, free movement of fish, and high diversity of fish species. In this paper, we propose YOLO-Fish, a deep learning based fish detection model. We have proposed two models, YOLO-Fish-1 and YOLO-Fish-2. YOLO-Fish-1 enhances YOLOv3 by fixing the issue of upsampling step sizes of to reduce the misdetection of tiny fish. YOLO-Fish-2 further improves the model by adding Spatial Pyramid Pooling to the first model to add the capability to detect fish appearance in those dynamic environments. To test the models, we introduce two datasets: DeepFish and OzFish. The DeepFish dataset contains around 15k bounding box annotations across 4505 images, where images belong to 20 different fish habitats. The OzFish is another dataset comprised of about 43k bounding box annotations of wide varieties of fish across around 1800 images. YOLO-Fish1 and YOLO-Fish2 achieved average precision of 76.56% and 75.70%, respectively for fish detection in unconstrained real-world marine environments, which is significantly better than YOLOv3. Both of these models are lightweight compared to recent versions of YOLO like YOLOv4, yet the performances are very similar.     

Evaluation of six process‐based forest growth models using eddy‐covariance measurements of CO2 and H2O fluxes at six forest sites in Europe

Reliable models are required to assess the impacts of climate change on forest ecosystems. Precise and independent data are essential to assess this accuracy. The flux measurements collected by the EUROFLUX project over a wide range of forest types and climatic regions in Europe allow a critical testing of the process‐based models which were developed in the LTEEF project. The ECOCRAFT project complements this with a wealth of independent plant physiological measurements. Thus, it was aimed in this study to test six process‐based forest growth models against the flux measurements of six European forest types, taking advantage of a large database with plant physiological parameters. The reliability of both the flux data and parameter values itself was not under discussion in this study. The data provided by the researchers of the EUROFLUX sites, possibly with local corrections, were used with a minor gap‐filling procedure to avoid the loss of many days with observations. The model performance is discussed based on their accuracy, generality and realism. Accuracy was evaluated based on the goodness‐of‐fit with observed values of daily net ecosystem exchange, gross primary production and ecosystem respiration (gC m^?2 d^?1), and transpiration (kg H₂O m^?2 d^?1). Moreover, accuracy was also evaluated based on systematic and unsystematic errors. Generality was characterized by the applicability of the models to different European forest ecosystems. Reality was evaluated by comparing the modelled and observed responses of gross primary production, ecosystem respiration to radiation and temperature. The results indicated that: Accuracy. All models showed similar high correlation with the measured carbon flux data, and also low systematic and unsystematic prediction errors at one or more sites of flux measurements. The results were similar in the case of several models when the water fluxes were considered. Most models fulfilled the criteria of sufficient accuracy for the ability to predict the carbon and water exchange between forests and the atmosphere. Generality. Three models of six could be applied for both deciduous and coniferous forests. Furthermore, four models were applied both for boreal and temperate conditions. However, no severe water‐limited conditions were encountered, and no year‐to‐year variability could be tested. Realism. Most models fulfil the criterion of realism that the relationships between the modelled phenomena (carbon and water exchange) and environment are described causally. Again several of the models were able to reproduce the responses of measurable variables such as gross primary production (GPP), ecosystem respiration and transpiration to environmental driving factors such as radiation and temperature. Stomatal conductance appears to be the most critical process causing differences in predicted fluxes of carbon and water between those models that accurately describe the annual totals of GPP, ecosystem respiration and transpiration. As a conclusion, several process‐based models are available that produce accurate estimates of carbon and water fluxes at several forest sites of Europe. This considerable accuracy fulfils one requirement of models to be able to predict the impacts of climate change on the carbon balance of European forests. However, the generality of the models should be further evaluated by expanding the range of testing over both time and space. In addition, differences in behaviour between models at the process level indicate requirement of further model testing, with special emphasis on modelling stomatal conductance realistically.     

Improved prediction of Canada lynx distribution through regional model transferability and data efficiency

The application of species distribution models (SDMs) to areas outside of where a model was created allows informed decisions across large spatial scales, yet transferability remains a challenge in ecological modeling. We examined how regional variation in animal‐environment relationships influenced model transferability for Canada lynx (Lynx canadensis), with an additional conservation aim of modeling lynx habitat across the northwestern United States. Simultaneously, we explored the effect of sample size from GPS data on SDM model performance and transferability. We used data from three geographically distinct Canada lynx populations in Washington (n = 17 individuals), Montana (n = 66), and Wyoming (n = 10) from 1996 to 2015. We assessed regional variation in lynx‐environment relationships between these three populations using principal components analysis (PCA). We used ensemble modeling to develop SDMs for each population and all populations combined and assessed model prediction and transferability for each model scenario using withheld data and an extensive independent dataset (n = 650). Finally, we examined GPS data efficiency by testing models created with sample sizes of 5%–100% of the original datasets. PCA results indicated some differences in environmental characteristics between populations; models created from individual populations showed differential transferability based on the populations'' similarity in PCA space. Despite population differences, a single model created from all populations performed as well, or better, than each individual population. Model performance was mostly insensitive to GPS sample size, with a plateau in predictive ability reached at ~30% of the total GPS dataset when initial sample size was large. Based on these results, we generated well‐validated spatial predictions of Canada lynx distribution across a large portion of the species'' southern range, with precipitation and temperature the primary environmental predictors in the model. We also demonstrated substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original.     

Quantifying the effects of temperature and flow regime on the abundance of 0+ cyprinids in the upper River Rhone using Bayesian hierarchical modelling

1. Assuming that recruitment variation is one of the main sources of fish population and assemblage changes, it is necessary to understand how natural variations in the environment influence 0+ fish abundance. Temperature regimes play an important role in enhancing both spawning activity and survival during early larval fish development. Flow regime variation, which is a powerful source of stream disturbance, is another factor to be taken into account. 2. Responses to these variables need to be assessed using long‐term datasets, since standard statistical approaches fail to provide a causal structure or to quantify the different effects. We therefore used a 26‐year dataset to evaluate the respective effects of seven derived independent variables describing the effects of temperature and flow regimes on the 0+ juvenile abundance of eight fish species in the River Rhone. 3. A clustering procedure using the Kendall tau rank correlation coefficient was implemented and identified three groups of fish according to their synchronic variations in juvenile abundance; i.e. varying with decreasing juvenile abundance, slightly increasing juvenile abundance and increasing juvenile abundance. These clusters provided the basis for building hierarchical log‐Poisson generalized linear models. The Bayesian paradigm gives a reliable framework for model selection, and the best model was determined using the Bayes Factor. 4. The posterior distribution of the regression parameters was coherent with what was expected based on knowledge of the biology of the different species. It indicates that temperature regime drives 0+ juvenile abundance but that flow regime also plays an important regulating role. The models thus detected evidence of the consequences of specific flow events such as larval drift and an increase in available habitat during higher flow discharges. 5. Our study illustrates the advantages of using a hierarchical modelling approach to quantify ecological effects by improving discrimination between the different sources of uncertainty, leading to better precision when estimating regression parameters.     

The influence of abundance on detectability

Plant and animal survey detection rates are important for ecological surveys, environmental impact assessment, invasive species monitoring, and modeling species distributions. Species can be difficult to detect when rare but, in general, how detection probabilities vary with abundance is unknown. We developed a new detectability model based on the time to detection of the first individual of a species. Based on this model, the predicted detection rate is proportional to a power function of abundance with a scaling exponent between zero and one that depends on clustering of individuals. We estimated the model parameters with data from three independent datasets: searches for chenopod shrub species and coins, experimental searches for planted seedlings, and frog surveys at multiple sites in sub‐tropical forests of eastern Australia. Analyses based on the detection time and detection probability suggest that detection rate increases with abundance as predicted. The model provides a way to scale detection rates to cases of low abundance when direct estimation of detection rates is often impractical.     

淡水鱼类入侵种的分布、入侵途径、机制与后果

生物入侵已经成为全球面临的三大环境问题之一。鱼类入侵现象也随全球经济一体化的进程日益严重。本文综述了全球淡水鱼类入侵的现状和研究进展, 包括鱼类入侵的定义及分布、入侵途径和机制、产生的生态和社会经济影响以及预防措施等。据统计, 目前全球外来鱼类达624种, 该数量超过30年前的两倍。外来鱼类主要通过水产养殖(51%)、观赏渔业(21%)、休闲垂钓(12%)、渔业捕捞运输(7%)等多种途径被引进。入侵鱼类对本地种产生了捕食、种内种间竞争、杂交和疾病传播等负面影响, 破坏本地生态系统, 但是其正面的生态及社会经济影响也不可忽略。近20年来全球鱼类入侵日益受到重视, 相关论文发表数量翻了8倍。值得提出的是, 近10年来全球鱼类入侵风险评价系统的研究显著增加, 一些鱼类入侵模型已应用于五大洲的多个国家。我国淡水外来鱼类共计439种。然而, 我国关于鱼类入侵的研究起步较晚, 发表文献数仅占全球的3.7%, 且主要研究方向仍集中在入侵物种的分布及生物学特性等基础研究上, 缺乏对于鱼类入侵机制及风险评价预测的研究。因此, 我们建议: (1)开展全国范围的本底调查并建立数据库, 实现数据共享, 明确鱼类入侵的历史与分布现状; (2)联合多个政府部门和机构, 对鱼类入侵进行长期观测, 从整个水生生态系统的角度出发, 深入了解其入侵机制及其产生的正面和负面生态和社会经济影响; (3)加强增殖放流的科学研究和管理; (4)构建区域性外来鱼类入侵风险评价系统, 有效预测鱼类入侵活动, 评价入侵种的危害, 并为相关政府部门的决策提供科学依据。