Developing environmental flow recommendations for freshwater mussels using the biological traits of species guilds

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Comment on 'The nature of the fluid boundary layer and the selection of parameters for benthic ecology' (Carling, 1992)

• 1 Near-bed flow equations and definitions have been reviewed by Carling (1992). The main errors and ambiguities in this contribution are identified.
• 2 The hypotheses required to derive the governing equations are usually violated in complex flows of natural streams.

     

Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale

1. Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large‐scale insect identification projects are time‐consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.
2. Here, we outline a methodology for training classification models to identify pitfall trap‐collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single‐level) identifying ground beetles from the species to subfamily level. All models were trained using pre‐extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.
3. The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single‐level classification method at higher taxonomic levels.
4. The general methodology outlined here serves as a proof‐of‐concept for classifying pitfall trap‐collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large‐scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.

     

Field techniques for the classification of near-bed flow regimes

• 1 The equipment and methods needed for collecting the data required for near-bed flow regime classification are detailed.
• 2 To illustrate appropriate procedures for data analysis, data collected from four cobble bed streams are used to classify near-bed flow regimes both at the measured depth and at estimated bankfull depth.
• 3 Implications of the resulting classifications are discussed from a limnological perspective. It is concluded that while the biological relevance of near-bed flow regimes has not been formally established, the terms ‘pool’, ‘run’, and ‘riffle’ are not always useful for differentiating between habitats in the near-bed zone.

     

Environmental drivers of macroinvertebrate communities in high Arctic rivers (Svalbard)

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Juvenile growth of a macrohabitat generalist tied to hydrological character of large‐river system

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Runoff and the longitudinal distribution of macroinvertebrates in a glacier‐fed stream: implications for the effects of global warming

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Models of the saccadic eye movement control system

1. A sequence of four models is proposed for the saccadic eye movement control system. The models become increasingly complex as they are made to respond to increasingly more complicated target movements in accordance with experimental results. Compatibility with neurological structure and function is stressed in the formation of the models. In each case, the elements of the models are constructed to conform as closely as possible to neuroanatomical structures and behave in a way that has been established or suggested by neurophysiology.
2. The dynamic behavior of the mechanics of the extraocular muscles and eyeball suspensory tissues has been established by recording from oculomotoneurons in alert monkeys. The transfer function of this mechanical system is used in these models.
3. Recent experiments on the neural circuits in the brain stem that are responsible for saccadic eye movements suggest an arrangement of the premotor circuitry that contains two principal neural networks; an integrator and a pulse generator. This circuitry is used in the models.
4. When the above modifications are made to existing models of the saccadic system, they remove the necessity of supposing that the visual information is sampled by the nervous system. The models do not include a sampler although the saccadic pulse generator still makes the overall system behavior similar to that of a sampled-data system.
5. The basic model is modified to make its behavior agree with experimental eye movement responses to target ramps and step-ramps. This is done by using error and its rate of change to estimate the error that will exist one reaction time in the future.
6. Parallel processing of data is a well recognized property of the nervous system. By utilizing it in combination with a random decision threshold, the model is extended to produce results in agreement with experiments for double-step target movements in which the second step occurs less than 0.2 sec after the first.
7. Finally, a model is presented which incorporates a continuum of parallel processing to represent the retinotopic spatial organization of the visual system and the tecto-bulbar motor commands. The model is conceptual; it was not constructed or tested but is used to discuss more complex eye movement phenomena such as those that appear to occur when the decision process must shift between hemispheres and how the system might produce quick correcting saccades with latencies as short as 85 msec.

     

New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys

1. Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.
2. We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys.
3. The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods.
4. The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.

     

Using fish guilds to assess community responses to temperature and flow regimes in unregulated and regulated Canadian rivers

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Kinetic analysis of the michaelis-menten mechanism in which the substrate and the product are unstable

• 1.1. A kinetic analysis of the Michaelis-Menten mechanism for the case in which both the substrate and the product are unstable, either spontaneously or as the result of the addition of a reagent, has been made.
• 2.2. The explicit time course equations of the immediate product and the species into which it subsequently is transformed have been derived under the conditions of rapid equilibrium and limiting substrate concentration.
• 3.3. The validity of these equations has been checked using numerical simulations.
• 4.4. The kinetic data analysis which we suggest is based on the time progress curves of the product or, in the case in which the product accumulation cannot be monitored experimentally, on the time progress curve of the species into which the immediate product is transformed.
• 5.5. This analysis allows the determination of the rate and the equilibrium constants if adequate experimental results are available.
• 6.6. We have chosen a numerical example, with which we illustrate the procedure of the kinetic data analysis by simulating some curves with assumed experimental errors.

     

Limited transferability of stream‐fish distribution models among river catchments: reasons and implications

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Probing whole‐stream metabolism: influence of spatial heterogeneity on rate estimates

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Geographical distribution modelling of the bronze bug: a worldwide invasion

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Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space

• 1 Advances in dynamic ecosystem modelling have made a number of different approaches to vegetation dynamics possible. Here we compare two models representing contrasting degrees of abstraction of the processes governing dynamics in real vegetation.
• 2 Model (a) (GUESS) simulates explicitly growth and competition among individual plants. Differences in crown structure (height, depth, area and LAI) influence relative light uptake by neighbours. Assimilated carbon is allocated individually by each plant to its leaf, fine root and sapwood tissues. Carbon allocation and turnover of sapwood to heartwood in turn govern height and diameter growth.
• 3 Model (b) (LPJ) incorporates a ‘dynamic global vegetation model’ (DGVM) architecture, simulating growth of populations of plant functional types (PFTs) over a grid cell, integrating individual‐level processes over the proportional area (foliar projective cover, FPC) occupied by each PFT. Individual plants are not simulated, but are replaced by explicit parameterizations of their growth and interactions.
• 4 The models are identical in their representation of core physiological and biogeochemical processes. Both also use the same set of PFTs, corresponding to the major woody plant groups in Europe, plus a grass type.
• 5 When applied at a range of locations, broadly spanning climatic variation within Europe, both models successfully predicted PFT composition and succession within modern natural vegetation. However, the individual‐based model performed better in areas where deciduous and evergreen types coincide, and in areas subject to pronounced seasonal water deficits, which would tend to favour grasses over drought‐intolerant trees.
• 6 Differences in model performance could be traced to their treatment of individual‐level processes, in particular light competition and stress‐induced mortality.
• 7 Our results suggest that an explicit individual‐based approach to vegetation dynamics may be an advantage in modelling of ecosystem structure and function at the resolution required for regional‐ to continental‐scale studies.

     

Dams and canyons disrupt gene flow among populations of a threatened riparian plant

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Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies

• 1 An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides‐only approach.
• 2 Mathematical modelling using differential equations has been used in theoretical studies of host–parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction.
• 3 The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3‐year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data.
• 4 The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall.
• 5 The ANN provides a powerful tool for predicting host–parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies.

     

Bird Conservation: The Science and the Action Report on the BOU Joint Conference, 6–10 April 1994

• 1 Birds are hugely popular and the public demands their conservation.
• 2 Ornithology has made a major contribution to nature conservation by virtue of this popular support. The value of birds as environmental indicators has been greatly enhanced by voluntary data collection on a wide scale over many years.
• 3 Habitat loss and degradation are the main causes of species decline, even though other factors may contribute to extinction. More research should address the causes of decline at an early stage, while the chance of recovery is highest.
• 4 The geographical ranges of native bird species should be maintained, both to avoid the risk of local or wider extinction and to enable people to enjoy them as part of their normal experience.
• 5 To maintain species ranges. conservation must be incorporated in policies affecting the wider countryside and the sea. This is as important as managing protected areas.
• 6 The management of protected areas can only be successful in the context of sympathetic management of the surrounding countryside.
• 7 The Biodiversity Convention requires countries to produce national conservation plans and strategies. This offers ornithologists an unprecedented opportunity to contribute to conservation by developing explicit objectives and specific targets for the maintenance (or restoration) of numbers and distributions of species, and of extent and quality of habitats. Targets should be ambitious but realistic and be sufficiently precise as to be testable.
• 8 Predictive models have the potential to support conservation advice, but traditional natural history studies have proved vital in the past and theory could not replace them.
• 9 Detailed ecological research with long data runs is the ideal basis for conservation action. But urgency demands shorter studies, informed by ecological intuition and knowledge, and reaching specific recommendations for action.
• 10 Conservation actions should be treated as experiments so that techniques can be improved progressively. This applies both to the management of nature reserves and to habitat management stemming from broader policy measures, for example in Environmentally Sensitive Areas.
• 11 Monitoring across a wide species base is essential because the threats to wildlife are unpredictable. Birds have proven to be successful indicators because they are highly visible: are enthusiastically counted by volunteers: and respond to a wide variety of environmental impacts.
• 12 Threshold levels, indicating the normal upper and lower levels of variation (for instance in numbers or breeding success), are needed in order to trigger prompt remedial action.
• 13 Monitoring, research and consenration action must be taken forward internationally. Integrated and common approaches enable exchange of data and inforniation. and reinforce national actions across the range.
• 14 Fxisting data need to be made more accessible by greater collaboration and openness, and by the use of computerization.
• 15 Ornithologists need to build stronger partnerships, both with other biologists and with decision-makers across the range of land-use and economic policy. This will be helped by better communication built on clear but simple messages for non-biologists.
• 16 The training of future ecologists should take account of the wide range of skills required by the expanding discipline of conservation.

     

Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

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