Morphological differentiation in introduced pumpkinseed Lepomis gibbosus (L.) occupying different habitat zones in Portuguese reservoirs

Introduced pumpkinseed Lepomis gibbosus sampled from four habitat zones (fluvial pelagic, fluvial littoral, lacustrine pelagic and lacustrine littoral) in three Portuguese reservoirs were used to test the hypotheses that habitats with the least similar characteristics will show the most differentiation, and that morphological differences will relate to functional adaptations to flow and trophic habitats. Results from discriminant function analysis and ANCOVA showed that there were significant differences in external morphology in pumpkinseed captured from the four habitat zones in all three reservoirs. Littoral and pelagic differentiation was stronger than fluvial and lacustrine differentiation in all of the reservoirs, and the most significant variable that differentiated pumpkinseed from the littoral and pelagic habitats was body depth. The illustration of external morphological differentiation in pumpkinseed along both habitat dimensions demonstrates the high degree of morphological plasticity of this introduced species.     

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Background

Osteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.

Results

In this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.

Conclusion

Compared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

     

Multi-scale habitat models for reintroduced bird populations: a case study of South Island saddlebacks on Motuara Island

Understanding resource selection by animals is important when considering habitat suitability at proposed release sites within threatened species recovery programmes. Multi-scale investigatory approaches are increasingly encouraged, as the patchy distribution of suitable habitats in fragmented landscapes often determines species presence and survival. Habitat models applied to a threatened New Zealand forest passerine, the South Island saddleback (Philesturnus carunculatus carunculatus), reintroduced to Ulva Island (Stewart Island) found that at landscape scale breeding pairs? preferences for sites near the coast were driven by micro-scale vegetation structure. We tested these results by examining models of breeding site selection by a reintroduced saddleback population on Motuara Island (Marlborough Sounds) at two scales: (1) micro-scale, for habitat characteristics that may drive breeding site selection, and (2) landscape scale, for variations in micro-scale habitat characteristics that may influence site colonisation in breeding pairs. Results indicated that birds on Motuara Island responded similarly to those on Ulva Island, i.e. birds primarily settled at the margins of coastal scrub and forest and later cohorts moved into larger stands of coastal forest where they established breeding territories. Plant species composition was also important in providing breeding saddleback pairs with adequate food supply and nesting support. However, Motuara Island birds differed in their partitioning of habitat use: preferred habitats were used for nesting while birds were foraging outside territorial boundaries or in shared sites. These differences may be explained because Motuara has a more homogeneous distribution of microscale habitats throughout the landscape and a highly bird-populated environment. These results show that resource distribution and abundance across the landscape needs to be accounted for in the modelling of density?bird?habitat relationships. In the search for future release sites, food (invertebrates and fruiting tree species) should be abundant close to available nesting sites, or evenly spread and available throughout the landscape.     

Habitat suitability modelling for species at risk is sensitive to algorithm and scale: A case study of Blanding's turtle,Emydoidea blandingii,in Ontario,Canada

Species distribution modelling (SDM) can help conservation by providing information on the ecological requirements of species at risk. We developed habitat suitability models at multiple spatial scales for a threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored the effect of background data selection and modelling algorithm selection on habitat suitability predictions. We used sighting records, high-resolution land cover data (25 m), and two SDM techniques: boosted regression trees; and maximum entropy modelling. The area under the receiver characteristic operating curve (AUC) for habitat suitability models tested on independent data ranged from 0.878 to 0.912 when using random background and from 0.727 to 0.741 with target-group background. E. blandingii habitat suitability was best predicted by air temperature, wetland area, open water area, road density, and cropland area. Habitat suitability increased with increasing air temperature and wetland area, and decreased with increasing cropland area. Low road density and open water increased habitat suitability, while high levels of either variable decreased habitat suitability. Robust habitat suitability maps for species at risk require using a multi-scale and multi-algorithm approach. If well used, SDM can offer insight on the habitat requirements of species at risk and help guide the development of management plans. Our results suggest that E. blandingii management plans should promote the protection of terrestrial habitat surrounding residential wetlands, halt the building of roads within and adjacent to currently occupied habitat, and identify movement corridors for isolated populations.     

Predicting the potential distribution of wheatear birds using stacked generalization-based ensembles

Habitat suitability models, usually referred to as species distribution models (SDMs), are widely applied in ecology for many purposes, including species conservation, habitat discovery, and gain evolutionary insights by estimating the distribution of species. Machine learning algorithms as well as statistical models have been recently used to predict the distribution of species. However, they seemed to have some limitations due to the data and the models used. Therefore, this study proposes a novel approach for assessing habitat suitability based on ensemble learning techniques. Three heterogeneous ensembles were built using the stacked generalization method to model the distribution of four wheatear species (Oenanthe deserti, Oenanthe leucopyga, Oenanthe leucura, and Oenanthe oenanthe) located in Morocco. Initially, a set of base-learners were constructed by virtue of training for each specie's dataset six machine learning algorithms (Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), K-nearest neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GB), and Random Forest (RF)). Then, the predictions of these base learners were fed as training data to train three meta-learners (Logistic Regression (LR), SVC, and MLP). To evaluate and assess the performance of the proposed approaches, we used: (1) six performance criteria (accuracy, recall, precision, F1-score, AUC, and TSS), (2) Borda Count (BC) ranking method based on multiple criteria to rank the best-performing models, and (3) Scott Knott (SK) test to statistically compare the performance of the presented models. The results based on the six-evaluation metrics showed that stacked ensembles outperformed their singles in all species datasets, and the stacked model with SVC as a meta-learner outperformed the other two ensembles. The results showed the potential of using ensemble learning techniques to model species distribution and recommend the use of the stacked generalization technique as a combination strategy since it gave better results compared to single models in four wheatear species datasets. Moreover, to assess the impact of future climate changes on the distribution of the four wheatear species, the best-performing distribution model was selected and projected into the current and future climatic conditions. The distributions of the Moroccan wheatear birds were found to be slightly affected by future climate changes.     

基于机器学习算法的草地地上生物量估测——以祁连山草地为例

草地地上生物量（Aboveground Biomass,AGB）是指导畜牧业生产管理的重要指标,是草畜平衡综合分析的基础。目前,有关祁连山草地AGB反演的研究较少,且多源数据间的尺度差异问题并未得到很好的解决。为了解祁连山草地AGB的空间分布状况,利用Sentinel-2多光谱数据、无人机（Unmanned Aerial Vehicle,UAV）数据以及2021年植被生长期实测草地AGB数据实现了空天地一体化监测,通过决策树回归（Decision Tree Regression,DTR）、随机森林回归（Random Forest Regression,RFR）、梯度提升决策回归树（Gradient Boosting Regression Tree,GBRT）以及极致梯度提升（eXtreme Gradient Boosting,XGBoost）共4种算法反演草地AGB的适用性分析,利用最优模型反演了祁连山草地的AGB空间分布状况。结果表明：研究区内多种植被指数所表现出的特性有所差异。祁连山地区AGB在空间分布上呈现出由西北向东南递增的趋势,平均AGB为925.43kg/hm²。6种植被指数与实测AGB之间均表现为显著正相关,适合作为祁连山草地AGB遥感反演的指标;XGBoost模型较其它模型具有最高的R²值（0.78）和精度（74.75%）、最低的均方根误差（RMSE,99.74 kg/hm²）和平均绝对误差（MAE,71.60 kg/hm²）,模型反演效果最好;UAV数据能够提供更加详细的空间细节特征,减小Sentinel-2数据和实地采样数据间的尺度差异;因此,基于6种植被指数与祁连山草地AGB间的相关性,构建XGBoost模型反演研究区草地AGB空间分布状况是具有实践意义的。研究结果将为指导祁连山草地畜牧业的发展和维护草地生态系统的平衡提供一定的参考价值与数据支撑。     

Transferable species distribution modelling: Comparative performance of Generalised Functional Response models

Predictive species distribution models (SDMs) are becoming increasingly important in ecology, in the light of rapid environmental change. However, the predictions of most current SDMs are specific to the habitat composition of the environments in which they were fitted. This may limit SDM predictive power because species may respond differently to a given habitat depending on the availability of all habitats in their environment, a phenomenon known as a functional response in resource selection. The Generalised Functional Response (GFR) framework captures this dependence by formulating the SDM coefficients as functions of habitat availability. The original GFR implementation used global polynomial functions of habitat availability to describe the functional responses. In this study, we develop several refinements of this approach and compare their predictive performance using two simulated and two real datasets. We first use local radial basis functions (RBF), a more flexible approach than global polynomials, to represent the habitat selection coefficients, and balance bias with precision via regularization to prevent overfitting. Second, we use the RBF-GFR and GFR models in combination with the classification and regression tree CART, which has more flexibility and better predictive powers for non-linear modelling. As further extensions, we use random forests (RFs) and extreme gradient boosting (XGBoost), ensemble approaches that consistently lead to variance reduction in generalization error. We find that the different methods are ranked consistently across the datasets for out-of-data prediction. The traditional stationary approach to SDMs and the GFR model consistently perform at the bottom of the ranking (simple SDMs underfit, and polynomial GFRs overfit the data). The best methods in our list provide non-negligible improvements in predictive performance, in some cases taking the out-of-sample R2 from 0.3 up to 0.7 across datasets. At times of rapid environmental change and spatial non-stationarity ignoring the effects of functional responses on SDMs, results in two different types of prediction bias (under-prediction or mis-positioning of distribution hotspots). However, not all functional response models perform equally well. The more volatile polynomial GFR models can generate biases through over-prediction. Our results indicate that there are consistently robust GFR approaches that achieve impressive gains in transferability across very different datasets.     

The modular habitat model (MHM) for the ide, Leuciscus idus (L.) - a new method to predict the suitability of inshore habitats for fish

Groynes are the dominant river engineering structures along the lowland section of large European rivers such as the Rhine, Danube and the Elbe. More than 6000 groynes structure the 400 km stretch of the potamal of the Elbe River. After 1945, destruction of the groynes increased through ice and flood events in the eastern part of Germany. In the past ten years, groyne reconstruction was accompanied by a controversial discussion in the context of the ecological integrity of the Elbe River. With the modular habitat model (MHM) a tool was developed to evaluate the suitability and to balance the availability of fish habitats in groyne fields of different conditions. The morphodynamic module produced a digital terrain model and a spatial model of flow velocity for each groyne field separately. Based on point abundance sampling by electro‐fishing, models of habitat preference were developed for different life history stages by logistic regression. Statistical models predicting the preference of fish‐environment relationships (Leuciscus idus) at different life history stages. The models were discriminated and validated by receiver operating characteristic (ROC) curves. The link between the statistical and the spatial model was realised in the suitability module. The suitability of microhabitats is expressed in classes for each species and life history stage separately. Habitat availability is balanced on the level of mesohabitat, e.g. different types of groyne fields. The temporal dynamics of habitat availability are analysed by considering different levels of discharge. For the stage ‘juvenile A’ and preadult the habitat suitability is better in fields downstream of destroyed groynes. For ‘juvenile B’ and adult stages of the ide, groyne fields in general constitute low habitat suitability. Differences in spatial availability are higher than the differences in temporal habitat availability.     

External morphology of a Slovenian population of pumpkinseed Lepomis gibbosus (L.) from a habitat with extreme thermal conditions

The external morphology and growth variability of morphometric characters of pumpkinseed (n = 141) from an oxbow (River Sava, Slovenia) that receives thermal effluent were examined using triple regression analysis. Differences in external morphology between pumpkinseed from the oxbow and both native North American (River Otonabee, Looncall Lake, Canada) and non‐native Central European populations (side arms of the River Danube, Slovakia) were evaluated. Two possible morphotypes among adults were observed, whereas the morphology of juveniles appears rather uniform across geographical location (i.e. Otonabee, Looncall, Danube). This suggests that environmental conditions, i.e. epigenetical information, seem to be responsible for most of variability in pumpkinseed morphology, which represents a function of epigenetical mechanisms. However, further investigation into epigenetical interactions, especially early development, fecundity, number of spawning acts per season, parental care, egg size and age at maturation are necessary to test this hypothesis.     

Comparing correlative and bioenergetics‐based habitat suitability models for drift‐feeding fishes

相似文献   

Ligula infestation of bleak Alburnus alburnus (L.) in the tidal Thames

Heavy infestations of the cestode Ligula are reported in bleak Alburnus alburnus (L.) from the tidal Thames. This parasite has not apparently been recorded previously from this host in the British Isles. The life cycle of the parasite is briefly summarized. Data on this infestation suggest an increase in the incidence of infestation up to a maximum in young bleak, followed by a decrease in older fish. There also appears to be a decrease from summer to autumn. The positive relationship between the weight of the parasite and the length of the host is discussed. Observations are made on the effect of Ligula on its host; it is suggested that many of the young bleak die during the autumn and winter, following their infestation.     

Potential distribution of vulnerable Entandrophragma angolense (Welw.) C. DC. (Meliaceae) in East Africa

The various human‐induced threats imposed on nature have recently triggered the study of species' distributions. We developed potential suitability models using two algorithms for a threatened African mahogany, Entandrophragma angolense, in three East African countries; Kenya, Tanzania and Uganda. The effect of features selection and modelling algorithm selection on potential suitability predictions was explored. Occurrence records and high‐resolution environmental data were used. The two species distribution modelling techniques were genetic algorithm rule for prediction; and maximum entropy modelling. With Maxent, the area under the receiver characteristic operating curve (AUC) for potential distribution models tested on independent data ranged from 0.942 to 0.972 when using automatic features and from 0.974 to 0.666 with target or specific features. With GARP, AUC for potential distribution models ranged from 0.591 to 0.736 with all rule types and from 0.388 to 0.805 for specific rule types (Tables  1  and 2 ). The area under the E. angolense potential suitability was best predicted by soil, rainfall and aspect using GARP. Potential suitability increased with increasing aspect and decreased with increasing slope. Low rainfall and elevation increased potential suitability, while high levels of either variable decreased potential suitability. Potential suitability maps for vulnerable species require using a multi‐algorithm, fine scale data approach and incorporation of environmental variables like soil, slope, land use and elevation. Species distribution models can offer insight on the distribution requirements of vulnerable species and help guide the development of management plans. Results of this study suggest that E. angolense management plans should promote the protection of terrestrial forests surrounding water bodies including Mabira forest in Uganda.     

Assessing bluegill (Lepomis macrochirus) habitat suitability using partial dependence function combined with classification approaches

Classification approaches have been used to understand the habitat suitability of key species. Partial dependence function is an especially useful concept despite of a lack of studies that compare the results of the function against observations. Furthermore, there has been scant research investigating the relative performance of classification approaches for describing habitat suitability. Thus, we aim to assess the applicability of partial dependence function combined with classification approaches to describe habitat suitability of the bluegill Lepomis macrochirus, a riverine fish in the Kanto region of Japan. A total of 425 samples, along with eight environmental variables, were surveyed by the National Censuses on River Environments and were used throughout this research. Five classification approaches were combined with a partial dependence function to assess the habitat suitability of bluegill. The areas under the curves based on the training and test data were calculated 100 times using each of the five classification approaches. Additionally, partial dependence on individual and paired environmental variables was estimated using each combination of a five-classification approach and partial dependence function, and this dependence was plotted to determine the habitat suitability of bluegill. As a result, random forest approach demonstrated high predictive accuracy compared to other classification approaches. The combination of the partial dependence function and random forest described the peaks of habitat suitability observed in the field, both using individual and paired environmental variables. Moreover, habitat suitability based on pairs of environmental variables also indicated that bluegill changes their habitat throughout the year.     

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.     

Predicting the distribution of <Emphasis Type="Italic">Encephalartos latifrons</Emphasis>, a critically endangered cycad in South Africa

This study evaluates how a modelling approach to determine areas of suitable habitat for the Critically Endangered Albany cycad Encephalartos latifrons can assist in systematic conservation planning for this and other rare and threatened cycads. A map distinguishing suitable from unsuitable habitat for E. latifrons was produced and important environmental predictors (climate, geology, topography and vegetation) influencing the suitable habitat were estimated. The maximum entropy (MaxEnt) modelling technique was chosen for this study as it has consistently performed well compared with alternative modelling methods and is also an appropriate model choice when the sample size is small and locality records are relatively few. Predicted habitat suitability showed that some locations chosen for translocation and restoration of E. latifrons specimens are not suitable. This revealed that modelling suitable habitat can guide relocation and regeneration of E. latifrons and perhaps other threatened cycads with restricted distributions and few locality records. The species distribution model constructed for E. latifrons is the first reported habitat model for a Critically Endangered cycad in South Africa. The results may be incorporated into conservation planning and structured decision-making about translocations and restoration programmes involving vulnerable cycads, which are among the most threatened organisms globally.     

Habitat and home range fidelity in a trophically dimorphic pumpkinseed sunfish (<Emphasis Type="Italic">Lepomis gibbosus</Emphasis>) population

We investigated habitat selection in a trophically dimorphic population of pumpkinseed sunfish (Lepomis gibbosus) to determine whether littoral and limnetic ecotypes exhibit habitat or site fidelity. A transplant experiment was conducted, in which 998 pumpkinseeds captured from littoral and limnetic sites were marked and released in either the site of capture, the nearest site of the same habitat type, or the nearest site of the opposite habitat type. Daily recapture attempts over the course of the reproductive and growing season provided a 25% recapture rate, 40% of which were recaptured on multiple occasions at the same site. Site fidelity was very high in both ecotypes. Results estimated with a multi-state transition model indicated that the probability of a transplanted pumpkinseed returning to its site of origin ranged from 74% for limnetic pumpkinseeds released into a different limnetic or littoral site, to 93% for littoral pumpkinseeds released into a limnetic site. Furthermore, the probability of a pumpkinseed being recaptured at its site of origin if not transplanted was estimated at 97 and 98% for limnetic individuals and littoral individuals, respectively. Discriminant Function Analysis of helminth parasite loads sampled from littoral and limnetic individuals could classify site of origin with 96–100% accuracy, suggesting that the habitat and site fidelity patterns observed with mark–recapture are indicative of long-term habitat segregation of the two forms. The results of our experiment provide compelling evidence of correlated habitat selection as a function of home range fidelity within both ecotypes of a subtly dimorphic species. Such behaviour could have a significant effect on present or future gene flow.     

Predicted Deep-Sea Coral Habitat Suitability for the U.S. West Coast

Regional scale habitat suitability models provide finer scale resolution and more focused predictions of where organisms may occur. Previous modelling approaches have focused primarily on local and/or global scales, while regional scale models have been relatively few. In this study, regional scale predictive habitat models are presented for deep-sea corals for the U.S. West Coast (California, Oregon and Washington). Model results are intended to aid in future research or mapping efforts and to assess potential coral habitat suitability both within and outside existing bottom trawl closures (i.e. Essential Fish Habitat (EFH)) and identify suitable habitat within U.S. National Marine Sanctuaries (NMS). Deep-sea coral habitat suitability was modelled at 500 m×500 m spatial resolution using a range of physical, chemical and environmental variables known or thought to influence the distribution of deep-sea corals. Using a spatial partitioning cross-validation approach, maximum entropy models identified slope, temperature, salinity and depth as important predictors for most deep-sea coral taxa. Large areas of highly suitable deep-sea coral habitat were predicted both within and outside of existing bottom trawl closures and NMS boundaries. Predicted habitat suitability over regional scales are not currently able to identify coral areas with pin point accuracy and probably overpredict actual coral distribution due to model limitations and unincorporated variables (i.e. data on distribution of hard substrate) that are known to limit their distribution. Predicted habitat results should be used in conjunction with multibeam bathymetry, geological mapping and other tools to guide future research efforts to areas with the highest probability of harboring deep-sea corals. Field validation of predicted habitat is needed to quantify model accuracy, particularly in areas that have not been sampled.     

Sub-population structure of common fish species in the Elbe River estimated from DNA analysis

The aim of this study was to analyse the genetic structure of populations for seven common cyprinid fish species within a 120‐km‐long stretch of the lowland Elbe River, northern Germany. The results are needed for habitat modelling to estimate the proportion that environmentally based variance has of the total variances of home range, species distribution, habitat use and fish assemblage structure. Polymerase chain reaction (PCR)‐fingerprinting offers a rapid, efficient method for generating genetic markers and was therefore used to obtain an overview on population‐genetic structures of the following seven fish species: asp (Aspius aspius), bleak (Alburnus alburnus), blue bream (Abramis ballerus), common bream (Abramis brama), gudgeon (Gobio gobio), ide (Leuciscus idus) and roach (Rutilus rutilus). Of the 20 random primers, between eight (ide) and 18 (roach) produced polymorphic bands. The mean levels of genetic similarity between samples, estimated as bandsharing frequencies, varied between 76% in bleak and 98% in asp. The corresponding genetic distances among samples varied between 0.02 ± 0.01 in asp and 0.24 ± 0.09 in bleak. The genetic distances among samples were not significant in all of the pairwise comparisons, and correlated only weakly with the geographic distances among sampling sites. It was therefore concluded that the stretch of the Elbe surveyed was inhabited by single, panmictic populations of the species studied and thus that the observed habitat preferences, fish distribution, home range and ecological performance of species within this area will depend on stochastic environmental factors or result from biotic interactions.     

Assessing the effect of Separation Levee Project on Chinese sturgeon (<Emphasis Type="Italic">Acipensor sinensis</Emphasis>) spawning habitat suitability in Yangtze River,China

Chinese sturgeon (Acipenser sinensis) is a protected anadromous fish species. The migration pattern of the fish has been blocked by the construction of Gezhouba Dam, reducing the natural spawning site length to less than 7 km along the Yangtze River. However, the fish has since established an alternative spawning ground in the narrow reach downstream of Gezhouba Dam. To enhance navigation, a Separation Levee Project (SLP) was implemented in the new-found spawning habitat of the fish. To therefore evaluate the effect of the SLP on Chinese sturgeon spawning habitat suitability, the conditions in the spawning habitat were simulated using River2D (a two-dimensional hydrodynamic model). Two main approaches (habitat kinetic energy and circulation metrics) were used in the simulation. The study showed that SLP only slightly changed the physical conditions in the spawning habitat. Using hydrodynamic simulation, the weighted usable area (WUA) before and after the SLP construction was also computed and habitat preference curve developed for water depth and velocity. On the average, SLP reduced WUA—a finding that was consistent with field-measured data. Based on WUA, the habitat conditions were more sensitive to SLP proximity than metrics based on velocity gradients. SLP posed detrimental impacts on the suitability of spawning habitats of Chinese sturgeon. The findings in this study provide further basis for the protection and restoration of Chinese sturgeon spawning habitats in especially the lower reach of Yangtze River.     

Modelling the distribution of a threatened habitat: the California sage scrub

Aim Using predictive species distribution and ecological niche modelling our objectives are: (1) to identify important climatic drivers of distribution at regional scales of a locally complex and dynamic system – California sage scrub; (2) to map suitable sage scrub habitat in California; and (3) to distinguish between bioclimatic niches of floristic groups within sage scrub to assess the conservation significance of analysing such species groups. Location Coastal mediterranean‐type shrublands of southern and central California. Methods Using point localities from georeferenced herbarium records, we modelled the potential distribution and bioclimatic envelopes of 14 characteristic sage scrub species and three floristic groups (south‐coastal, coastal–interior disjunct and broadly distributed species) based upon current climate conditions. Maxent was used to map climatically suitable habitat, while principal components analysis followed by canonical discriminant analysis were used to distinguish between floristic groups and visualize species and group distributions in multivariate ecological space. Results Geographical distribution patterns of individual species were mirrored in the habitat suitability maps of floristic groups, notably the disjunct distribution of the coastal–interior species. Overlap in the distributions of floristic groups was evident in both geographical and multivariate niche space; however, discriminant analysis confirmed the separability of floristic groups based on bioclimatic variables. Higher performance of floristic group models compared with sage scrub as a whole suggests that groups have differing climate requirements for habitat suitability at regional scales and that breaking sage scrub into floristic groups improves the discrimination between climatically suitable and unsuitable habitat. Main conclusions The finding that presence‐only data and climatic variables can produce useful information on habitat suitability of California sage scrub species and floristic groups at a regional scale has important implications for ongoing efforts of habitat restoration for sage scrub. In addition, modelling at a group level provides important information about the differences in climatic niches within California sage scrub. Finally, the high performance of our floristic group models highlights the potential a community‐level modelling approach holds for investigating plant distribution patterns.