From furnace up to freezer:Elevational patterns of plant diversity in Mount Palvar,a semi-arid Irano-Turanian mountain range of southwest Asia

Much of the world's biodiversity lies in heterogeneous mountain areas with their diverse environments.As an example,Iranian montane ranges are highly diverse,particularly in the Irano-Turanian phytogeographical region.Understanding plant diversity patterns with increasing elevation is of high significance,not least for conservation planning.We studied the pattern of species richness,Shannon diversity,endemic richness,endemics ratio,and richness of life forms along a 3900 m elevational transect in Mount Palvar,overlooking the Lut Desert in Southeast Iran.We also analyzed the effect of environmental variables on species turnover along the vertical gradient.A total of 120 vegetation plots(10 m×10 m)were sampled along the elevational transect containing species and environmental data.To discover plant diversity pattern along the elevational gradient,generalized additive model(GAM)was used.Non-metric multidimensional scaling(NMDS)was applied for illustrating the correlation between species composition and environmental variables.We found hump-shaped pattern for species richness,Shannon diversity,endemic richness,and species richness of different life forms,but a monotonic increasing pattern for ratio of endemic species from low to high elevations.Our study confirms the humped pattern of species richness peaking at intermediate elevations along a complete elevational gradient in a semi-arid mountain.The monotonic increase of endemics ratio with elevation in our area as a case study is consistent with global increase of endemism with elevation.According to our results,temperature and precipitation are two important climatic variables that drive elevational plant diversity,particularly in seasonally dry areas.Our study suggests that effective conservation and management are needed for this low latitude mountain area along with calling for long-term monitoring for species redistribution.

Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data

Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.

Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.

Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models

There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.

Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.

Fitting Generalized Additive Logistic Regression Model with GAM Procedure

In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.

Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean

Generalized linear models （GLM） and generalized additive models （GAM） were used to standardize catch per unit fishing effort （CPUE） of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean. Three groups of variables were considered in the standardization： spatial variables （longitude and latitude）, temporal variables （year and month） and environmental variables, including sea surface temperature （SST）, sea surface salinity （SSS） and sea level height （SLH）. CPUE was treated as the dependent variable and its error distribution was assumed to be log-normal in each model. The model selections of GLM and GAM were based on the finite sample-corrected Akaike information criterion （AICC） and pseudo-coefficient （Pcf） combined P-value, respectively. Both GAM and GLM analysis showed that the month was the most important variable affecting CPUE and could explain 21.3% of variability in CPUE while other variables only explained 8.66%. The interaction of spatial and temporal variables weakly influenced the CPUE. Moreover, spatio-temporal factors may be more important in influencing the CPUE of this squid than environmental variables. The standardized and nominal CPUEs were similar and had the same trends in spatio-temporal distribution, but the standardized CPUE values tended to be smaller than the nominal CPUE. The CPUE tended to have much higher monthly variation than annual variations and their values increased with month. The CPUE became higher with increasing latitude-high CPUE usually occurred in 145°E-148°E and 149°E-162°E. The CPUE was higher when SST was 14-21℃ and the SLH from -22 cm to -18 cm. In this study, GAM tended to be more suitable than GLM in analysis of CPUE.

The study on fishing ground of neon flying squid, Ommastrephes bartrami,and ocean environment based on remote sensing data in the Northwest Pacific Ocean

The relationships between the neon flying squid, Ommastrephes bartrami, and the relative ocean environmental factors are analyzed. The environmental factors collected are sea surface temperature （SST）, chlorophyll concentration （Chl-α） and sea surface height （SSH） from NASA, as well as the yields of neon flying squid in the North Pacific Ocean. The results show that the favorable temperature for neon flying squid living is 10℃-22℃ and the favorite temperature is between 15℃-17℃. The Chl-α concentration is 0.1-0.6 mg/m^3. When Chl-α concentration changes to 0.12-0.14 mg/m^3, the probability of forming fishing ground becomes very high. In most fishing grounds, the SSH is higher than the mean SSH. The generalized additive model （GAM） was applied to analyze the correlations between neon flying squid and ocean environmental factors. Every year, squids migrate northward from June to August and return southward during October-November, and the characteristics of the both migrations are very different. When squids migrate to the north, most relationships between the yields and SST are positive. The relationships are negative when squids move to southward. The relationships between the yields and Chl-a concentrations are negative from June to October, and insignificant in November. There is no obvious correlation between the catches of squid and longitude, but good with latitude.

Growth and form of Quercus robur and Fraxinus excelsior respond distinctly different to initial growing space: results from 24-year-old Nelder experiments

Initial growing space is of critical importance to growth and quality development of individual trees. We investigated how mortality, growth （diameter at breast height, total height）, natural pruning （height to first dead and first live branch and branchiness） and stem and crown form of 24-year-old pedunculate oak （Quercus robur [L.]） and European ash （Fraxinus excelsior [L.]） were affected by initial spacing. Data were recorded from two replicate single-species Nelder wheels located in southern Germany with eight initial stocking regimes varying from 1,020 to 30,780 seedlings·ha？1. Mortality substantially decreased with increasing initial growing space but significantly differed among the two species, averaging 59% and 15% for oak and ash plots, respectively. In contrast to oak, the low self-thinning rate found in the ash plots over the investigated study period resulted in a high number of smaller intermediate or suppressed trees, eventually retarding individual tree as well as overall stand development. As a result, oak gained greater stem dimensions throughout all initial spacing regimes and the average height of ash significantly increased with initial growing space. The survival of lower crown class ashes also appeared to accelerate self-pruning dynamics. In comparison to oak, we observed less dead and live primary branches as well as a smaller number of epicormic shoots along the first 6 m of the lower stem of dominant and co-dominant ashes in all spacing regimes. Whereas stem form of both species was hardly affected by initial growing space, the percentage of brushy crowns significantly increased with initial spacing in oak and ash. Our findings suggest that initial stockings of ca. 12,000 seedlings per hectare in oak and 2,500 seedlings per hectare in ash will guarantee a sufficient number of at least 300 potential crop trees per hectare in pure oak and ash plantations at the end of the self-thinning phase, respectively. If the problem of epicormic shoots and inadequate self-pruning can be controlled with trainer species, the initial stocking may be reduced significantly in oak.

Spatio-seasonal patterns of fish diversity,Haizhou Bay,China

Spatial-seasonal patterns in fish diversity in Haizhou Bay were studied based on stratified random surveys conducted in 2011.Principal component analysis was conducted to distinguish different diversity components,and the relationships among 11 diversity indices were explored.Generalized additive models were constructed to examine the environmental effects on diversity indices.Eleven diversity indices were grouped into four components:(1) species numbers and richness,(2) heterogeneous indices,(3) evenness,and(4) taxonomic relatedness.The results show that diversity indices among different components are complementary.Spatial patterns show that fish diversity was higher in coastal areas,which was affected by complex bottom topography and spatial variations of water mass and currents.Seasonal trends could be best explained by the seasonal migration of dominant fish species.Fish diversity generally declined with increasing depth except for taxonomic distinctness,which increased with latitude.In addition,bottom temperature had a significant effect on diversity index of richness.These results indicate that substrate complexity and environmental gradients had important influences on fish diversity patterns,and these factors should be considered in fishery resource management and conservation.Furthermore,diversity in two functional groups(demersal/pelagic fishes) was influenced by different environmental factors.Therefore,the distribution of individual species or new indicators in diversity should be applied to examine spatio-seasonal variations in fish diversity.

Optimization of environmental variables in habitat suitability modeling for mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent waters

Habitat suitability index(HSI)models have been widely used to analyze the relationship between species abundance and environmental factors,and ultimately inform management of marine species.The response of species abundance to each environmental variable is different and habitat requirements may change over life history stages and seasons.Therefore,it is necessary to determine the optimal combination of environmental variables in HSI modelling.In this study,generalized additive models(GAMs)were used to determine which environmental variables to be included in the HSI models.Significant variables were retained and weighted in the HSI model according to their relative contribution(%)to the total deviation explained by the boosted regression tree(BRT).The HSI models were applied to evaluate the habitat suitability of mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent areas in 2011 and 2013–2017.Ontogenetic and seasonal variations in HSI models of mantis shrimp were also examined.Among the four models(non-optimized model,BRT informed HSI model,GAM informed HSI model,and both BRT and GAM informed HSI model),both BRT and GAM informed HSI model showed the best performance.Four environmental variables(bottom temperature,depth,distance offshore and sediment type)were selected in the HSI models for four groups(spring-juvenile,spring-adult,falljuvenile and fall-adult)of mantis shrimp.The distribution of habitat suitability showed similar patterns between juveniles and adults,but obvious seasonal variations were observed.This study suggests that the process of optimizing environmental variables in HSI models improves the performance of HSI models,and this optimization strategy could be extended to other marine organisms to enhance the understanding of the habitat suitability of target species.

National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.

Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government's monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.

Variation in the distribution of wintering anchovy Engraulis japonicus and its relationship with water temperature in the central and southern Yellow Sea

In the present study,we investigated a shift in the spatial distribution of wintering anchovy(Engraulis japonicus)and its relationship with water temperature,using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea,during 2000–2015.Our results indicate that the latitudinal distribution of wintering anchovy varied between years,but there was no consistent pattern in the direction of change(north or south).Wintering anchovy did not move northward with increasing water temperature.However,the latitudinal distribution of wintering anchovy correlated well with 10°C and 11°C isotherms.The results of both a one-step and a two-step generalized additive model indicated that water temperature was associated with both presence and biomass of wintering anchovy.This paper is the fi rst to systematically examine the relationship between anchovy distribution and water temperature using a variety of techniques.All the fi ndings confi rm the impact of water temperature on wintering anchovy distribution,which has important implications for the continued management of the anchovy resource and the enhancement of marine fi shery resources in the Yellow Sea,especially as the climate changes.However water temperature only partly explains the species distribution of anchovy,and stock characteristics also aff ect fi shery distribution.Therefore,other factors should be considered in future research.

Effects of spatio-temporal and environmental factors on distribution and abundance of wintering anchovy Engraulis japonicus in central and southern Yellow Sea

We investigated the spatio-temporal and environmental factors that affect the distribution and abundance of wintering anchovy and quantifi es the infl uences of these factors. Generalized additive models(GAMs) were developed to examine the variation in species distribution and abundance with a set of spatiotemporal and oceanographic factors, using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea during 2000–2011. The fi nal model accounted for 28.21% and 41.03% of the variance in anchovy distribution and abundance, respectively. The results of a two-step GAM showed that hour, longitude, latitude, temperature gradient(TGR), and chlorophyll a(Chl- a) concentration best explained the anchovy distribution(presence/absence) and that a model including year, longitude, latitude, depth, sea surface temperature(SST), and TGR best described anchovy abundance(given presence). Longitude and latitude were the most important factors affecting both distribution and abundance, but the area of high abundance tended to be east and south of the area where anchovy were most likely to be present. Hour had a signifi cant effect on distribution, but year was more important for anchovy abundance, indicating that the anchovy catch ratio varied across the day but abundance had an apparent interannual variation. With respect to environmental factors, TGR and Chl- a concentration had effects on distribution, while depth, SST, and TGR affected abundance. Changes in SST between two successive years or between any year and the 2000–2011 mean were not associated with changes in anchovy distribution or abundance. This fi nding indicated that short- and long-term water temperature changes during 2000–2011 were not of suffi cient magnitude to give rise to variation in wintering anchovy distribution or abundance in the study area. The results of this study have important implications for fi sheries management.

Evaluating the impact of spatio-temporal scale on CPUE standardization

This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.