Modelling the nesting-habitat of threatened vulture species in the caucasus:An ecosystem approach to formalising environmental factors in species distribution models

Abiotic factors play an important role in species localisation,but biotic and anthropogenic predictors must also be considered in distribution modelling for models to be biologically meaningful.In this study,we formalised the biotic predictors of nesting sites for four threatened Caucasian vultures by including species distribution models(wild ungulates,nesting tree species)as biotic layers in the vulture Maxent models.Maxent was applied in the R dismo package and the best set of the model parameters were defined in the R ENMeval package.Performance metrics were continuous Boyce index,Akaike's information criterion,the area under receiver operating curve and true skill statistics.We also calculated and evaluated the null models.Kernel density estimation method was applied to assess the overlap of vulture ecological niches in the environmental space.The accessibility of anthropogenic food resources was estimated using the Path Distance measure that considers elevation gradient.The availability of pine forests(Scots Pine)and wild ungulates(Alpine Chamois and Caucasian Goat)contributed the most(29.6%and 34.3%)to Cinereous Vulture(Aegypius monachus)nesting site model.Wild ungulate distribution also contributed significantly(about 46%)to the Bearded Vulture(Gypaetus barbatus)model.This scavenger nests in the highlands of the Caucasus at a minimum distance of 5–10 km from anthropogenic facilities.In contrast,livestock as a food source was most important in colony distribution of Griffon Vulture(Gyps fulvus).The contribution of distances to settlements and agricultural facilities to the model was 45%.The optimal distance from Egyptian Vulture(Neophron percnopterus)nesting sites to settlements was only 3–10 km,to livestock facilities no more than 15 km with the factor contribution of about 57%.Excluding the wild ungulate availability,the ecological niches of studied vultures overlapped significantly.Despite similar foraging and nesting requirements,Caucasian vultures are not pronounced nesting and trophic competitors due to the abundance of nesting sites,anthropogenic food sources and successful niche sharing.

Common Species Distribution Models in Biodiversity Analysis and Their Challenges and Prospects in Application

Species distribution models have been widely used to explore suitable habitats of species,the impact of climate change on the distribution of suitable habitats of species,and the construction of ecological reserves.This paper introduced species distribution models commonly used in biodiversity analysis,as well as model performance evaluation indexes,challenges in the application of species distribution models,and finally prospected the development trend of research on species distribution models.

Eff ects of climate change on the potential habitat distribution of swimming crab Portunus trituberculatus under the species distribution model

Over the last decades,the species distribution model(SDM)has become an essential tool for studying the potential eff ects of climate change on species distribution.In this study,an ensemble SDM was developed to predict the changes in species distribution of swimming crab Portunus trituberculatus across diff erent seasons in the future(2050s and 2100s)under the climate scenarios of Representative Concentration Pathway(RCP)4.5 and RCP8.5.Results of the ensemble SDM indicate that the distribution of this species will move northward and exhibit evident seasonal variations.Among the four seasons,the suitable habitat for this species will be signifi cantly reduced in summer,with loss rates ranging from 45.23%(RCP4.5)to 88.26%(RCP.8.5)by the 2100s.The loss of habitat will mostly occur in the East China Sea and the southern part of the Yellow Sea,while a slight increase in habitat will occur in the northern part of the Bohai Sea.These fi ndings provide an information forecast for this species in the future.Such forecast will be helpful in improving fi shery management under climate change.

Species distribution modeling in regions of high need and limited data： waterfowl of China

Background: A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources.Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China.Methods: Faced with limited data, we built species distribution models using a habitat suitability approach for China's breeding and non-breeding(hereafter, wintering) waterfowl.An extensive review of the literature was used to determine model parameters for habitat modeling.Habitat relationships were implemented in GIS using land cover covariates.Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available.Results: We developed suitability models for 42 waterfowl species(30 breeding and 39 wintering) at 1 km resolution for the extent of China, along with cumulative and genus level species richness maps.Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China.Wintering waterfowl suitability was highest in the lowland regions of southeastern China.Validation measures indicated strong performance in predicting species presence.Comparing our model outputs to China's protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized.Conclusions: These suitability models are the first available for many of China's waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.

Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area

Correlative species distribution models(SDMs)are important tools to estimate species’geographic distribution across space and time,but their reliability heavily relies on the availability and quality of occurrence data.Estimations can be biased when occurrences do not fully represent the environmental requirement of a species.We tested to what extent species’physiological knowledge might influence SDM estimations.Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia,we compiled a comprehensive dataset of occurrence records.We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs:a naïve model that solely depends on environmental correlates,and a physiologically informed model that further incorporates physiological information as priors.We further tested the models’sensitivity to calibration area choices by fitting them with different buffered areas around known presences.Compared with naïve models,the physiologically informed models successfully captured the negative influence of high temperature on A.japonicus and were less sensitive to the choice of calibration area.The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change(i.e.,larger range expansion and less contraction)than the physiologically informed models.Our findings highlight benefits from incorporating physiological information into correlative SDMs,namely mitigating the uncertainties associated with the choice of calibration area.Given these promising features,we encourage future SDM studies to consider species physi-ological information where available.

Finer topographic data improves distribution modeling of Picea crassifolia in the northern Qilian Mountains

The Qilian Mountains, a national key ecological function zone in Western China, play a pivotal role in ecosystem services. However, the distribution of its dominant tree species, Picea crassifolia (Qinghai spruce), has decreased dramatically in the past decades due to climate change and human activity, which may have influenced its ecological functions. To restore its ecological functions, reasonable reforestation is the key measure. Many previous efforts have predicted the potential distribution of Picea crassifolia, which provides guidance on regional reforestation policy. However, all of them were performed at low spatial resolution, thus ignoring the natural characteristics of the patchy distribution of Picea crassifolia. Here, we modeled the distribution of Picea crassifolia with species distribution models at high spatial resolutions. For many models, the area under the receiver operating characteristic curve (AUC) is larger than 0.9, suggesting their excellent precision. The AUC of models at 30 m is higher than that of models at 90 m, and the current potential distribution of Picea crassifolia is more closely aligned with its actual distribution at 30 m, demonstrating that finer data resolution improves model performance. Besides, for models at 90 m resolution, annual precipitation (Bio12) played the paramount influence on the distribution of Picea crassifolia, while the aspect became the most important one at 30 m, indicating the crucial role of finer topographic data in modeling species with patchy distribution. The current distribution of Picea crassifolia was concentrated in the northern and central parts of the study area, and this pattern will be maintained under future scenarios, although some habitat loss in the central parts and gain in the eastern regions is expected owing to increasing temperatures and precipitation. Our findings can guide protective and restoration strategies for the Qilian Mountains, which would benefit regional ecological balance.

Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling

In recent years,Meloidogyne enterolobii has emerged as a major parasitic nematode infesting many plants in tropical or subtropical areas.However,the regions of potential distribution and the main contributing environmental variables for this nematode are unclear.Under the current climate scenario,we predicted the potential geographic distributions of M.enterolobii worldwide and in China using a Maximum Entropy(MaxEnt)model with the occurrence data of this species.Furthermore,the potential distributions of M.enterolobii were projected under three future climate scenarios(BCC-CSM2-MR,CanESM5 and CNRM-CM6-1)for the periods 2050s and 2090s.Changes in the potential distribution were also predicted under different climate conditions.The results showed that highly suitable regions for M.enterolobii were concentrated in Africa,South America,Asia,and North America between latitudes 30°S to 30°N.Bio16(precipitation of the wettest quarter),bio10(mean temperature of the warmest quarter),and bio11(mean temperature of the coldest quarter)were the variables contributing most in predicting potential distributions of M.enterolobii.In addition,the potential suitable areas for M.enterolobii will shift toward higher latitudes under future climate scenarios.This study provides a theoretical basis for controlling and managing this nematode.

Applying various algorithms for species distribution modelling

Species distribution models have been used extensively in many fields,including climate change biology,landscape ecology and conservation biology.In the past 3 decades,a number of new models have been proposed,yet researchers still find it difficult to select appropriate models for data and objectives.In this review,we aim to provide insight into the prevailing species distribution models for newcomers in the field of modelling.We compared 11 popular models,including regression models(the generalized linear model,the generalized additive model,the multivariate adaptive regression splines model and hierarchical modelling),classification models(mixture discriminant analysis,the generalized boosting model,and classification and regression tree analysis)and complex models(artificial neural network,random forest,genetic algorithm for rule set production and maximum entropy approaches).Our objectives are:(i)to compare the strengths and weaknesses of the models,their characteristics and identify suitable situations for their use(in terms of data type and species-environment relationships)and(ii)to provide guidelines for model application,including 3 steps:model selection,model formulation and parameter estimation.

Modelling the spatial distribution of snake species in northwestern Tunisia using maximum entropy(Maxent) and Geographic Information System(GIS)

We used GIS and maximum entropy to predict the potential distribution of six snake species belong to three families in Kroumiria（Northwestern Tunisia）： Natricidae（Natrix maura and Natrix astreptophora）, Colubridae（Hemorrhois hippocrepis, Coronella girondica and Macroprotodon mauritanicus）, and Lamprophiidae（Malpolon insignitus）. The suitable habitat for each species was modelled using the maximum entropy algorithm, combining presence field data（collected during 16 years：2000–2015） with a set of seven environmental variables（mean annual precipitation, elevation, slope gradient,aspect, distance to watercourses, land surface temperature and normalized Differential Vegetation Index. The relative importance of these environmental variables was evaluated by jackknife tests and the predictive power of our models was assessed using the area under the receiver operating characteristic. The main explicative variables of the species distribution were distance from streams and elevation, with contributions ranging from 60 to 77 and from 10 to 25%,respectively. Our study provided the first habitat suitability models for snakes in Kroumiria and this information can be used by conservation biologists and land managers concerned with preserving snakes in Kroumiria.

Potential distributional shifts in North America of allelopathic invasive plant species under climate change models

Predictive studies play a crucial role in the study of biological invasions of terrestrial plants under possible climate change scenarios.Invasive species are recognized for their ability to modify soil microbial communities and influence ecosystem dynamics.Here,we focused on six species of allelopathic flowering plants-Ailanthus altissima,Casuarina equisetifolia,Centaurea stoebe ssp.micranthos,Dioscorea bulbifera,Lantana camara,and Schinus terebinthifolia-Xhat are invasive in North America and examined their potential to spread further during projected climate change.We used Species Distribution Models(SDMs)to predict future suitable areas for these species in North America under several proposed future climate models.ENMEval and Maxent were used to develop SDMs,estimate current distributions,and predict future areas of suitable climate for each species.Areas with the greatest predicted suitable climate in the future include the northeastern and the coastal northwestern regions of North America.Range size estimations demonstrate the possibility of extreme range loss for these invasives in the southeastern United States,while new areas may become suitable in the northeastern United States and southeastern Canada.These findings show an overall northward shift of suitable climate during the next few decades,given projected changes in temperature and precipitation.Our results can be utilized to analyze potential shifts in the distribution of these invasive species and may aid in the development of conservation and management plans to target and control dissemination in areas at higher risk for potential future invasion by these allelopathic species.

Mapping the potential distribution suitability of 16 tree species under climate change in northeastern China using Maxent modelling

Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In this study,we developed species distribution model(SDM)for 16 major tree species with 2,825 permanent sample plots with natural origin from Chinese National Forest Inventory data collected in Jilin Province using the Maxent model.Three types of environmental factors including bioclimate,soil and topography with a total of 33 variables were tested as the input.The values of area under the curve(AUC,one of the receiver operating characteristics of the Maxent model)in the training and test datasets were between 0.784 and 0.968,indicating that the prediction results were quite reliable.The environmental factors affecting the distribution of species were ranked in terms of their importance to the species distribution.Generally,the climatic factors had the greatest contribution,which included mean diurnal range,annual mean temperature,temperature annual range,and iosthermality.But the main environmental factors varied with tree species.Distribution suitability maps under current(1950-2000)and future climate scenarios(CCSM4-RCP 2.6 and RCP 6.0 during 2050)were produced for 16 major tree species in Jilin Province using the model developed.The predicted current and future ranges of habitat suitability of the 16 tree species are likely to be positively and negatively affected by future climate.Seven tree species were found to benefit from future climate including B etula costata,Fraxinus mandshurica,Juglans mandshurica,Phellodendron amurense,Populus ussuriensis,Quercus mongolica and Ulmus pumila;five tree species will experience decline in their suitable habitat including B.platyphylla,Tilia mongolica,Picea asperata,Pinus sylvestris,Pinus koraiensis;and four(Salix koreensis,Abies fabri,Pinus densiflora and Larix olgensis)showed the inconsistency under RCP 2.6 and RCP 6.0 scenarios.The maps of the habitat suitability can be used as a basis for afforestation and forest restoration in northeastern China.The SDMs could be a potential tool for forest management planning.

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

Background:Species Distribution Modelling(SDM)coupled with freely available multispectral imagery from Sentinel-2(S2)satellite provides an immense contribution in monitoring invasive species.However,attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices(S2-RIs)and biophysical variables,in particular,were limited.Hence,this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin,Ethiopia.Thirty-five variables were computed from Sentinel-2B level-2A,and out of the variables,twelve significant variables were selected using Variable Inflation Factor(VIF).A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software“sdm”package.The performance of the models was evaluated using sensitivity,specificity,True Skill Statistics(TSS),kappa coefficient,area under the curve(AUC),and correlation.Results:Our findings demonstrated that except bioclim all machine learning and regression models provided successful prediction.Among the tested models,Random Forest(RF)performed better with 93%TSS and 99%AUC followed by Boosted Regression Trees(BRT),ensemble,Generalized Additive Model(GAM),Support Vector Machine(SVM),and Generalized Linear Model(GLM)in decreasing order.The relative influence of vegetation indices was the highest followed by soil indices,biophysical variables,and water indices in decreasing order.According to RF prediction,16.14%(1553.5 km^(2))of the study area was invaded by the alien species.Conclusions:Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species distribution.Besides,the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based invasive SDM.

Trends in species distribution modelling in context of rare and endemic plants:a systematic review

Background:Many research papers have utilized Species Distribution Models to estimate a species’current and future geographic distribution and environmental niche.This study aims to(a)understand critical features of SDMs used to model endemic and rare species and(b)to identify possible constraints with the collected data.The present systematic review examined how SDMs are used on endemic and rare plant species to identify optimal practices for future research.Results:The evaluated literature(79 articles)was published between January 2010 and December 2020.The number of papers grew considerably over time.The studies were primarily conducted in Asia(41%),Europe(24%),and Africa(2%).The bulk of the research evaluated(38%)focused on theoretical ecology,climate change impacts(19%),and conservation policy and planning(22%).Most of the papers were published in publications devoted to biodiversity conservation,ecological or multidisciplinary fields.The degree of uncertainty was not disclosed in most studies(81%).Conclusion:This systematic review provides a broad overview of the emerging trends and gaps in the SDMs research.The majority of studies failed to present uncertainties and error estimates.However,when model performance estimates are given,the model results will be highly effective,allowing for more assurance in the predictions they make.Furthermore,based on our systematic review,we recommend that in the future rare and endemic SDMs should represent uncertainty levels and estimates of errors in the modelling process.

Climate-change habitat shifts for the vulnerable endemic oak species(Quercus arkansana Sarg.)

Quercus arkansana(Arkansas oak)is at risk of becoming endangered,as the total known population size is represented by a few isolated populations.The potential impact of climate change on this species in the near future is high,yet knowledge of its predicted effects is limited.Our study utilized the biomod2 R package to develop habi-tat suitability ensemble models based on bioclimatic and topographic environmental variables and the known loca-tions of current distribution of Q.arkansana.We predicted suitable habitats across three climate change scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5)for 2050,2070,and 2090.Our findings reveal that the current suitable habitat for Q.arkansana is approximately 127,881 km^(2) across seven states(Texas,Arkansas,Alabama,Louisiana,Mississippi,Georgia,and Florida);approximately 9.5%is encompassed within state and federally managed protected areas.Our models predict that all current suitable habitats will disap-pear by 2050 due to climate change,resulting in a northward shift into new regions such as Tennessee and Kentucky.The large extent of suitable habitat outside protected areas sug-gests that a species-specific action plan incorporating pro-tected areas and other areas may be crucial for its conserva-tion.Moreover,protection of Q.arkansana habitat against climate change may require locally and regionally focused conservation policies,adaptive management strategies,and educational outreach among local people.

Modeling Bat Species Richness and Spatial Distribution in Burkina Faso

The spatial distribution of bats in Burkina Faso is little-known. Previous studies have only described the bat species’ richness in Burkina Faso. This study was conducted to highlight bat species’ richness distribution within Burkina Faso and environmental variables that influence this distribution with the aim to give support for protection and further sampling for biodiversity. The Species Distribution Models (SDMs) were used to perform this study. To do that, species occurrences were collected throughout literature and field sampling and correlated to environmental variables through the Maxent software (Maximum Entropy). Our modeling variables included climate, vegetation cover, topography and hydrography data. The Jackknife test was performed to determine the importance of environmental variables that influence the species distribution model. The results showed that bats are present in all areas of vegetation in Burkina Faso. Species richness varies across the country. The species richness for major families increases from North to South. The total annual precipitation and topography are the main variables that positively influence bats distribution in Burkina Faso but the bare ground cover and standard deviation of the maximum temperature negatively influence this distribution. This modeling approach of bat species richness is important for policies makers and represents an invaluable tool in ecological management, particularly in the current context of climate change.

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species.In this study,the spatial distribution of five endemic and threatened species belonging to the genus Onosma(including O.asperrima,O.bisotunensis,O.kotschyi,O.platyphylla,and O.straussii)was investigated under present and future climate change scenarios:RCP2.6(RCP,representative concentration pathway;optimistic scenario)and RCP8.5(pessimistic scenario)for the years 2050 and 2080 in Iran.Analysis was conducted using the maximum entropy(MaxEnt)model to provide a basis for the protection and conservation of these species.Seven environmental variables including aspect,depth of soil,silt content,slope,annual precipitation,minimum temperature of the coldest month,and annual temperature range were used as main predictors in this study.The model output for the potential habitat suitability of the studied species showed acceptable performance for all species(i.e.,the area under the curve(AUC)>0.800).According to the models generated by MaxEnt,the potential current patterns of the species were consistent with the observed areas of distributions.The projected climate maps under optimistic and pessimistic scenarios(RCP2.6 and RCP8.5,respectively)of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions.Among all species,O.bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080.Finally,the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes.The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.

Modeling of cold-temperate tree Pinus koraiensis (Pinaceae) distribution in the Asia-Pacific region: Climate change impact

Background:Pinus koraiensis Siebold&Zucc.(Korean pine)is a key species of the mixed cold temperate forests of Northeast Asia.Current climate change can significantly worsen the quality of P.koraiensis habitats and therefore lead to a large-scale structural and functional transformation of the East Asian mixed forests.We built a species distribution model(SDM)for P.koraiensis using the random forest classifier–a versatile machine learning al-gorithm,to discover overlap areas of potential species occurrence in the climate condition of the Last Glacial Maximum(~21,000 year before present)and in the projected future climates(2070 year),from which possible permanent refugia for P.koraiensis were identified.Results:Using the random forest supervised learning algorithm,we developed models of the modern distribution of P.koraiensis in accordance with the five selected bioclimatic variables(Kira’s warmth and coldness indices,the index of continentality,the rain precipitation index,and the snow precipitation index).In addition to current climatic conditions,we performed this analysis for the climate of the Last Glacial Maximum and for the future projected climate(2070)under scenarios RCP2.6 and RCP8.5.Among the predictors,the rain index appears to be the most significant.The land area estimates with high suitability for P.koraiensis was 303,785 km 2 under current climatic conditions,586,499 km 2 for the Last Glacial Maximum,and 337,573 km^(2) for the future(2070)period under the RCP2.6 scenario,and 397,764 km^(2) under the RCP8.5 scenario.Conclusions:Most of the potential range of P.koraiensis during the Last Glacial Maximum was located outside the current distribution area of the species.The climatically suitable P.koraiensis habitats will likely disappear in the western part of its modern range.In the southern part of the range,which includes glacial refugia,the areas of continuous distribution of the P.koraiensis populations since the end of the Pleistocene are expected to be frag-mented,but some localities in the north of the Korean Peninsula,northeast China,southern Primorye(Russia),and central Honshu(Japan)with suitable climatic conditions for the species will support the existence of populations.

Potential Distribution of Seagrass Meadows Based on the MaxEnt Model in Chinese Coastal Waters

Seagrass meadows are generally diverse in China and have become important ecosystem with essential functions.However,the seagrass distribution across the seawaters of China has not been evaluated,and the magnitude and direction of changes in seagrass meadows remain unclear.This study aimed to provide a nationwide seagrass distribution map and explore the dynamic changes in seagrass population under global climate change.Simulation studies were performed using the modeling software MaxEnt with 58961 occurrence records and 27 marine environmental variables to predict the potential distribution of seagrasses and calculate the area.Seven environmental variables were excluded from the modeling processes based on a correlation analysis to ensure predicted suitability.The predicted area was 790.09 km^(2),which is much larger than the known seagrass distribution in China(87.65 km^(2)).By 2100,the suitable habitat of seagrass will shift northwest and increase to 923.62 km2.Models of the sum of the individual family under-pre-dicted the national distribution of seagrasses and consistently showed a downward trend in the future.Out of all environmental vari-ables,physical parameters(e.g.,depth,land distance,and sea surface temperature)contributed the most in predicting seagrass distri-butions,and nutrients(e.g.,nitrate,phosphate)ranked among the key influential predictors for habitat suitability in our study area.This study is the first effort to fill a gap in understanding the distribution of seagrasses in China.Further studies using modeling and biological/ecological approaches are warranted.

Relative importance of tidal flats and artificial habitats for two spoonbill species and related interspecific differences

Artificial/seminatural environments,such as aquacultural ponds,saltpans,and croplands,have recently been acknowledged as important habitats for coastal waterbirds.Although coastal waterbirds tend to use artificial habitats around tidal flats as roosting sites during high-tide,it remains unclear whether the importance of surrounding habitats relative to tidal flats varies among landscape types,seasons,species,or tidal conditions.The Black-faced Spoonbill(Platalea minor)and Eurasian Spoonbill(P.leucorodia)are two closely related sympatric species in East Asia with narrow and wide distribution ranges and habitat requirements,respectively.We therefore expect that both species will use surrounding artificial habitats across seasons at high tides,but Blackfaced Spoonbills will use them less frequently than Eurasian Spoonbills.Here,we address these hypotheses in the Imazu tidal flat and its surrounding environments in southern Japan.We investigated the habitat use and behavioral patterns of both species through route and behavioral surveys during the fall migration and wintering seasons in 2021.We found that both species used surrounding habitats including artificial ones more frequently than the tidal flat regardless of the tidal condition or season,but spoonbills used these habitats more frequently in winter than in autumn.We also found that Eurasian Spoonbills foraged in surrounding artificial habitats more frequently than Black-faced Spoonbills.These results not only demonstrate how coastal waterbirds exploit surrounding habitats relative to tidal flats but also suggest that the importance of surrounding habitats varies among species and seasons.Our study thus emphasizes that valuing and managing surrounding habitats in addition to tidal flats are key to conserving globally declining waterbirds.

Diversity of beetle species and functional traits along gradients of deadwood suggests weak environmental filtering

Background: Gradients in local environmental characteristics may favour the abundance of species with particular traits, while other species decline, or favour species with different traits at the same time, without an increase in average species abundances. Therefore, we asked: do variations in species and traits differ along gradients of deadwood variables? Do species abundance and trait occurrence change with species richness within or between functional groups? Thus, we analysed the beetle assemblages of five forest sites located in Italy, along the Apennines mountains.Methods: From 2012 to 2018 we sampled beetles and five deadwood types in 193 plots to characterise the deadwood gradient: standing dead trees, snags, dead downed trees, coarse woody debris, and stumps. We modelled beetle species relative abundances and trophic traits occurrences against the deadwood variables using joint species distribution models.Results: Out of 462 species, only 77 showed significant responses to at least one deadwood type, with a weak mean response across species. Trophic groups showed mostly negative responses to deadwood variables. Species abundance increased with species richness among sites only for phytophagous and saproxylophagous. Trait occurrence did not increase with species richness among sites, except for phytophagous and saproxylophagous.However, trait occurrence changed significantly with species richness of several trophic groups within some sites.We found that increases in species richness do not result in decreases in species abundance of a given trophic group, but rather null or positive relationships were found suggesting low interspecific competition.Conclusions: Our findings suggest that in Mediterranean mountain forests there is still room for increasing the level of naturalness, at least for what concerns deadwood management. On one side, our findings suggest that competition for deadwood substrates is still low, on the other side they indicate that increasing deadwood volume and types to improve overall beetle richness may increase also beetle abundances.