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Understanding the biology and conducting effective conservation of migratory species requires an understanding of migratory connectivity – the geographic linkages of populations between stages of the annual cycle. Unfortunately, for most species, we are lacking such information. The North American Bird Banding Laboratory (BBL) houses an extensive database of marking, recaptures and recoveries, and such data could provide migratory connectivity information for many species. To date, however, few species have been analyzed for migratory connectivity largely because heterogeneous re‐encounter probabilities make interpretation problematic. We accounted for regional variation in re‐encounter probabilities by borrowing information across species and by using effort covariates on recapture and recovery probabilities in a multistate capture–recapture and recovery model. The effort covariates were derived from recaptures and recoveries of species within the same regions. We estimated the migratory connectivity for three tern species breeding in North America and over‐wintering in the tropics, common (Sterna hirundo), roseate (Sterna dougallii), and Caspian terns (Hydroprogne caspia). For western breeding terns, model‐derived estimates of migratory connectivity differed considerably from those derived directly from the proportions of re‐encounters. Conversely, for eastern breeding terns, estimates were merely refined by the inclusion of re‐encounter probabilities. In general, eastern breeding terns were strongly connected to eastern South America, and western breeding terns were strongly linked to the more western parts of the nonbreeding range under both models. Through simulation, we found this approach is likely useful for many species in the BBL database, although precision improved with higher re‐encounter probabilities and stronger migratory connectivity. We describe an approach to deal with the inherent biases in BBL banding and re‐encounter data to demonstrate that this large dataset is a valuable source of information about the migratory connectivity of the birds of North America. Understanding the biology and conducting effective conservation of migratory species requires knowledge of migratory connectivity, the geographic linkages of populations between stages of the annual cycle. Unfortunately, we are lacking this information for most migratory species. We demonstrate the use of available large‐scale banding and re‐encounter data to estimate migratory connectivity for North American breeding birds in a multistate recapture and recovery model that accounts for re‐encounter probabilities.

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal's movement path. 4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks. 5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.

Knowledge of band reporting is important for converting band encounter data into estimates of harvest probabilities, which can then be used to assess harvest management goals or estimate population size and other vital rates. Historical estimates of band reporting probabilities have come from reward-band studies or joint analysis of band recovery and harvest survey data, but there are long gaps between estimates, and most studies have focused exclusively on mallards (Anas platyrhynchos). We compiled 337 estimates of band reporting probabilities for North American waterfowl and conducted a Bayesian state-space analysis to provide a continuous time series of estimated reporting probability from 1948 to 2010. Band reporting probability increased sharply between 1996 and 2000 when toll-free phone numbers were added to band inscriptions and agencies implemented electronic methods for band reporting, but our analysis also identified gradual long-term trends in reporting probability throughout the time series. We found little evidence for among-species variation in reporting probability, but a few species that are widely regarded as trophies by waterfowl hunters (canvasbacks [Aythya valisineria], redheads [Aythya americana], and northern pintails [Anas acuta]) had higher historical reporting probabilities than mallards. We also found little evidence of geographic variation in reporting probabilities, although we confirmed lower reporting probabilities for eastern Canada. We recommend using our estimates of band reporting probabilities and their variances as informed priors in future analyses of band recovery data to fully embrace uncertainty about how this parameter affects estimates of other population parameters. Retrospective studies using parts collection data are needed to explore potential among-species variation in reporting probabilities during recent decades.

Due to the large climatic and geographical differences, the occurrence time of maximum 1-day precipitation (RM1day) in different zones of the Huaihe River Bain (HRB) is different. In this study, in order to analyze the characteristics of precipitation encounters, we used the mixture of von Mises distributions and Copula functions to analyze the occurrence time distribution, interval time distribution, and joint encounter probability of RM1day in the HRB. We divide the upstream area of HRB into Zone1, the midstream area into Zone2-1 and Zone2-2, and the downstream area into Zone3 and Zone4. The results show that the RM1day of Zone1, Zone2-1, Zone2-2, Zone3, and Zone4 are mainly concentrated on July 13, July 9, July 6, July 19, and July 14, respectively. The probability appearing RM1day of Zone1 first to Zone2-1, Zone1 first to Zone2-2, Zone2-2 first to Zone2-1, and Zone4 first to Zone3 is 50.84%, 40.0%, 49.1%, and 42.4%, respectively. It shows that most of the RM1day occurred in the southern sites of the HRB earlier than the northern sites. Zone1 and Zone2-2, Zone1 and Zone2-1, Zone2-2 and Zone2-1, and Zone4 and Zone3 have the highest encounter probability of RM1day in July, with 0.34%, 0.56%, 0.37%, and 0.42%, respectively. Therefore, we need to pay more focus to the risk of extreme precipitation encounter in July. This study provides an important reference for flood control and anti-logging in the HRB.

Based on the data series location water, Yellow River water, and the South-to-North Water Diversion water which were obtained from 1985 to 2017 in Zhengzhou, China, a joint probability distribution (JPD) model and encounter probability (EP) were established using the optimized t-copula function. The EP was combined, and a risk analysis was performed based on the water shortage situation of each user in different encounters. The results showed that (1) the trivariate JPD could reflect the probability of multisource water supply more precisely than the bivariate JPD; (2) the asynchronous EP was 76.6%, higher than the synchronous EP by 53.2%, indicating that the three water sources were complementary, and the local water exhibited the most significant impact on water supply system in Zhengzhou; and (3) the maximum EP for S1 was 11.7%, all users in S27 experienced varying degrees of water shortage, and the domestic water shortage was (−0.271, −0.33) billion m3. The reservoirs should be converted into joint dispatch, and water rights trading and other water resource management measures should be adopted. The amount of extracted groundwater may be increased if necessary to maintain the urban water supply system.

ABSTRACT Previous studies have examined individual-level factors influencing bird species knowledge or the popularity of species. Here, we analyse for the first time which bird species can be identified by the general population using knowledge tests and whether knowledge can be explained by ecological factors, species traits and social/cultural factors. Until now, there is no empirical evidence as to why some bird species are better known or more familiar to humans than others. We hypothesize that bird species with a higher probability of human encounters and high public appeal should be more familiar, and hence, better known. Further, species that visit feeders and species that are more represented in culture should be more familiar to the public. We constructed a bird species familiarity index based on 50 bird species presented to 3438 respondents from Germany. This was linked to different ecological and morphological species traits, encounter probability, species appeal and cultural representation (cultural representation). Encounter probability, species appeal and cultural representation were subjected to exploratory factor analyses and finally correlated with the familiarity index. We found medium to high correlations between species familiarity and appeal and cultural representation, respectively. No relationship was found with encounter probability. Finally, familiarity was positively correlated with eBird entries and scientific data from bird feeders. Typical bird feeder species were better known by the public. The study shows that appeal and cultural representation are the most important sources of bird familiarity in the public, but bird feeding also contributes to knowledge.

Ecological water replenishment (EWR) via interbasin water transfer projects has been regarded as a critical solution to reducing the risk of lake shrinkage and wetland degradation. The hydrological conditions of EWR water sources do not change synchronously, which may have an impact on the transferable water. Based on the GAMLSS model and the multivariate Copula model, this work presents a research approach for EWR via interbasin water transfer projects that can capture the non-stationarity of the runoff series and the frequency of dryness–wetness encounters, as well as speculates on various scenarios throughout the project operation phase. We present a case study on the Baiyangdian Lake, acting as the largest freshwater wetland in North China, which has suffered from severe degradation during the past decades and deserves thorough ecological restoration. The GAMLSS model was used to examine the non-stationarity characteristics of EWR water sources including the Danjiangkou Reservoir (DJK), the Huayuankou reach of the Yellow River (HYK), and upstream reservoirs (UR). The multivariate Copula model was implemented to evaluate the synchronous–asynchronous characteristics for hydrological probabilities for the multiple water sources. Results show that 1) significant non-stationarity has been detected for all water sources. Particularly, a significant decreasing trend has been found in UR and HYK. 2) The non-stationary model with time as the explanatory variable is more suitable for the runoff series of DJK, HYK, and UR. Under the non-stationary framework, the wet–dry classification of runoff series is completely changed. 3) Whether the bivariate or trivariate combination types, the asynchronous probability among the three water resources is over 0.6 except DJK-HYK, which indicates the complementary relationship. Multiple water resources are necessary for EWR. What is more, during a dry year of UR, the conditional probability that both DJK and HYK are in a dry year is 0.234. To alleviate the problem of not having enough water, some additional water resources and an acceptable EWR plan are required.

This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar track data. To achieve these objectives, two figures of merit were proposed: System Dynamic Range and System Tuning Envelope. To examine the conflict detection’s performance variability under different uncertainty levels and predictor types, simple multi-horizon trajectory predictors trained with two machine learning techniques of different characteristics are assessed at various look-ahead times: extreme gradient boosting with a discrete nature and a multi-layer perceptron regressor with a continuous nature. The results highlight the interdependence between the performances of the trajectory predictor and the conflict detector, and the quantification of this relationship can be represented through a sigmoid function. In addition, the two proposed figures of merit are effective for selecting suitable operating points in an adaptive conflict detector, based on dynamic thresholds and the performance requirements necessary for the trajectory predictors to achieve the expected detection performance at different look-ahead time.

Opportunistic networks are self-organizing networks that do not require a complete path between the source node and the destination node as it uses encounter opportunities brought by nodes movement to achieve network communication. Opportunistic networks routing algorithms are numerous and can be roughly divided into four categories based on different forwarding strategies. The Prophet routing algorithm is an important routing algorithm in opportunistic networks. It forwards messages based on the encounter probability between nodes, and has good innovation significance and optimization potential. However, the Prophet routing algorithm does not consider the impact of the historical throughput of the node on message transmission, nor does it consider the impact of the encounter duration between nodes on message transmission. Therefore, to improve the transmission efficiency of opportunistic networks, this paper based on the Prophet routing algorithm, fuses the impact of the historical throughput of the node and the encounter duration between nodes on message transmission at the same time, and proposes the Prophet_TD routing algorithm based on the historical throughput and the encounter duration. This paper uses the Opportunistic Networks Environment v1.6.0 (the ONE v1.6.0) as the simulation platform, controls the change of running time and the number of nodes respectively, conducts simulation experiments on the Prophet_TD routing algorithm. The simulation results show that compared to the traditional Prophet routing algorithm, on the whole, the Prophet_TD routing algorithm has a higher message delivery rate and a lower network overhead rate, and its average latency is also lower when node density is large.

Wireless Sensor Networks (WSNs) have become essential parts in various smart city projects. However, the application-specific WSN deployment is constrained by its high cost, low flexibility and hard management. To address these limitations, a complementary promising solution, known as mobile crowdsensing, is proposed. Mobile crowdsensing leverages the surge of mobile devices as well as the sensors attached to them to opportunistically and cooperatively conduct sensing tasks. Thanks to the crowdness and mobility of mobile devices, mobile crowdsensing is able to enlarge the sensing scale and granularity. Existing mobile crowdsensing techniques are usually centralized methods and rely on infrastructure communications. Witnessing the development of Device-to-Device (D2D) communications, it is ideal to explore such abilities such that the sensing tasks can be conducted in a distributed manner as well as an infrastructureless way. Via D2D, all participated nodes can directly assign tasks to encountered nodes. In this paper, aided by the encounter relationship among mobile nodes, we study the time minimization task assignment problem in mobile crowdsensing. Specially, we propose offline and online algorithms based on historic encounter information and real-time assigned task execution time, respectively. Real-world trace based experiments validate the efficiency of our proposal.