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Abstract Integrated step-selection analyses (iSSAs) are versatile and powerful frameworks for studying habitat and movement preferences of tracked animals. iSSAs utilize integrated step-selection functions (iSSFs) to model movements in discrete time, and thus, require animal location data that are regularly spaced in time. However, many real-world datasets are incomplete due to tracking devices failing to locate an individual at one or more scheduled times, leading to slight irregularities in the duration between consecutive animal locations. To address this issue, researchers typically only consider bursts of regular data (i.e., sequences of locations that are equally spaced in time), thereby reducing the number of observations used to model movement and habitat selection. We reassess this practice and explore four alternative approaches that account for temporal irregularity resulting from missing data. Using a simulation study, we compare these alternatives to a baseline approach where temporal irregularity is ignored and demonstrate the potential improvements in model performance that can be gained by leveraging these additional data. We also showcase these benefits using a case study on a spotted hyena (Crocuta crocuta).

Abstract Background All behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed ‘dynamic body acceleration’ (DBA). However, converting acceleration proxies (m/s2) to standard units (watts) involves costly in-lab respirometry measurements, and there is a lack of viable substitutes for empirical calibration relationships when these are unavailable. Methods We used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting ‘power calibration equation’ to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber’s law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population. Results ACTIWAKE’s DEE estimates did not differ significantly from DLW (t(19) = − 1.25; P = 0.22), while ACTIREST24’s estimates did (t(19) = − 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1–2% of mean) was lower than that in DLW (s.d. 33% of mean). Conclusions We found that linear locomotion-based calibration applied to the waking period, and a ‘flat’ resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.

Abstract Unbiased mortality estimates are fundamental for testing ecological and evolutionary theory as well as for developing effective conservation actions. However, mortality estimates are often confounded by dispersal, especially in studies where dead‐recovery is not possible. In such instances, missing individuals (i.e. individuals with unobserved time of death) may have died or permanently emigrated from a study area, making inferences about their fate difficult. Mortality before and during dispersal, as well as the decision to disperse, usually depend on a suite of individual, social and environmental covariates, which in turn can be used to draw conclusions about the fate of missing individuals. Here, we propose a Bayesian hierarchical model that takes into account time‐varying covariates to estimate transitions between life‐history states and mortality in each state using mark‐resighting data with missing individuals. Specifically, our framework estimates mortality rates in two states (resident and dispersing state) by treating the fate of missing individuals as a latent (i.e. unobserved) variable that is statistically inferred based on information from individuals with a known fate and given the individual, social and environmental conditions at the time of disappearance. Our model also estimates rates of state transition (i.e. emigration) to assess whether a missing individual was more likely to have died or survived due to unobserved emigration from the study area. We used simulations to check the validity of our model and assessed its performance with data of varying degrees of uncertainty. Our modelling framework provided accurate mortality and emigration estimates for simulated data of different sample sizes, proportions of missing individuals, and resighting intervals. Variation in sample size appeared to affect the precision of estimated parameters the most. Our approach offers a solution to estimating unbiased mortality of both resident and dispersing individuals as well as the probability of emigration using mark‐resighting data with incomplete death records. Conditional on the availability of data on known‐fate individuals and relevant time‐varying covariates, our model can reconstruct the fate (death or emigration) of missing individuals. The modularity of our framework allows mortality analyses to be tailored to a variety of species‐specific life histories.

Background: To optimize results reporting after penile cancer (PC) surgery, we proposed a Tetrafecta and assessed its ability to predict overall survival (OS) probabilities. Methods: A purpose-built multicenter, multi-national database was queried for stage I–IIIB PC, requiring inguinal lymphadenectomy (ILND), from 2015 onwards. Kaplan–Meier (KM) method assessed differences in OS between patients achieving Tetrafecta or not. Univariable and multivariable regression analyses identified its predictors. Results: A total of 154 patients were included in the analysis. The 45 patients (29%) that achieved the Tetrafecta were younger (59 vs. 62 years; p = 0.01) and presented with fewer comorbidities (ASA score ≥ 3: 0% vs. 24%; p < 0.001). Although indicated, ILND was omitted in 8 cases (5%), while in 16, a modified template was properly used. Although median LNs yield was 17 (IQR: 11–27), 35% of the patients had Tetrafecta cohort displayed significantly higher OS probabilities (Log Rank = 0.01). Uni- and multivariable logistic regression analyses identified age as the only independent predictor of Tetrafecta achievement (OR: 0.97; 95%CI: 0.94–0.99; p = 0.04). Conclusions: Our Tetrafecta is the first combined outcome to comprehensively report results after PC surgery. It is widely applicable, based on standardized and reproducible variables and it predicts all-cause mortality.

Sexual disorders following retroperitoneal pelvic lymph node dissection (RPLND) for testis tumor can affect the quality of life of patients. The aim of the current study was to investigate several different andrological outcomes, which may be influenced by robot-assisted (RA) RPLND. From January 2012 to March 2020, 32 patients underwent RA-RPLND for stage I nonseminomatous testis cancer or postchemotherapy (PC) residual mass. Modified unilateral RPLND nerve-sparing template was always used. Major variables of interest were erectile dysfunction (ED), premature ejaculation (PE), dry ejaculation (DE), or orgasm alteration. Finally, fertility as well as the fecundation process (sexual intercourse or medically assisted procreation [MAP]) was investigated. Ten patients (31.3%) presented an andrological disorder of any type after RA-RPLND. Hypospermia was present in 4 (12.5%) patients, DE (International Index of Erectile Function-5 [IIEF-5]

The snow leopard (Panthera uncia) is one of the world's most elusive felids. In Bhutan, which is one of the 12 countries where the species still persists, reliable information on its distribution and habitat suitability is lacking, thus impeding effective conservation planning for the species. To fill this knowledge gap, we created a country-wide species distribution model using “presence-only” data from 420 snow leopard occurrences (345 from a sign survey and 77 from a camera-trapping survey) and 12 environmental covariates consisting of biophysical and anthropogenic factors. We analyzed the data in an ensemble model framework which combines the outputs from several species distribution models. To assess the adequacy of Bhutan's network of protected areas and their potential contribution toward the conservation of the species, we overlaid the output of the ensemble model on the spatial layers of protected areas and biological corridors. The ensemble model identified 7,206 km2 of Bhutan as suitable for the snow leopard: 3,647 km2 as highly suitable, 2,681 km2 as moderately suitable, and 878 km2 as marginally suitable. Forty percent of the total suitable habitat consisted of protected areas and a further 8% of biological corridors. These suitable habitats were characterized by a mean livestock density of 1.3 individuals per hectare, and a mean slope of 25°; they closely match the distribution of the snow leopard's main wild prey, the bharal (Pseudois nayaur). Our study shows that Bhutan's northern protected areas are a centre for snow leopard conservation both at the national and regional scale.

While climate change has been shown to impact several life‐history traits of wild‐living animal populations, little is known about its effects on dispersal and connectivity. Here, we capitalize on the highly variable flooding regime of the Okavango Delta to investigate the impacts of changing environmental conditions on the dispersal and connectivity of the endangered African wild dog (Lycaon pictus). Based on remote sensed flood extents observed over 20 years, we derive two extreme flood scenarios: a minimum and a maximum flood extent, representative of very dry and very wet environmental periods. These conditions are akin to those anticipated under increased climatic variability, as it is expected under climate change. Using a movement model parameterized with GPS data from dispersing individuals, we simulate 12,000 individual dispersal trajectories across the ecosystem under both scenarios and investigate patterns of connectivity. Across the entire ecosystem, surface water coverage during maximum flood extent reduces dispersal success (i.e., the propensity of individuals to disperse between adjacent subpopulations) by 12% and increases dispersal durations by 17%. Locally, however, dispersal success diminishes by as much as 78%. Depending on the flood extent, alternative dispersal corridors emerge, some of which in the immediate vicinity of human‐dominated landscapes. Notably, under maximum flood extent, the number of dispersing trajectories moving into human‐dominated landscapes decreases by 41% at the Okavango Delta's inflow, but increases by 126% at the Delta's distal end. This may drive the amplification of human–wildlife conflict. While predicting the impacts of climate change on environmental conditions on the ground remains challenging, our results highlight that environmental change may have significant consequences for dispersal patterns and connectivity, and ultimately, population viability. Acknowledging and anticipating such impacts will be key to effective conservation strategies and to preserve vital dispersal corridors in light of climate change and other human‐related landscape alterations. [ABSTRACT FROM AUTHOR]

Abstract Background Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors. Methods We address this by first leveraging magnetometers’ similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth’s local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition. Results The overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture. Conclusions Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor’s strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.

Introduction: Radical cystectomy (RC) is the gold standard treatment for patients with muscle-invasive or refractory non-muscle invasive bladder cancer. It is estimated that approximately 64% and 13% of RC patients experience any complication and major complications, respectively. Specialized immunonutrition (SIM) aims to reduce the rates of complications after RC. We reported surgical complication rates in RC patients who received (SIM group) versus who did not receive (no-SIM group) perioperative SIM. Moreover, we investigated factors associated with complications after RC. Material and Methods: This is a retrospective cohort study of 52 patients who underwent RC between April 2016 and December 2017. Overall, 26 (50%) patients received perioperative SIM. We recorded age, gender, Charlson Comorbidity Index (CCI), body mass index (BMI), Malnutrition Universal Screening Tool (MUST) score, unintentional weight loss (UWL), SIM drinks consume, surgical approach, urinary diversion, neoadjuvant chemotherapy (NAC), use of total parenteral nutrition (TPN), final pathology, length of stay (LOS), and complications. Results: SIM was associated with higher rates of documented infections ( P = .03). Conversely, post-operative ileus was associated with higher rates of overall infections ( P = .03). Median LOS was comparable within the 2 groups. Overall, 4 (15.38%) versus 0 (0%) patients in SIM versus no-SIM group were readmitted to hospital ( P = .03). Age, CCI, NAC, and TPN were not associated with complication rates. Conclusions: SIM is not associated with lower rates of post-operative complications in RC candidates. Moreover, higher rates of documented infections were observed in the SIM group. Patients with post-operative ileus experienced more infections. Age, CCI, NAC, and TPN were not predictive of complications.

Background: Inguinal lymphadenectomy remains the gold standard for the treatment of inguinal lymph node metastases from penile carcinoma, melanoma, Merkel cell carcinoma, and squamous cell carcinoma (SCC). This procedure is associated with significant complications. In order to reduce morbidity, minimally invasive approaches have been described. We report our preliminary experience with robot-assisted inguinal lymphadenectomies (RAIL). Methods: RAIL was performed according to the Sotelo technique. When indicated, a robot-assisted pelvic lymphadenectomy (RAPLND) was performed. We recorded age, sex, comorbidities, baseline oncologic diagnosis, operative time, hospital stay, lymph node yield, complications, time to drain removal, and oncologic outcomes. Results: From December 2016 to February 2019, 13 patients underwent RAIL. Median age was 65 years (range: 31–85 years). Primary malignancy was melanoma in five patients, Merkel cell carcinoma in four, dermal duct tumor in one, penile cancer in two, and SCC in one. RAIL was monolateral in 12 cases and bilateral in 1 case. A total of 10 monolateral RAPLNDs were performed; median operative time was 279 min (range: 169–320). Median lymph nodes yield was 11 (range: 2–24) for monolateral RAIL and 9 for monolateral RAPLND (range 2–24). Median hospital stay was 4 days (range: 2–5). No procedure was converted to open. Median follow up was 16 months (range: 5–31). Five Clavien-Dindo grade I complications were recorded. Median time to drain removal was 32.5 days (range 7–65). Three recurrences and two cancer-related deaths were recorded. Conclusions: RAIL is feasible and associated with a short hospital stay, with little incidence of perioperative complications.

Understanding dispersal patterns and demographic processes is crucial for the development of evidence-based conservation practices. Obtaining such information relies on the ability to identify and track individuals across spatial and temporal scales relevant to the life-history events under investigation. This knowledge can be achieved by combining photographic and sighting data collected by various sources with a high accuracy automated individual identification platform. Here, we present the African Carnivore Wildbook (ACW), an AI-based graphical user interface tool capable of identifying individuals of several African carnivore species and specifically developed to accommodate the above outlined needs. We showcase the ACW functionality using the endangered African wild dog as an example. Pictures collected over an area >56,000 km² and submitted to ACW allowed inferences on movement patterns and dispersal at regional and international scales; for instance, transboundary dispersal events >200 km were documented. ACW furthermore enabled monitoring some individuals for >4 years; such information is invaluable for reliable survival analyses. We discuss how the ACW can contribute to data collection at appropriate spatial and temporal scales to support population monitoring, scientific research and management of African wild dogs and other apex carnivores and to the conservation of these charismatic species. [ABSTRACT FROM AUTHOR]

The size of the home range of a mammal is affected by numerous factors. However, in the normally solitary, but polygynous, Leopard (Panthera pardus), home range size and maintenance is complicated by their transitory social grouping behavior, which is dependent on life history stage and/or reproductive status. In addition, the necessity to avoid competition with conspecifics and other large predators (including humans) also impacts upon home range size. We used movement data from 31 sites across Africa, comprising 147 individuals (67 males and 80 females) to estimate the home range sizes of leopards. We found that leopards with larger home ranges, and in areas with more vegetation, spent longer being active and generally traveled faster, and in straighter lines, than leopards with smaller home ranges. We suggest that a combination of bottom-up (i.e. preferred prey availability), top-down (i.e. competition with conspecifics), and reproductive (i.e. access to mates) factors likely drive the variability in Leopard home range sizes across Africa. However, the maintenance of a large home range is energetically expensive for leopards, likely resulting in a complex evolutionary trade-off between the satisfaction of basic requirements and preventing potentially dangerous encounters with conspecifics, other predators, and people. [ABSTRACT FROM AUTHOR]

ObjectiveTo identify trends of patients' urinary and sexual dysfunctions from a clinical and psychological perspective and understand whether sociodemographic and medical predictors could differentiate among patients following different one-year longitudinal trajectories.MethodsAn Italian sample of 478 prostate cancer patients undergone Robot-Assisted Radical Prostatectomy completed the EPIC-26 survey between July 2015 and July 2016 at the pre-hospitalization (T0), 45 days (T1) and 3 (T2), 6 (T3), 9 (T4), and 12 months (T5) after surgery. Sociodemographic and clinical characteristics (age, BMI, diabetes, nerve-sparing procedure) were also collected. Latent Class Growth Analysis was conducted separately for sexual dysfunction and urinary incontinence EPIC-26 subscales. The association between membership in the two longitudinal trajectories of urinary and sexual dysfunctions was assessed by considering Chi-square test and its related contingency table.ResultsPeople who have a high level of urinary incontinence at T1 are likely to have a worse recovery. Age, BMI and pre-surgical continence may affect the level of incontinence at T1 and the recovery trajectories. Patients with low and moderate sexual problems at T1 can face a moderate linear recovery, while people with high level of impotence immediately after surgery may take a longer period to solve sexual dysfunctions. Age and the pre-surgical sexual condition may impact the recovery. Finally, a great proportion of patients reported both steady problems in sexual function and constant high levels of urinary incontinence over time.ConclusionsThis study highlights different categories of patients at risk who may be important to know in order to develop personalized medical pathways and predictive models in a value-based healthcare.

Background: circulating levels of lymphocytes, platelets and neutrophils have been identified as factors related to unfavorable clinical outcome for many solid tumors. The aim of this cohort study is to evaluate and validate the use of the Prostatic Systemic Inflammatory Markers (PSIM) score in predicting and improving the detection of clinically significant prostate cancer (csPCa) in men undergoing robotic radical prostatectomy for low-risk prostate cancer who met the inclusion criteria for active surveillance. Methods: we reviewed the medical records of 260 patients who fulfilled the inclusion criteria for active surveillance. We performed a head-to-head comparison between the histological findings of specimens after radical prostatectomy (RP) and prostate biopsies. The PSIM score was calculated on the basis of positivity according to cutoffs (neutrophil-to-lymphocyte ratio (NLR) 2.0, platelets-to-lymphocyte ratio (PLR) 118 and monocyte-to-lymphocyte-ratio (MLR) 5.0), with 1 point assigned for each value exceeding the specified threshold and then summed, yielding a final score ranging from 0 to 3. Results: median NLR was 2.07, median PLR was 114.83, median MLR was 3.69. Conclusion: we found a significantly increase in the rate of pathological International Society of Urological Pathology (ISUP) ≥ 2 with the increase of PSIM. At the multivariate logistic regression analysis adjusted for age, prostate specific antigen (PSA), PSA density, prostate volume and PSIM, the latter was found the sole independent prognostic variable influencing probability of adverse pathology.