Direct and indirect effects of environmental drivers on reindeer reproduction

The impact of climate change on the dynamics of populations has been well documented and is widespread. However, weather variability influences populations both directly and indirectly, and is mediated by species interactions. This complexity may impede proper climate impact assessments. Hence, predicting the consequences of climate change may require including processes that occur both with time lags and across trophic levels. Based on our current understanding of the mechanisms linking local climate and trophic interactions in tundra ecosystems, we used a state-space formulation of a mediation model that allowed for assessing the relative contribution of direct and indirect environmental (weather and trophic) effects on reindeer Rangifer tarandus reproductive success. Our study showed that the mediator effect of body condition caused delayed but predictable effects of weather, plant productivity, and reindeer densities on reproductive success. Furthermore, these predictors also affected reproductive success directly and with the same sign, suggesting that direct and indirect effects pulled in the same direction with respect to their combined total effect on reproductive success. Hence, poor weather conditions not only affect calf production negatively the same year, but also increase the likelihood of poor reproductive success the subsequent year. The results support the expectation that calf slaughter mass (as a proxy for herd body condition) is an important indicator of the state of reindeer herds with respect to their production potential and resilience to weather events and climate change. Finally, the model framework employed in the present study can be further developed as a potential vehicle for near-term forecasting, and thereby constitutes a useful tool for adaptive management

Iterative model predictions for wildlife populations impacted by rapid climate change

To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005–2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach

Food web approach for managing Arctic wildlife populations in an era of rapid environmental change

Scientists and wildlife managers implementing adaptive monitoring and management schemes, are tasked with providing predictions of population responses to harvest and environmental changes. Such predictions are useful not only to forecast direct effects of climate, productivity, land use, or habitat degradation, but also changes in the food web, such as expanding/increasing species that are predators, prey, and competitors of populations of concern. Explicit consideration of food webs and their dynamics in more complex models could provide better predictions of future changes, and allow us to better assess the influence of management actions. Here, we present our perspective on what we have learned from conducting a number of case studies using such a food web approach with a focus on climate and harvest impacts and their implications for management. We found empirical support for many of our hypothesized food web effects, and were able in some cases to obtain short-term forecasts with slightly lower prediction error using models that account for food web dynamics compared with simpler models. Predictions are the foundation of adaptive management because they allow quantitative assessment of the effects of management actions; however, evaluating predictions requires adequate and high-quality monitoring data. Results from our case studies show that a combination of long-term monitoring and different types of study designs coupled with models of adequate complexity are likely required to better understand populations’ responses to environmental changes and harvest, as well as the consequences for food webs

Life-cycle analysis of an endangered migratory goose to assess the impact ofconservation actions on population recovery

Evaluating the effectiveness of conservation actions is challenging for migratory species because a population can be impacted anywhere along its route. Conservation actions for the critically endangered Fennoscandian lesser white-fronted goose population include culling of red foxes in the breeding area and habitat improvements and reduction of illegal hunting in the non-breeding areas. One goal of the predator control strategy is to prevent adult birds from using an autumn migration route through western Asia, where mortality is believed to be higher than on the migration route through eastern Europe. We used 23 years of count data obtained at different staging areas to parameterize a seasonal state-space model describing the full-annual cycle dynamics of this population and evaluate whether the recent population recovery was linked to these conservation efforts. The results did not provide evidence that predator control influenced population recovery, as survival on the European route did not appear higher than on the allegedly riskier Asian route. However, adult survival at staging areas on both routes and at wintering sites may have improved in the last decade, suggesting a positive effect of the other conser- vation initiatives. These results emphasize the importance of including the non-breeding dynamics in population assessments of migratory species and highlight the challenge of evaluating the efficacy of separate conservation actions when a proper experimental design is unfeasible. Our study, which is a unique case of cross-national, coordinated conservation efforts, exemplifies how to model complex population dynamics to assess the influ- ence of costly conservation initiatives. Goose management State-space model Management evaluation Lesser whitefronted goose Unmarked individuals Non-breeding dynamic

A retrospective multicentric observational study of trastuzumab emtansine in HER2 positive metastatic breast cancer: A real-world experience

We addressed trastuzumab emtansine (T-DM1) efficacy in HER2+ metastatic breast cancer patients treated in real-world practice, and its activity in pertuzumab-pretreated patients. We conducted a retrospective, observational study involving 23 cancer centres, and 250 patients. Survival data were analyzed by Kaplan Meier curves and log rank test. Factors testing significant in univariate analysis were tested in multivariate models. Median follow-up was 15 months and median T-DM1 treatment-length 4 months. Response rate was 41.6%, clinical benefit 60.9%. Median progression-free and median overall survival were 6 and 20 months, respectively. Overall, no differences emerged by pertuzumab pretreatment, with median progression-free and median overall survival of 4 and 17 months in pertuzumab-pretreated (p=0.13), and 6 and 22 months in pertuzumab-na\uc3\uafve patients (p=0.27). Patients who received second-line T-DM1 had median progression-free and median overall survival of 3 and 12 months (p=0.0001) if pertuzumab-pretreated, and 8 and 26 months if pertuzumab-na\uc3\uafve (p=0.06). In contrast, in third-line and beyond, median progression-free and median overall survival were 16 and 18 months in pertuzumab-pretreated (p=0.05) and 6 and 17 months in pertuzumab-na\uc3\uafve patients (p=0.30). In multivariate analysis, lower ECOG performance status was associated with progression-free survival benefit (p < 0.0001), while overall survival was positively affected by lower ECOG PS (p < 0.0001), absence of brain metastases (p 0.05), and clinical benefit (p < 0.0001). Our results are comparable with those from randomized trials. Further studies are warranted to confirm and interpret our data on apparently lower T-DM1 efficacy when given as second-line treatment after pertuzumab, and on the optimal sequence order

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Data_FM

Zip folder containing datasets and R code to reproduce the analysis of our paper

Understanding and forecasting population dynamics in changing arctic ecosystems

Rapid environmental changes are currently occurring in the Arctic because of global warming. Arctic food webs can exhibit complex dynamics because of the prevalence of tight interactions between trophic levels. Climate change impacts can therefore propagate across food webs and result in non-trivial indirect effects on arctic species and populations. In this thesis, constituted by four papers, I address the general issue of how rapid climate change and other environmental stressors affect the population dynamics of arctic species of management concern. I focused on three target species: the Svalbard rock ptarmigan, the willow ptarmigan, and the lesser white-fronted goose. I based my investigation on long-term time series available for both the study populations and linked ecosystem components. I aimed to infer general ecological mechanisms driving population dynamics of arctic species facing climate change, but also provide recommendations for improved monitoring and management of the study populations. I developed a-priori conceptual models describing climate and management impacts and converted them into advanced statistical models. I found that major changes in winter climate in terms of winter temperature seem to have driven the recent increase in the population of Svalbard rock ptarmigan, while intensified outbreaks of insect pests and delayed onset of winter may have contributed to the decline of willow ptarmigan populations. As stakeholders were interested in having predictions of population density to adapt harvest strategies, I tested the ability of different models to provide near-term forecasts of ptarmigan density. I also found no evidence that predator control contributed to the recent recovery of the endangered Fennoscandian population of lesser white-fronted goose, and that goose breeding success is primarily influenced by population cycles of small rodent species. My thesis constitutes a compelling example of how a holistic approach incorporating food web dynamics can improve our understanding of the multifaceted impacts of environmental changes and aid the management of populations subjected to rapid climate changes

End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers

Sustainable management of wildlife populations can be aided by building models that both identify current drivers of natural dynamics and provide near-term predictions of future states. We employed a Strategic Foresight Protocol (SFP) involving stakeholders to decide the purpose and structure of a dynamic state-space model for the population dynamics of the Willow Ptarmigan, a popular game species in Norway. Based on local knowledge of stakeholders, it was decided that the model should include food web interactions and climatic drivers to provide explanatory predictions. Modeling confirmed observations from stakeholders that climate change impacts Ptarmigan populations negatively through intensified outbreaks of insect defoliators and later onset of winter. Stakeholders also decided that the model should provide anticipatory predictions. The ability to forecast population density ahead of the harvest season was valued by the stakeholders as it provides the management extra time to consider appropriate harvest regulations and communicate with hunters prior to the hunting season. Overall, exploring potential drivers and predicting short-term future states, facilitate collaborative learning and refined data collection, monitoring designs, and management priorities. Our experience from adapting a SFP to a management target with inherently complex dynamics and drivers of environmental change, is that an open, flexible, and iterative process, rather than a rigid step-wise protocol, facilitates rapid learning, trust, and legitimacy. climate change; decision-making; food web; harvesting; near-term forecasting; population cycles; stakeholders; strategic foresight.publishedVersio