Insect decline: immediate action is needed.

Insects appeared more than 400 million years ago and they represent the richest and most diverse taxonomic group with several million species. Yet, under the combined effect of the loss of natural habitats, the intensification of agriculture with massive use of pesticides, global warming and biological invasions, insects show alarming signs of decline. Although difficult to quantify, species extinction and population reductions are confirmed for many ecosystems. This results in a loss of services such as the pollination of plants, including food crops, the recycling of organic matter, the supply of goods such as honey and the stability of food webs. It is therefore urgent to halt the decline of Insects. We recommend implementing long-term monitoring of populations, tackling the causes of insect decline by reducing the use of synthetic insecticides, preserving natural habitats, and reinventing a positive relationship between humans and insects.

Apparus il y a plus de 400 millions d'années, les Insectes représentent le groupe taxonomique le plus riche et diversifié, avec plusieurs millions d'espèces. Sous l'effet de la disparition des habitats, de l'intensification de l'agriculture avec l'usage massif des pesticides, du réchauffement climatique et des invasions biologiques, les Insectes montrent des signes alarmants de déclin. Bien que difficiles à quantifier, la disparition des espèces et la réduction de leurs populations sont avérées et communes à de nombreux écosystèmes. Elles se traduisent par une perte des services rendus, comme la pollinisation des plantes vivrières, le recyclage de la matière organique, la fourniture de biens comme le miel, et l'équilibre des réseaux trophiques. Il est donc urgent de freiner le déclin des Insectes. Pour cela, il faut mettre en œuvre des suivis à long terme des populations, réduire l'usage des insecticides de synthèse, préserver les habitats naturels, et réinventer la relation de l'Homme à l'Insecte en revalorisant son image et ses usages.

An overview of the 2017 report of the French academy of Sciences on biodiversity.

In the present context of concerns for biodiversity, the French Academy of Sciences produced in 2017 a report entitled "Mechanisms of adaptation of biodiversity to climate change and their limits". We briefly review here the production process and structure of the report, and summarize its conclusions and recommendations. The conclusions emphasize the role of habitat fragmentation in the expected impact of climate change on biodiversity, in particular for organisms with limited dispersal abilities, and the disparities in species responses which must be taken to understand the future of species assemblages ("communities") under different scenarios of climate change. The recommendations cover the organization of biodiversity research and monitoring (development of observatories, key role of embedded time scales and modeling, integration of Human and Social Sciences), as well as critical domains such as Human, animal and plant health, agriculture and forestry policies, and management of the Environment.

Data gaps and opportunities for comparative and conservation biology.

Biodiversity loss is a major challenge. Over the past century, the average rate of vertebrate extinction has been about 100-fold higher than the estimated background rate and population declines continue to increase globally. Birth and death rates determine the pace of population increase or decline, thus driving the expansion or extinction of a species. Design of species conservation policies hence depends on demographic data (e.g., for extinction risk assessments or estimation of harvesting quotas). However, an overview of the accessible data, even for better known taxa, is lacking. Here, we present the Demographic Species Knowledge Index, which classifies the available information for 32,144 (97%) of extant described mammals, birds, reptiles, and amphibians. We show that only 1.3% of the tetrapod species have comprehensive information on birth and death rates. We found no demographic measures, not even crude ones such as maximum life span or typical litter/clutch size, for 65% of threatened tetrapods. More field studies are needed; however, some progress can be made by digitalizing existing knowledge, by imputing data from related species with similar life histories, and by using information from captive populations. We show that data from zoos and aquariums in the Species360 network can significantly improve knowledge for an almost eightfold gain. Assessing the landscape of limited demographic knowledge is essential to prioritize ways to fill data gaps. Such information is urgently needed to implement management strategies to conserve at-risk taxa and to discover new unifying concepts and evolutionary relationships across thousands of tetrapod species.

Slow recovery from a disease epidemic in the spotted hyena, a keystone social carnivore.

Predicting the impact of disease epidemics on wildlife populations is one of the twenty-first century's main conservation challenges. The long-term demographic responses of wildlife populations to epidemics and the life history and social traits modulating these responses are generally unknown, particularly for K-selected social species. Here we develop a stage-structured matrix population model to provide a long-term projection of demographic responses by a keystone social predator, the spotted hyena, to a virulent epidemic of canine distemper virus (CDV) in the Serengeti ecosystem in 1993/1994 and predict the recovery time for the population following the epidemic. Using two decades of longitudinal data from 625 known hyenas, we demonstrate that although the reduction in population size was moderate, i.e., the population showed high ecological 'resistance' to the novel CDV genotype present, recovery was slow. Interestingly, high-ranking females accelerated the population's recovery, thereby lessening the impact of the epidemic on the population.

Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty.

Estimating eco-epidemiological parameters in free-ranging populations can be challenging. As known individuals may be undetected during a field session, or their health status uncertain, the collected data are typically "imperfect". Multi-event capture-mark-recapture (MECMR) models constitute a substantial methodological advance by accounting for such imperfect data. In these models, animals can be "undetected" or "detected" at each time step. Detected animals can be assigned an infection state, such as "susceptible" (S), "infected" (I), or "recovered" (R), or an "unknown" (U) state, when for instance no biological sample could be collected. There may be heterogeneity in the assignment of infection states, depending on the manifestation of the disease in the host or the diagnostic method. For example, if obtaining the samples needed to prove viral infection in a detected animal is difficult, this can result in a low chance of assigning the I state. Currently, it is unknown how much uncertainty MECMR models can tolerate to provide reliable estimates of eco-epidemiological parameters and whether these parameters are sensitive to heterogeneity in the assignment of infection states. We used simulations to assess how estimates of the survival probability of individuals in different infection states and the probabilities of infection and recovery responded to (1) increasing infection state uncertainty (i.e., the proportion of U) from 20 to 90%, and (2) heterogeneity in the probability of assigning infection states. We simulated data, mimicking a highly virulent disease, and used SIR-MECMR models to quantify bias and precision. For most parameter estimates, bias increased and precision decreased gradually with state uncertainty. The probabilities of survival of I and R individuals and of detection of R individuals were very robust to increasing state uncertainty. In contrast, the probabilities of survival and detection of S individuals, and the infection and recovery probabilities showed high biases and low precisions when state uncertainty was >50%, particularly when the assignment of the S state was reduced. Considering this specific disease scenario, SIR-MECMR models are globally robust to state uncertainty and heterogeneity in state assignment, but the previously mentioned parameter estimates should be carefully interpreted if the proportion of U is high.

Pacific Decadal and El Niño oscillations shape survival of a seabird.

Understanding and modeling population change is urgently needed to predict effects of climate change on biodiversity. High trophic-level organisms are influenced by fluctuations of prey quality and abundance, which themselves may depend on climate oscillations. Modeling effects of such fluctuations is challenging because prey populations may vary with multiple climate oscillations occurring at different time scales. The analysis of a 28-yr time series of capture-recapture data of a tropical seabird, the Nazca Booby (Sula granti), in the Galápagos, Ecuador, allowed us to test for demographic effects of two major ocean oscillations occurring at distinct time-scales: the inter-annual El Niño Southern Oscillation (ENSO) and inter-decadal oscillations. As expected for a tropical seabird, survival of fledgling birds was highly affected by extreme ENSO events; by contrast, neither recruitment nor breeding participation were affected by either ENSO or decadal oscillations. More interesting, adult survival, a demographic trait that canalizes response to environmental variations, was unaffected by inter-annual ENSO oscillations yet was shaped by the Pacific Decadal Oscillation and small pelagic fish regime. Adult survival decreased during oceanic conditions associated with higher breeding success, an association probably mediated in this species by costs of reproduction that reduce survival when breeding attempts end later. To our knowledge, this is the first study suggesting that survival of a vertebrate can be vulnerable to a natural multidecadal oscillation.

Social status mediates the fitness costs of infection with canine distemper virus in Serengeti spotted hyenas.

The extent to which the fitness costs of infection are mediated by key life-history traits such as age or social status is still unclear. Within populations, individual heterogeneity in the outcome of infection is the result of two successive processes; the degree of contact with the pathogen (exposure) and the immune response to infection. In social mammals, because individuals holding high social status typically interact more frequently with group members, they should be more often in contact with infected individuals than those of low social status. However, when access to resources is determined by social status, individuals with a high social status are often better nourished, have a greater opportunity to allocate resources to immune processes and therefore should have a smaller chance of succumbing to infection than individuals with low social status.We investigated the risk and fitness costs of infection during a virulent epidemic of canine distemper virus (CDV) in a social carnivore, the spotted hyena, in the Serengeti National Park. We analysed two decades of detailed life-history data from 625 females and 816 males using a multi-event capture-mark-recapture model that accounts for uncertainty in the assignment of individual infection states.Cubs of mothers with a high social status had a lower probability of CDV infection and were more likely to survive infection than those with low social status. Subadult and adult females with high social status had a higher infection probability than those with low social status. Subadult females and pre-breeder males that had recovered from CDV infection had a lower survival than susceptible ones.Our study disentangles the relative importance of individual exposure and resource allocation to immune processes, demonstrates fitness costs of infection for juveniles, particularly for those with low social status, shows that patterns of infection can be driven by different mechanisms among juveniles and adults and establishes a negative relationship between infection and fitness in a free-ranging mammal. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.13059/suppinfo is available for this article.

Partitioning prediction uncertainty in climate-dependent population models.

The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate-demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose (Chen caeruslescens atlantica), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.

Year-round effects of climate on demographic parameters of an arctic-nesting goose species.

Understanding how climate change will affect animal population dynamics remains a major challenge, especially in long-distant migrants exposed to different climatic regimes throughout their annual cycle. We evaluated the effect of temperature throughout the annual cycle on demographic parameters (age-specific survival and recruitment, breeding propensity and fecundity) of the greater snow goose (Chen caerulescens atlantica L.), an arctic-nesting species. As this is a hunted species, we used the theory of exploited populations to estimate hunting mortality separately from natural mortality in order to evaluate climatic effects only on the latter form of mortality. Our analysis was based on a 22-year marking study (n = 27,150 females) and included live recaptures at the breeding colony and dead recoveries from hunters. We tested the effect of climatic covariates by applying a procedure that accounts for unexplained environmental variation in the demographic parameter to a multistate capture-mark-recapture recruitment model. Breeding propensity, clutch size and hatching probability all increased with high temperatures on the breeding grounds. First-year survival to natural causes of mortality increased when temperature was high at the end of the summer, whereas adult survival was not affected by temperature. On the contrary, accession to reproduction decreased with warmer climatic conditions during the non-breeding season. Survival was strongly negatively related to hunting mortality in adults, as expected, but not in first-year birds, which suggests the possibility of compensation between natural and hunting mortality in the latter group. We show that events occurring both at and away from the breeding ground can affect the demography of migratory birds, either directly or through carryover effects, and sometimes in opposite ways. This highlights the need to account for the whole life cycle of an animal when attempting to project the response of populations to future climatic changes.

Individual heterogeneity in black brant survival and recruitment with implications for harvest dynamics.

We examined individual heterogeneity in survival and recruitment of female Pacific black brant (Branta bernicla nigricans) using frailty models adapted to a capture-mark-recapture context. Our main objectives were (1) to quantify levels of heterogeneity and examine factors affecting heterogeneity, and (2) model the effects of individual heterogeneity on harvest dynamics through matrix models. We used 24 years of data on brant marked and recaptured at the Tutakoke River colony, AK. Multievent models were fit as hidden Markov chain using program E-SURGE with an adequate overdispersion coefficient. Annual survival of individuals marked as goslings was heterogeneous among individuals and year specific with about 0.23 difference in survival between "high" (0.73)- and "low" (0.50)-quality individuals at average survival probability. Adult survival (0.85 ± 0.004) was homogeneous and higher than survival of both groups of juveniles. The annual recruitment probability was heterogeneous for brant >1-year-old; 0.56 (±0.21) and 0.31 (±0.03) for high- and low-quality individuals, respectively. Assuming equal clutch sizes for high- and low-quality individuals and that 80% of offspring were in the same quality class as the breeding female resulted in reproductive values about twice as high for high-quality individuals than low-quality individual for a given class of individuals producing differential contributions to population growth among groups. Differences in reproductive values greatly increased when we assumed high-quality individuals had larger clutch sizes. When we assumed that 50% of offspring were in the same quality class as their mothers and clutches were equal, differences in reproductive values between quality classes were greatly reduced or eliminated (breeders [BRs]). We considered several harvest scenarios using the assumption that 80% of offspring were in the same quality class as their mothers. The amount of compensation for harvest mortality declined as the proportion of high-quality individuals in the harvest increased, as differences in clutch sizes between groups decreased and as the proportion of BRs in the harvest increased. Synthesis and applications. Harvest at the same proportional level of the overall population can result in variable responses in population growth rate when heterogeneity is present in a population. λ was <1.0 under every scenario when harvest rates were >10%, and heterogeneity caused as much as +2% difference in growth rates at the highest levels of proportional harvest for low-quality individuals and the greatest differences in qualities between classes of individuals, a critical difference for a population with λ near 1.0 such as the brant. We observed less response in overall survival in the presence of heterogeneity because we did not observe heterogeneity in the annual survival of BRs. This analysis provides a comprehensive view of overall compensation at the population level and also constitutes the first example of a survival-recruitment model with heterogeneity. Individual heterogeneity should be more explicitly considered in harvest management of vertebrates.

Estimating demographic parameters using hidden process dynamic models.

Structured population models are widely used in plant and animal demographic studies to assess population dynamics. In matrix population models, populations are described with discrete classes of individuals (age, life history stage or size). To calibrate these models, longitudinal data are collected at the individual level to estimate demographic parameters. However, several sources of uncertainty can complicate parameter estimation, such as imperfect detection of individuals inherent to monitoring in the wild and uncertainty in assigning a state to an individual. Here, we show how recent statistical models can help overcome these issues. We focus on hidden process models that run two time series in parallel, one capturing the dynamics of the true states and the other consisting of observations arising from these underlying possibly unknown states. In a first case study, we illustrate hidden Markov models with an example of how to accommodate state uncertainty using Frequentist theory and maximum likelihood estimation. In a second case study, we illustrate state-space models with an example of how to estimate lifetime reproductive success despite imperfect detection, using a Bayesian framework and Markov Chain Monte Carlo simulation. Hidden process models are a promising tool as they allow population biologists to cope with process variation while simultaneously accounting for observation error.

The evolution of dispersal in random environment.

In this paper we introduce a stochastic model for a population living and migrating between s sites without distinction in the states between residents and immigrants. The evolutionary stable strategies (ESS) is characterized by the maximization of a stochastic growth rate. We obtain that the expectation of reproductive values, normalized by some random quantity, are constant on all sites and that the expectation of the normalized vector population structure is proportional to eigenvector of the dispersion matrix associated to eigenvalue one, which are, in some way, analogous to the results obtained in the deterministic case.

The impact of global change on terrestrial Vertebrates.

Examples of the impact of human activities on Vertebrate populations abound, with famous cases of extinction. This article reviews how and why Vertebrates are affected by the various components of global change. The effect of direct exploitation, while strong, is currently superseded by changes in use of all sorts, while climate change has started having significant effects on some Vertebrate populations. The low maximum growth rate of Vertebrate populations makes them particularly sensitive to global change, while they contribute relatively modestly to major ecosystem services. One may conclude that unless they are considered as sentinels of the biological consequences of global changes, their situation will go on strongly deteriorating, in particular under the influence of interactions of different components of global change such as changes in use and climate change.

Studying dispersal at the landscape scale: efficient combination of population surveys and capture-recapture data.

Researchers often rely on capture-mark-recapture (CMR) data to study animal dispersal in the wild. Yet their spatial coverage often does not encompass the entire dispersal range of the study individuals, sometimes producing misleading results. Information contained in population surveys and variation in population spatial structure can be used to overcome this issue. We build an integrated model in a multisite context in which CMR data are only collected at a subset of sites, but numbers of breeding pairs are counted at all sites. In a Black-headed Gull Chroicocephalus ridibundus population, the integrated-modeling approach induces an increase in precision for the demographic parameters of interest (variances, on average, were decreased by 20%) and provides a more precise extrapolation of results from the CMR data to the whole population. Patterns of condition-dependent dispersal are therefore made easier to detect, and we obtain evidence for colony-size dependence in recruitment, dispersal, and breeding success. These results suggest that first-time breeders disperse to small colonies in order to recruit earlier. The exchange of experienced breeders between colonies appears as a main determinant of the observed variation in colony sizes.

Assessing whether mortality is additive using marked animals: a Bayesian state-space modeling approach.

Whether different sources of mortality are additive, compensatory, or depensatory is a key question in population biology. A way to test for additivity is to calculate the correlation between cause-specific mortality rates obtained from marked animals. However, existing methods to estimate this correlation raise several methodological issues. One difficulty is the existence of an intrinsic bias in the correlation parameter. Although this bias can be formally expressed, it requires knowledge about natural survival without any competing mortality source, which is difficult to assess in most cases. Another difficulty lies in estimating the true process correlation while properly accounting for sampling variation. Using a Bayesian approach, we developed a state-space model to assess the correlation between two competing sources of mortality. By distinguishing the mortality process from its observation through dead recoveries and live recaptures, we estimated the process correlation. To correct for the intrinsic bias, we incorporated experts' opinions on natural survival. We illustrated our approach using data on a hunted population of wild boars. Mortalities were not additive and natural mortality increased with hunting mortality likely as a consequence of non-controlled mortality by crippling loss. Our method opens perspectives for wildlife management and for the conservation of endangered species.

Importance of accounting for detection heterogeneity when estimating abundance: the case of French wolves.

Assessing conservation strategies requires reliable estimates of abundance. Because detecting all individuals is most often impossible in free-ranging populations, estimation procedures have to account for a <1 detection probability. Capture-recapture methods allow biologists to cope with this issue of detectability. Nevertheless, capture-recapture models for open populations are built on the assumption that all individuals share the same detection probability, although detection heterogeneity among individuals has led to underestimating abundance of closed populations. We developed multievent capture-recapture models for an open population and proposed an associated estimator of population size that both account for individual detection heterogeneity (IDH). We considered a two-class mixture model with weakly and highly detectable individuals to account for IDH. In a noninvasive capture-recapture study of wolves we based on genotypes identified in feces and hairs, we found a large underestimation of population size (27% on average) occurred when IDH was ignored.

Breeding dispersal in black-headed gull: the value of familiarity in a contrasted environment.

1. Some species (e.g. migratory species with high movement ability) are unlikely to experience any physical cost when dispersing, at least at the landscape scale. In these species dispersal is nevertheless behaviourally constrained to avoid non-physical costs such as the loss of familiarity with the breeding environment, and these constraints can be maladaptive in a fast-changing environment. 2. We evaluated such constraints using multievent modelling of a 20-year capture-mark-recapture data set from a multisite population of black-headed gull (Chroicocephalus ridibundus). The population undertakes seasonal migrations that are very large compared with the size of the study area. 3. Distances between colonies appeared as a strong predictor of breeding adults' dispersal rates, confirming behavioural constraints on dispersal. In addition, birds that had recruited outside their colony of birth (natal dispersers) tended to return to their colony of birth later in life (long-term memory effect). 4. An attraction for larger colonies was also visible in breeding adult dispersal patterns. The fact that distance and memory still constrained dispersal although the largest colony provided higher breeding success indicated departures from the ideal-free distribution, probably linked with the lack of information about distant colonies. Moreover, the regional population apparently functioned as a meta-colony where individuals frequently bred in suboptimal-choice locations before being able to recruit in their preferred colony.