Towards a better characterisation of deep-diving whales' distributions by using prey distribution model outputs?

In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels distribution and by extension, of marine top predator distributions. More proximal variables, such as potential prey fields, could refine relationships between top predator distributions and their environment. In situ data on prey distributions are not available over large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM), provides simulations of the biomass and production of zooplankton and six functional groups of micronekton at the global scale. Here, we explored whether generalised additive models fitted to simulated prey distribution data better predicted deep-diver densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus) than models fitted to environmental variables. We assessed whether the combination of environmental and prey distribution data would further improve model fit by comparing their explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would simulate larger prey beyond 1,000 m would probably better characterise the prey of deep-diving cetaceans.

A real-world implementation of a nationwide, long-term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity.

Biodiversity has undergone a major decline throughout recent decades, particularly in farmland. Agricultural practices are recognized to be an important pressure on farmland biodiversity, and pesticides are suspected to be one of the main causes of this decline in biodiversity. As part of the national plan for reduction of pesticides use (Ecophyto), the French ministry of agriculture launched the 500 ENI (nonintended effects) monitoring program in 2012 in order to assess the unintended effects of agricultural practices, including pesticide use, on biodiversity represented by several taxonomic groups of interest for farmers. This long-term program monitors the biodiversity of nontargeted species (earthworms, plants, coleoptera, and birds), together with a wide range of annual data on agricultural practices (crop rotation, soil tillage, weed control, fertilizers, chemical treatments, etc.). Other parameters (e.g., landscape and climatic characteristics) are also integrated as covariates during the analyses. This monitoring program is expected to improve our understanding of the relative contribution of the different drivers of population and community trends. Here, we present the experience of setting up the 500 ENI network for this ambitious and highly complex monitoring program, as well as the type of data it collects. The issue of data quality control and some first results are discussed. With the aim of being useful to readers who would like to set up similar monitoring schemes, we also address some questions that have arisen following the first five years of the implementation phase of the program.

Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection.

The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.

Description of long-term monitoring of farmland biodiversity in a LTSER.

Understanding the response of biodiversity to management, land use and climate change is a major challenge in farmland to halt the decline of biodiversity. Farmlands shelter a wide variety of taxa, which vary in their life cycle and habitat niches. Consequently, monitoring biodiversity from sessile annual plants to migratory birds requires dedicated protocols. In this article, we describe the protocols applied in a long-term research platform, the LTSER Zone Atelier "Plaine & Val de Sèvre" (for a full description see Bretagnolle et al. (2018) [1]). We present the data in the form of the description of monitoring protocols, which has evolved through time for arable weeds, grassland plants, ground beetles, spiders, grasshoppers, wild bees, hoverflies, butterflies, small mammals, and farmland birds (passerines, owls and various flagship species).

Species distribution modeling based on the automated identification of citizen observations.

PREMISE OF THE STUDY: A species distribution model computed with automatically identified plant observations was developed and evaluated to contribute to future ecological studies. METHODS: We used deep learning techniques to automatically identify opportunistic plant observations made by citizens through a popular mobile application. We compared species distribution modeling of invasive alien plants based on these data to inventories made by experts. RESULTS: The trained models have a reasonable predictive effectiveness for some species, but they are biased by the massive presence of cultivated specimens. DISCUSSION: The method proposed here allows for fine-grained and regular monitoring of some species of interest based on opportunistic observations. More in-depth investigation of the typology of the observations and the sampling bias should help improve the approach in the future.

How many sightings to model rare marine species distributions.

Despite large efforts, datasets with few sightings are often available for rare species of marine megafauna that typically live at low densities. This paucity of data makes modelling the habitat of these taxa particularly challenging. We tested the predictive performance of different types of species distribution models fitted to decreasing numbers of sightings. Generalised additive models (GAMs) with three different residual distributions and the presence only model MaxEnt were tested on two megafauna case studies differing in both the number of sightings and ecological niches. From a dolphin (277 sightings) and an auk (1,455 sightings) datasets, we simulated rarity with a sighting thinning protocol by random sampling (without replacement) of a decreasing fraction of sightings. Better prediction of the distribution of a rarely sighted species occupying a narrow habitat (auk dataset) was expected compared to the distribution of a rarely sighted species occupying a broad habitat (dolphin dataset). We used the original datasets to set up a baseline model and fitted additional models on fewer sightings but keeping effort constant. Model predictive performance was assessed with mean squared error and area under the curve. Predictions provided by the models fitted to the thinned-out datasets were better than a homogeneous spatial distribution down to a threshold of approximately 30 sightings for a GAM with a Tweedie distribution and approximately 130 sightings for the other models. Thinning the sighting data for the taxon with narrower habitats seemed to be less detrimental to model predictive performance than for the broader habitat taxon. To generate reliable habitat modelling predictions for rarely sighted marine predators, our results suggest (1) using GAMs with a Tweedie distribution with presence-absence data and (2) implementing, as a conservative empirical measure, at least 50 sightings in the models.

Towards sustainable and multifunctional agriculture in farmland landscapes: Lessons from the integrative approach of a French LTSER platform.

Agriculture is currently facing unprecedented challenges: ensuring food, fiber and energy production in the face of global change, maintaining the economic performance of farmers and preserving natural resources such as biodiversity and associated key ecosystem services for sustainable agriculture. Addressing these challenges requires innovative landscape scale farming systems that account for changing economic and environmental targets. These novel agricultural systems need to be recognized, accepted and promoted by all stakeholders, including local residents, and supported by public policies. Agroecosystems should be considered as socio-ecological systems and alternative farming systems should be based on ecological principles while taking societal needs into account. This requires an in-depth knowledge of the multiple interactions between sociological and ecological dynamics. Long Term Socio-Ecological Research platforms (LTSER) are ideal for acquiring this knowledge as they (i) are not constrained by traditional disciplinary boundaries, (ii) operate at a large spatial scale involving all stakeholders, and (iii) use systemic approaches to investigate biodiversity and ecosystem services. This study presents the socio-ecological research strategy from the LTSER "Zone Atelier Plaine & Val de Sèvre" (ZA PVS), a large study area where data has been sampled since 1994. Its global aim is to identify effective solutions for agricultural development and the conservation of biodiversity in farmlands. Three main objectives are targeted by the ZAPVS. The first objective is intensive monitoring of landscape features, the main taxa present and agricultural practices. The second objective is the experimental investigation, in real fields with local farmers, of important ecosystem functions and services, in relation to pesticide use, crop production and farming socio-economic value. The third aim is to involve stakeholders through participatory research, citizen science and the dissemination of scientific results. This paper underlines the relevance of LTSERs for addressing agricultural challenges, while acknowledging that there are some yet unsolved key challenges.

The spatial distribution of Mustelidae in France.

We estimated the spatial distribution of 6 Mustelidae species in France using the data collected by the French national hunting and wildlife agency under the "small carnivorous species logbooks" program. The 1500 national wildlife protection officers working for this agency spend 80% of their working time traveling in the spatial area in which they have authority. During their travels, they occasionally detect dead or living small and medium size carnivorous animals. Between 2002 and 2005, each car operated by this agency was equipped with a logbook in which officers recorded information about the detected animals (species, location, dead or alive, date). Thus, more than 30000 dead or living animals were detected during the study period. Because a large number of detected animals in a region could have been the result of a high sampling pressure there, we modeled the number of detected animals as a function of the sampling effort to allow for unbiased estimation of the species density. For dead animals -- mostly roadkill -- we supposed that the effort in a given region was proportional to the distance traveled by the officers. For living animals, we had no way to measure the sampling effort. We demonstrated that it was possible to use the whole dataset (dead and living animals) to estimate the following: (i) the relative density -- i.e., the density multiplied by an unknown constant -- of each species of interest across the different French agricultural regions, (ii) the sampling effort for living animals for each region, and (iii) the relative detection probability for various species of interest.

Statistical ecology comes of age.

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Using animal performance data to evidence the under-reporting of case herds during an epizootic: application to an outbreak of bluetongue in cattle.

Following the emergence of the Bluetongue virus serotype 8 (BTV-8) in France in 2006, a surveillance system (both passive and active) was implemented to detect and follow precociously the progression of the epizootic wave. This system did not allow a precise estimation of the extent of the epizootic. Infection by BTV-8 is associated with a decrease of fertility. The objective of this study was to evaluate whether a decrease in fertility can be used to evidence the under-reporting of cases during an epizootic and to quantify to what extent non-reported cases contribute to the total burden of the epizootic. The cow fertility in herds in the outbreak area (reported or not) was monitored around the date of clinical signs. A geostatistical interpolation method was used to estimate a date of clinical signs for non-reported herds. This interpolation was based on the spatiotemporal dynamic of confirmed case herds reported in 2007. Decreases in fertility were evidenced for both types of herds around the date of clinical signs. In non-reported herds, the decrease fertility was large (60% of the effect in reported herds), suggesting that some of these herds have been infected by the virus during 2007. Production losses in non-reported infected herds could thus contribute to an important part of the total burden of the epizootic. Overall, results indicate that performance data can be used to evidence the under-reporting during an epizootic. This approach could be generalized to pathogens that affect cattle's performance, including zoonotic agents such as Coxiella burnetii or Rift Valley fever virus.

Can we predict foraging success in a marine predator from dive patterns only? Validation with prey capture attempt data.

Predicting how climatic variations will affect marine predator populations relies on our ability to assess foraging success, but evaluating foraging success in a marine predator at sea is particularly difficult. Dive metrics are commonly available for marine mammals, diving birds and some species of fish. Bottom duration or dive duration are usually used as proxies for foraging success. However, few studies have tried to validate these assumptions and identify the set of behavioral variables that best predict foraging success at a given time scale. The objective of this study was to assess if foraging success in Antarctic fur seals could be accurately predicted from dive parameters only, at different temporal scales. For this study, 11 individuals were equipped with either Hall sensors or accelerometers to record dive profiles and detect mouth-opening events, which were considered prey capture attempts. The number of prey capture attempts was best predicted by descent and ascent rates at the dive scale; bottom duration and descent rates at 30-min, 1-h, and 2-h scales; and ascent rates and maximum dive depths at the all-night scale. Model performances increased with temporal scales, but rank and sign of the factors varied according to the time scale considered, suggesting that behavioral adjustment in response to prey distribution could occur at certain scales only. The models predicted the foraging intensity of new individuals with good accuracy despite high inter-individual differences. Dive metrics that predict foraging success depend on the species and the scale considered, as verified by the literature and this study. The methodology used in our study is easy to implement, enables an assessment of model performance, and could be applied to any other marine predator.

Modelling dendritic ecological networks in space: an integrated network perspective.

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

Successful foraging zones of southern elephant seals from the Kerguelen Islands in relation to oceanographic conditions.

Southern elephant seals, Mirounga leonina, undertake large-scale oceanic movements to access favourable foraging areas. Successful foraging areas of elephant seals from the Kerguelen Islands are investigated here in relation to oceanographic parameters. Movements and diving activity of the seals as well as oceanographic data were collected through a new generation of satellite relayed devices measuring and transmitting locations, pressure, temperature and salinity. For the first time, we have associated foraging behaviour, determined by high increased sinuosity in tracks, and dive density (i.e. number of dives performed per kilometre covered), and changes in body condition, determined by variations in drift rate obtained from drift dives, to identify the oceanographic conditions of successful foraging zones for this species. Two main sectors, one close to the Antarctic continent and the other along the Polar Front (PF), where both foraging activity and body condition increase, seem to be of particular interest for the seals. Within these regions, some seals tended to focus their foraging activity on zones with particular temperature signatures. Along the Antarctic continent, some seals targeted colder waters on the sea bottom during benthic dives, while at the PF the favourable zones tended to be warmer. The possible negative effect of colder waters in Antarctic on the swimming performances of potential fish or squid prey could explain the behaviour of elephant seals in these zones, while warmer waters within the PF could correspond to the optimal conditions for potential myctophid prey of elephant seals.