Despite its theoretical prominence and sound principles, integrated pest management (IPM) continues to suffer from anemic adoption rates in developing countries. To shed light on the reasons, we surveyed the opinions of a large and diverse pool of IPM professionals and practitioners from 96 countries by using structured concept mapping. The first phase of this method elicited 413 open-ended responses on perceived obstacles to IPM. Analysis of responses revealed 51 unique statements on obstacles, the most frequent of which was "insufficient training and technical support to farmers." Cluster analyses, based on participant opinions, grouped these unique statements into six themes: research weaknesses, outreach weaknesses, IPM weaknesses, farmer weaknesses, pesticide industry interference, and weak adoption incentives. Subsequently, 163 participants rated the obstacles expressed in the 51 unique statements according to importance and remediation difficulty. Respondents from developing countries and high-income countries rated the obstacles differently. As a group, developing-country respondents rated "IPM requires collective action within a farming community" as their top obstacle to IPM adoption. Respondents from high-income countries prioritized instead the "shortage of well-qualified IPM experts and extensionists." Differential prioritization was also evident among developing-country regions, and when obstacle statements were grouped into themes. Results highlighted the need to improve the participation of stakeholders from developing countries in the IPM adoption debate, and also to situate the debate within specific regional contexts. sustainable agriculture | technology adoption | collective action dilemma

Perennial rivers and streams make a disproportionate contribution to global carbon (C)cycling. However, the contribution of intermittent rivers and ephemeral streams, which

1.A major barrier for the scientific community of climate change biologists is the spatial mismatch between the size of organisms and the resolution at which global climate data are collected and modelled. Thus, the development of integrative and quantitative tools for the monitoring and spatial characterization of microclimates across spatial scales is a key issue for climate change ecologists. 2. We proposed an integrative toolbox for quantifying the spatial heterogeneity in surface temperatures by bringing together procedures of unmanned aerial vehicles, thermal imagery, orthomosaic, GIS classification and spatial metrics. This toolbox permits to yield high-resolution visual and infrared orthoimages that are processed into a GIS for selecting surfaces of interest in the landscape (e.g. soil, vegetation). Then, the thermal matrices of selected surfaces (i.e. temperature values of the pixels belonging to the selected surfaces only) are processed within R to generate a variety of thermal landscape metrics (e.g. thermal patch richness and density, thermal aggregation and cohesion index). 3. We applied this toolbox to the thermal characterization of mountainous agricultural landscapes in Ecuador with implications for ectothermic pest dynamics. UAV flights at a height of 60 m above-ground level allowed us to acquired high-resolution visual and thermal images (1 and 5 cm/pixel, respectively) for 12 potato fields with a mean surface of 1017 AE 117 m 2 . Landscape metrics on plant and soil surfaces showed that crop phenology drives the spatial patterns of surface temperatures and strongly modifies the overall thermal ecology of crop fields, with potential implications for ectothermic pest occurrence and dynamics. 4. Overall, our toolbox affords a timely and innovative methodological framework to better assess the thermal heterogeneity of natural landscapes across a wide range of spatial scales. In particular, this toolbox would be of topical interest for ecologists trying to bridge the gap between the resolution of their climatic data and the body size of their study organisms.

Climate change and human pressures are changing the global distribution and the extent of intermittent rivers and ephemeral streams (IRES), which comprise half of the global river network area. IRES are characterized by periods of flow cessation, during which channel substrates accumulate and undergo physico‐chemical changes (preconditioning), and periods of flow resumption, when these substrates are rewetted and release pulses of dissolved nutrients and organic matter (OM). However, there are no estimates of the amounts and quality of leached substances, nor is there information on the underlying environmental constraints operating at the global scale. We experimentally simulated, under standard laboratory conditions, rewetting of leaves, riverbed sediments, and epilithic biofilms collected during the dry phase across 205 IRES from five major climate zones. We determined the amounts and qualitative characteristics of the leached nutrients and OM, and estimated their areal fluxes from riverbeds. In addition, we evaluated the variance in leachate characteristics in relation to selected environmental variables and substrate characteristics. We found that sediments, due to their large quantities within riverbeds, contribute most to the overall flux of dissolved substances during rewetting events (56%–98%), and that flux rates distinctly differ among climate zones. Dissolved organic carbon, phenolics, and nitrate contributed most to the areal fluxes. The largest amounts of leached substances were found in the continental climate zone, coinciding with the lowest potential bioavailability of the leached OM. The opposite pattern was found in the arid zone. Environmental variables expected to be modified under climate change (i.e. potential evapotranspiration, aridity, dry period duration, land use) were correlated with the amount of leached substances, with the strongest relationship found for sediments. These results show that the role of IRES should be accounted for in global biogeochemical cycles, especially because prevalence of IRES will increase due to increasing severity of drying events.

In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.

Bridging the gap between the predictions of coarse-scale climate models and the fine-scale climatic reality of species is a key issue of climate change biology research. While it is now well known that most organisms do not experience the climatic conditions recorded at weather stations, there is little information on the discrepancies between microclimates and global interpolated temperatures used in species distribution models, and their consequences for organisms’ performance. To address this issue, we examined the fine-scale spatiotemporal heterogeneity in air, crop canopy and soil temperatures of agricultural landscapes in the Ecuadorian Andes and compared them to predictions of global interpolated climatic grids. Temperature time-series were measured in air, canopy and soil for 108 localities at three altitudes and analysed using Fourier transform. Discrepancies between local temperatures vs. global interpolated grids and their implications for pest performance were then mapped and analysed using GIS statistical toolbox. Our results showed that global interpolated predictions over-estimate by 77.5±10% and under-estimate by 82.1±12% local minimum and maximum air temperatures recorded in the studied grid. Additional modifications of local air temperatures were due to the thermal buffering of plant canopies (from −2.7°K during daytime to 1.3°K during night-time) and soils (from −4.9°K during daytime to 6.7°K during night-time) with a significant effect of crop phenology on the buffer effect. This discrepancies between interpolated and local temperatures strongly affected predictions of the performance of an ectothermic crop pest as interpolated temperatures predicted pest growth rates 2.3–4.3 times lower than those predicted by local temperatures. This study provides quantitative information on the limitation of coarse-scale climate data to capture the reality of the climatic environment experienced by living organisms. In highly heterogeneous region such as tropical mountains, caution should therefore be taken when using global models to infer local-scale biological processes.

Surface temperature drives many ecological processes and infrared thermography is widely used by ecologists to measure the thermal heterogeneity of different species' habitats. However, the potential bias in temperature readings caused by distance between the surface to be measured and the camera is still poorly acknowledged. We examined the effect of distance from 0.3 to 80m on a variety of thermal metrics (mean temperature, standard deviation, patch richness and aggregation) under various weather conditions and for different structural complexity of the studied surface types (various surfaces with vegetation). We found that distance is a key modifier of the temperature measured by a thermal infrared camera. A non-linear relationship between distance and mean temperature, standard deviation and patch richness led to a rapid under-estimation of the thermal metrics within the first 20m and then only a slight decrease between 20 and 80m from the object. Solar radiation also enhanced the bias with increasing distance. Therefore, surface temperatures were under-estimated as distance increased and thermal mosaics were homogenized at long distances with a much stronger bias in the warmer than the colder parts of the distributions. The under-estimation of thermal metrics due to distance was explained by atmospheric composition and the pixel size effect. The structural complexity of the surface had little effect on the surface temperature bias. Finally, we provide general guidelines for ecologists to minimize inaccuracies caused by distance from the studied surface in thermography.

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.