High Interannual Variability Research Articles

Length, width, shape regularity, and chain structure: time series analysis of phytoplankton morphology from imagery.

Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time‐series station. We analyzed a 2‐yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year‐round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter‐annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.

Heritable and Climatic Sources of Variation in Juvenile Tree Growth in an Austrian Common Garden Experiment of Central European Norway Spruce Populations

We leveraged publicly available data on juvenile tree height of 299 Central European Norway spruce populations grown in a common garden experiment across 24 diverse trial locations in Austria and weather data from the trial locations and population provenances to parse the heritable and climatic components of juvenile tree height variation. Principal component analysis of geospatial and weather variables demonstrated high interannual variation among trial environments, largely driven by differences in precipitation, and separation of population provenances based on altitude, temperature, and snowfall. Tree height was highly heritable and modeling the covariance between populations and trial environments based on climatic data led to more stable estimation of heritability and population × environment variance. Climatic similarity among population provenances was highly predictive of population × environment estimates for tree height.

Impact of urbanization and stormwater infrastructure on ephemeral channel transmission loss in a semiarid watershed

Study regionMontoyas watershed in central New Mexico, a rapidly urbanizing basin characteristic for the semiarid Southwestern United States. The 142 km2 watershed drains to the Rio Grande. Study focusThis study evaluated the impacts of urbanization and stormwater infrastructure on ephemeral channel transmission loss (TL), an important source of groundwater recharge in dryland basins. A hydrologic model (HEC-HMS) was built for the watershed and calibrated based on seven years of precipitation and discharge data. The model was then used to simulate the individual and combined impacts of land use change (urbanization) and stormwater infrastructure (ponds, channel lining) on runoff and transmission losses over a ten-year period based on observed storm events. New hydrological insights for the regionResults show high inter-annual runoff variability with the two wettest years contributing 44–59% of the 10-year total, and dry years resulting in nearly no flow. Both runoff and TL increased with urbanization. Assuming natural ephemeral channels, TL comprised 64–81% of runoff reaching the basin outlet and 7–10% of domestic water use in the basin, depending on urbanization scenario. Stormwater detention ponds had minimal impact on TL; however, lining the lower 5, 10 and 15 km of ephemeral streams reduced TL by 31%, 55% and 73% over the ten-year study period. Results indicate that preservation of ephemeral channels, in particular permeable channel beds, should be considered a viable strategy for managed aquifer recharge in urbanizing areas.

Sources of Nonergodicity for Teleconnections as Cross‐Correlations

AbstractTeleconnections are prominent features of internal variability which, along with climatic means, can also undergo forced changes. The assessment of such changes based on single realization observational data calculating moving window temporal correlation coefficients is burdened by biases. Previously, we attributed one source of such a bias, that is, nonergodicity, to the omission of low‐frequency (LF) variability, but find here that it is negligible for typical processes due to their spectral characteristic, given that LF, say, interdecadal, variability is much weaker than the high frequency interannual variability. The unnecessary omission of detrending, as another source, is potentially much more deleterious, as we find that the bias depends quadratically on both the window size and the trends of the means. Given that forced changes in the variances should not introduce much bias, a nonlinear change of the teleconnection strength is implied as the main irreducible source of nonergodicity.

What are the impacts of water deficit, cultivars, and years on the dynamics of nutrient uptake by common bean? Part II: Ca, Mg, and S

We aimed to explain and compare Ca, Mg, and S uptake dynamics by two common bean cultivars with contrasting growth habits as a function of water deficit severity in two years to generate more specific information for their fertilization management. A field study was set up using the cultivars IAC Imperador, with a determinate growth habit and early cycle, and IPR Campos Gerais, with an indeterminate growth habit and intermediate cycle. These cultivars were maintained under three irrigation levels, that is, severe (54% ETc), moderate (77% ETc), and no water deficit (100% ETc). The absorption order was Ca > Mg > S, regardless of the treatment. For the IAC Imperador, the Ca uptake ranged from 72 to 127 kg ha−1, Mg from 17.2 to 39.1 kg ha−1 and S from 7.9 to 16.5 kg ha−1, while for the IPR Campos Gerais it ranged from 92 to 156 kg ha−1, 21.0 to 36.5 kg ha−1 and 14.6 to 33.7 kg ha−1, respectively. Overall, severe water deficit reduced Ca, Mg, and S absorption by more than 25%. The IPR Campos Gerais presents, on average, 64% higher S accumulation, while the highest Ca and Mg accumulation order depends on the year. Compared to the IAC Imperador, this cultivar showed a later S absorption peak (up to +23 days), and higher interannual variability in the growth and nutrient absorption due to its indeterminate growth habit. These differences evidence the importance of specific fertilizer management practices for Ca, Mg and S in common bean, assisting in optimizing nutrient use.

Global trends in grassland carrying capacity and relative stocking density of livestock.

Although the role of livestock in future food systems is debated, animal proteins are unlikely to completely disappear from our diet. Grasslands are a key source of primary productivity for livestock, and feed‐food competition is often limited on such land. Previous research on the potential for sustainable grazing has focused on restricted geographical areas or does not consider inter‐annual changes in grazing opportunities. Here, we developed a robust method to estimate trends and interannual variability (IV) in global livestock carrying capacity (number of grazing animals a piece of land can support) over 2001–2015, as well as relative stocking density (the reported livestock distribution relative to the estimated carrying capacity [CC]) in 2010. We first estimated the aboveground biomass that is available for grazers on global grasslands based on the MODIS Net Primary Production product. This was then used to calculate livestock carrying capacities using slopes, forest cover, and animal forage requirements as restrictions. We found that globally, CC decreased on 27% of total grasslands area, mostly in Europe and southeastern Brazil, while it increased on 15% of grasslands, particularly in Sudano‐Sahel and some parts of South America. In 2010, livestock forage requirements exceeded forage availability in northwestern Europe, and southern and eastern Asia. Although our findings imply some opportunities to increase grazing pressures in cold regions, Central Africa, and Australia, the high IV or low biomass supply might prevent considerable increases in stocking densities. The approach and derived open access data sets can feed into global food system modelling, support conservation efforts to reduce land degradation associated with overgrazing, and help identify undergrazed areas for targeted sustainable intensification efforts or rewilding purposes.

Arctic Maritime Cyclone Distribution and Trends in the ERA5 Reanalysis

Abstract We present a tracking algorithm for synoptic to meso-α-scale Arctic cyclones that differentiates between cold- and warm-core systems. The algorithm is applied to the ERA5 reanalysis north of 60°N from 1950 to 2019. In this dataset, over one-half of the cyclones that meet minimum intensity and duration thresholds can be classified as cold-core systems. Systems that undergo transition, typically from cold to warm core, make up 27.2% of cyclones and are longer lived. The relatively infrequent warm-core cyclones are more intense and are most common in winter. The Arctic-wide occurrence of maritime cyclones has increased from 1979 to 2019 when compared with the period from 1950 to 1978, but the trends have high interannual variability. This shift has ramifications for transportation, fisheries, and extractive industries, as well as impacts on communities across the Arctic.

Inter-annual variability in breeding census data across species and regions

There is an intense interest in long-term trends of species abundance that may reflect, for example, climate change or conservation actions. Less well studied are patterns in the magnitude of inter-annual variability in abundance across large spatial scales. We collated abundance time-series for 133 nesting sites across the globe of the seven sea turtle species. Inter-annual variability in nest numbers was lowest in loggerhead turtles (Caretta caretta), Kemp’s ridley (Lepidochelys kempii) and flatback turtle (Natator depressus) and highest in green turtles (Chelonia mydas), likely reflecting their lower trophic position compared to other species and hence tighter coupling of food availability to environmental conditions each year. The annual number of nests in green turtles could vary by 60-fold between successive years. We identified regional patterns in the magnitude of inter-annual variability in green turtle nest numbers, variability being highest for nesting beaches around Australia and lowest in the western Indian Ocean and equatorial Atlantic. These regional patterns are likely linked to corresponding patterns of environmental variability with, for example, areas subjected environmental extremes as part of the El Nino Southern Oscillation (ENSO) showing high inter-annual variability in nest numbers.

Widespread greening suggests increased dry-season plant water availability in the Rio Santa valley, Peruvian Andes

Abstract. In the semi-arid Peruvian Andes, the growing season is mostly determined by the timing of the onset and retreat of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa basin (RSB) reported more erratic rainy season onsets and further challenges related to changes in rainfall characteristics. Previous studies based on local rain gauges, however, did not find any significant long-term rainfall changes, potentially linked to the scarce data basis and inherent difficulties in capturing the highly variable rainfall distribution typical for complex mountain terrain. To date, there remains considerable uncertainty in the RSB regarding changes in plant-available water over the last decades. In this study, we exploit satellite-derived information of high-resolution vegetation greenness as an integrated proxy to derive variability and trends of plant water availability. By combining MODIS Aqua and Terra vegetation indices (VIs), datasets of precipitation (both for 2000–2020) and soil moisture (since 2015), we explore recent spatio-temporal changes in the vegetation growing season. We find the Normalized Difference Vegetation Index (NDVI) to be coupled to soil moisture on a sub-seasonal basis, while NDVI and rainfall only coincide on interannual timescales. Over 20 years, we find significant greening in the RSB, particularly pronounced during the dry season (austral winter), indicating an overall increase in plant-available water over the past 2 decades. The start of the growing season (SOS) exhibits high interannual variability of up to 2 months compared to the end of the growing season (EOS), which varies by up to 1 month, therefore dominating the variability of the growing season length (LOS). The EOS becomes significantly delayed over the analysis period, matching the observed dry-season greening. While both in situ and gridded rainfall datasets show incoherent changes in annual rainfall for the region, Climate Hazards InfraRed Precipitation with Station data (CHIRPS) rainfall suggests significant positive dry-season trends for 2 months coinciding with the most pronounced greening. As the greening signal is strongly seasonal and reaches high altitudes on unglaciated valley slopes, we cannot link this signal to water storage changes on timescales beyond one rainy season, making interannual rainfall variability the most likely driver. Exploring El Niño–Southern Oscillation (ENSO) control on greening, we find an overall increased LOS linked to an earlier SOS in El Niño years, which however cannot explain the observed greening and delayed EOS. While our study could not corroborate anecdotal evidence of recent changes, we confirm that the SOS is highly variable and conclude that rainfed farming in the RSB would profit from future efforts being directed towards improving medium-range forecasts of the rainy season onset.

Multidecadal Land Water and Groundwater Drought Evaluation in Peninsular India

To efficiently mitigate the multitude of direct and indirect impacts, basin-scale drought characterization is imperative, particularly for India, where water scarcity is continually increasing. We jointly used the GRACE gravity data, PCR-GLOBWB model outputs, and in situ data to quantify the deficits based on land water storage (LWS) and groundwater storage (GWS) in Peninsular India for 35 years from January 1980 to December 2014. The results showed that the study basins experienced high interannual variations despite the minimal linear LWS trends (0.26–0.56 mm yr−1). GWS showed a slow but persistent response (longest deficit spanning ~6 years) to the seasonal variations in the hydrological fluxes and remained the major contributor to LWS. We demonstrated (1) the potential of the PCR-GLOBWB model to analyze LWS and its segregated components beyond GRACE data records, (2) the LWS-based index as a better drought indicator than traditional drought indices, and (3) GRACE-LWS as a proxy indicator of real-time groundwater monitoring without relying upon the intermittent in situ observations. This study underscores the need to revise the water allocation strategies and irrigation systems to maintain the sustainability of groundwater systems and may serve as a holistic framework for remotely monitoring the water beneath our feet, especially in the data-limited regions globally.

A novel Budyko-based approach to quantify post-forest-fire streamflow response and recovery timescales

Recent increases in the incidences of wildfires have necessitated the development of methodologies to quantify the effect of these fires on streamflows. Climate variability has been cited as a major challenge in revealing the true contribution of disturbance to streamflow changes. To address this, we developed an annual Budyko “decomposition” method for (1) statistical change detection of hydrologic signatures post-fire, (2) separating climate-driven and fire-driven changes in streamflow, and (3) estimating hydrologic recovery timescales after fire. We demonstrate the use of this methodology for 17 watersheds in Southern California with high interannual variability in precipitation. We show that while traditional metrics like changes in flow or runoff ratio might not detect a disturbance effect due to confounding climate signals, the Budyko framework can be used successfully for statistical change detection. The Budyko approach was also found to be robust in detecting changes in 5 highly burned catchments (>40% burned area ratio), while changes in less burned (2) and unburned catchments (10) were insignificant. We further used the Budyko approach to quantify the contribution of fire-driven versus climate driven changes in streamflow and found that fire contributed to an average increase in streamflow on the order of 80 mm yr−1, though the effect varied greatly between years. Finally, we estimated hydrologic recovery timescales that varied between 5 and 45 years for four burned catchments. We found a significant linear relationship between recovery time and burned area at medium and high severity for our study catchments, with about 4 years of recovery time per 10% of the watershed burned.

Multidecadal Trends in Organic Carbon Flux Through a Grassland River Network Shaped by Human Controls and Climatic Cycles

AbstractGrasslands are among the most disturbed systems on Earth due to extensive agricultural development and river regulation. To define how these factors impact the movement of carbon (C) through river networks, we quantified multi‐decadal, seasonal patterns of dissolved organic C (DOC) fluxes through a grassland river network. Despite urbanization and effluent release in recent decades, we did not observe trends in DOC fluxes over 20 to 40 year monthly sample records. High interannual variability in DOC flux was linked to climate teleconnections, most strongly the Pacific Decadal Oscillation. Human impacts were more apparent on the seasonality of flux, since spring export dominated DOC flux in most sub‐watersheds, but seasonal changes were absent at the river mouth due to downstream flow control. In grassland regions, human impacts on DOC flux are complex, and subtle long‐term changes may be difficult to identify in contemporary monitoring records due to increasing hydro‐climatic variability.

Condors in space: an individual-based population model for California condor reintroduction planning

ContextExisting demographic models of California condors have not simultaneously considered individual condor movement paths, the distribution and juxtaposition of release sites, habitat components, or the spatial distribution of threats.ObjectivesOur objectives were to develop a dynamic spatially explicit and individual-based model (IBM) of California condor demography and to evaluate its ability to replicate empirical data on demography and distribution from California (1995–2019).MethodsWe built a female-only spatially explicit California condor IBM in HexSim, using a daily timestep that allowed us to simulate the foraging behavior of condors, changes in food distribution and availability, and the ephemeral threat of lead in decaying food resources.ResultsSimulated population size was highly correlated with annual population census data once the population became established with > 50 females (r2 = 0.99). Mean simulated fecundity and mortality estimates were not significantly different from empirical trends (p > 0.05), although empirical data had higher interannual variability. The geographic distribution of modeled condors was similar to the empirical distribution with an overall accuracy of 79%, a commission error of 27%, and an omission error of 9%. Simulated movement density corresponded moderately well to the density of observed GPS locations (weighted kappa = 0.44).ConclusionsWe developed a spatially explicit California condor IBM that is well-calibrated to empirical data from California. Given its mechanistic underpinnings and flexibility to incorporate a variety of spatial and demographic inputs, we expect our model to be useful for assessing the relative risks and benefits of future condor reintroduction and management scenarios.

Combined influence of maximum accumulation and melt rates on the duration of the seasonal snowpack over temperate mountains

The duration of the seasonal snowpack determines numerous aspects of the water cycle, ecology and the economy in cold and mountainous regions, and is a balance between the magnitude of accumulated snow and the rate of melt. The contribution of each component has not been well quantified under contrasting topography and climatological conditions although this may provide useful insights into how snow cover duration could respond to climate change. Here, we examined the contribution of the annual peak snow water equivalent (SWE) and the seasonal melt rate to define the duration of the snowpack over temperate mountains, using snow data for mountain areas with different climatological characteristics across the Iberian Peninsula. We used a daily snowpack database for the period 1980–2014 over Iberia to derive the seasonal peak SWE, melt rate and season snow cover duration. The influence of peak SWE and melt rates on seasonal snow cover duration was estimated using a stepwise linear model approach.The stepwise linear models showed high R-adjusted values (average R-adjusted = 0.7), without any clear dependence on the elevation or geographical location. In general, the peak SWE influenced the snow cover duration over all of the mountain areas analysed to a greater extent than the melt rates (89.1%, 89.2%, 81.6% 93.2% and 95.5% in the areas for the Cantabrian, Central, Iberian, Pyrenees and Sierra Nevada mountain ranges, respectively). At these colder sites, the melt season occurs mostly in the spring and tends to occur very fast. In contrast, the areas where the melt rates dominated snow cover duration were located systematically at lower elevations, due to the high interannual variability in the occurrence of annual peak SWE (in winter or early spring), yielding highly variable melt rates. However, in colder sites the melt season occurs mostly in spring and it is very fast in most of the years. The results highlight the control that the seasonal precipitation patterns, in combination with temperature, exert on the seasonal snow cover duration by influencing the peak SWE and suggest a future increased importance of melt rates as temperatures increase. Despite the high climatological variability of the Iberian mountain ranges, the results showed a consistent behaviour along the different mountain ranges, indicating that the methods and results may be transferrable to other temperate mountain areas of the world.

Resilient soybean and maize production under a varying climate in the semi-arid and sub-humid Chaco

Food security and economic stability of many developing countries rely on the resilience of major crops to climatic variability and climatic anomalies. Since climate change forecasts predict an increasing frequency and intensity of climatic disturbances, the need to increase our knowledge about the influence of climate variability on crop productivity is especially acute for areas with fragile environments such as the semi-arid and sub-humid Chaco in South America. We used climate records from recent decades and crop growth models to: (i) identify the main climatic variables that have influenced the productivity of soybean and maize which are major crops cultivated in the area and (ii) to assess the impact of inter-annual variability of climate variables on the productivity of soybean and maize. Simulated soybean and maize grain yields indicated that farmers in Chaco should be aware of the high interannual variability in the productivity of these crops. Farmers face a different risk cropping maize and soybean depending on the location. The productivity of soybean was below production costs in 10–13% of the studied years while the corresponding values for maize were 11–14%. Diversification of crop rotations is therefore key to enhance resilience and to increase the likelihood of harnessing favorable growing conditions. Management strategies intended to conserve soil water are of paramount importance, especially for soybean where anomalies in the precipitation during the first four months after sowing was the predictor that explained the highest amount of variance in grain yield (r = 0.58). To mitigate the effect of temperature, which essentially is a factor out of direct control of the farmer, the main practice that arose from our study is delaying planting dates (e.g., towards late January) to avoid the occurrence of high temperatures during the critical stages of the crop.

Carbon variation of dry grasslands in Central Asia in response to climate controls and grazing appropriation

Quantification of grassland carbon (C) variations is necessary for understanding how grazing and climate change interact to regulate carbon capture and release. Central Asia (CA) has the largest temperate grassland belt in the world and unique temperate dryland ecosystems, which experienced severe climate change and grazing-induced disturbances. However, the impact of grazing on C dynamics is highly uncertain owing to climate variations. Here, an arid ecosystem model (AEM) supplemented with a grazing module that specifically addressed physiological and ecological characteristics of dryland vegetation was developed to quantitatively simulate grassland C dynamics in response to changes in precipitation, temperature, grazing intensity, and CO2 level in the past decades. The regional simulation results showed that net primary productivity (NPP) was affected mainly by precipitation (in 59% of the studied area). Grazing had a negative effect on NPP and C stocks, whereas overcompensation occurred in 25.71% of the studied area, mainly in the dry western parts. The complex interaction effects of climate, CO2, and grazing negatively affected productivity, with a grassland NPP decrease of − 1.14 g C/m2/a and high interannual variability. We found that the temporal pattern of cumulative C sequestration, especially total C and vegetation C (VEGC), closely followed the annual fluctuations of precipitation. VEGC stocks decreased from 182.22 to 177.82 g C/m2, with a very low value between 1998 and 2008, when precipitation significantly decreased. The results indicate that southern Xinjiang and the Turgay Plateau of Kazakhstan are ecologically fragile areas due to grassland degradation.

The Relationship between Climate Change, Variability, and Food Security: Understanding the Impacts and Building Resilient Food Systems in West Pokot County, Kenya

This study integrated local and scientific knowledge to assess the impacts of climate change and variability on food security in West Pokot County, Kenya from 1980–2012. It characterized rainfall and temperature from 1980–2011 and the phenology of agricultural vegetation, assessed land use and land cover (LULC) changes, and surveyed local knowledge and perceptions of the relationships between climate change and variability, land use decisions, and food (in)security. The 124 respondents were aware of long-term changes in their environment, with 68% strongly believing that climate has become more variable. The majority of the respondents (88%) reported declining rainfall and rising temperatures, with respondents in the lowland areas reporting shortened growing seasons that affected food production. Meteorological data for 1980–2011 confirmed high inter-annual rainfall variability around the mean value of 973.4 mm/yr but with no notable trend. Temperature data showed an increasing trend between 1980 and 2012 with lowlands and highlands showing changes of +1.25 °C and +1.29 °C, respectively. Land use and land cover changes between 1984 and 2010 showed cropland area increased by +4176% (+33,138 ha), while grassland and forest areas declined by –49% (–96,988 ha) and –38% (–65,010 ha), respectively. These area changes illustrate human-mediated responses to the rainfall variability, such as increased stocking after good rainfall years and crop area expansion. The mean Normalized Difference Vegetation Index (NDVI) values ranged from 0.36–0.54 within a year, peaking in May and September. For weather-related planning, respondents relied on radio (64%) and traditional forecasters (26%) as predominant information sources. Supporting continuous climate change monitoring, intensified early warning systems, and disseminating relevant information to farmers could help farmers adopt appropriate adaptation strategies.

Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas).

Giant kelp populations that support productive and diverse coastal ecosystems at temperate and subpolar latitudes of both hemispheres are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally uneven, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp using satellite imagery requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery (8 images spanning the majority of our continuous timeseries, cumulatively covering over 2,700 km of coastline, and including all relevant sensors). Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017–2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.

Oceanic drivers of juvenile sea turtle strandings in the UK

Juvenile sea turtles can disperse thousands of kilometers from nesting beaches to oceanic development habitats, aided by ocean currents. In the North Atlantic, turtles dispersing from American beaches risk being advected out of warm nursery grounds in the North Atlantic Gyre into lethally cold northern European waters (e.g. around the UK). We used an ocean model simulation to compare simulated numbers of turtles that were advected to cold waters around the UK with observed numbers of turtles reported in the same area over ~5 decades. Rates of virtual turtles predicted to encounter lethal (10°C) or detrimental (15°C) temperatures (mean 19% ± 2.7 SD) and reach the UK were consistently low (median 0.83%, lower quartile 0.67%, upper quartile 1.02%), whereas there was high inter-annual variability in the numbers of dead or critically ill turtles reported in the UK. Generalized additive models suggest inter-annual variability in the North Atlantic Oscillation (NAO) index to be a good indicator of annual numbers of turtle strandings reported in the UK. We demonstrate that NAO variability drives variability in the dispersion scenarios of juvenile turtles from key nesting regions into the North Atlantic. Coastal effects, such as the number of storms and mean sea surface temperatures in the UK, were significant but weak predictors, with a weak effect on turtle strandings. Further understanding how changing environmental conditions such as NAO variability and storms affect the fate of juvenile turtles is vital for understanding the distribution and population dynamics of sea turtles.

Extremely high but localized pulses of coral recruitment in the southwestern lagoon of New Caledonia and implications for conservation

Recruitment processes largely drive spatial distributions, dynamics, and recovery potential of marine communities. Determining scales of variation in recruitment rates and composition can help in understanding population replenishment mechanisms, while identifying recruitment hotspots is crucial for improving conservation strategies, particularly for threatened marine ecosystems such as coral reefs. We examined the spatial and interannual variability (2012-2014) of coral recruitment at multiple scales within and among reef habitats (14 stations) in the southwestern lagoon of New Caledonia. Recruit assemblages were characterized by high recruitment rates compared to other regions (overall mean of 34.9 recruits per 11 × 11 × 1 cm settlement tile, corresponding to 1220.9 recruits m-2) and strong dominance of Acroporidae. We found a marked spatial heterogeneity among habitats but also exceptionally high interannual variation (100-fold), with extreme recruitment peaks (up to 13572.8 recruits m-2, with a maximum of 811 recruits on a single tile) recorded in 2014 at some fringing and mid-shelf reefs, the highest records ever reported to date. These encouraging results contrast with other reefs where recent declines in coral recruitment rates have been documented with major concerns for their resilience capacities. However, the marked spatio-temporal variability of coral recruitment complicates conservation strategies, as it makes it difficult to identify ‘recruitment hotspots’ as priority sites to protect for their potential capacity to boost the replenishment of local populations.