Pervasive gaps in Amazonian ecological research.

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%-18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost.

The importance of multi-year studies and commercial yield metrics in measuring pollinator dependence ratios: A case study in UK raspberries Rubus idaeus L.

The benefit of pollinators to crop production is normally calculated using "pollinator dependence ratios," which reflect the proportion of yield lost (here reported as a value between 0 and 1) in the absence of pollinators; these ratios are quantified experimentally using pollinator exclusion experiments. Pollinator dependence ratio estimates can vary considerably for a single crop, creating large, frequently overlooked, uncertainty in economic valuations of pollinators. The source of this variation is usually unclear. We experimentally measured the pollinator dependence ratio of two UK commercial cultivars of raspberry Rubus idaeus L., using a range of yield metrics-fruit set, marketable fruit set, fruit weight, and marketable fruit weight-over 3 years (2019-2021), to quantify the effects of yield metric, interannual variation, and cultivar on pollinator dependence ratio estimates. We found a difference in the pollinator dependence ratio for fruit set of 0.71 between 2019 and 2020, showing the importance of carrying out exclusion studies over multiple years. Averaged over multiple years and two cultivars, the dependence ratio was 0.68 measured using marketable fruit set and 0.64 using marketable fruit weight. Imposing a quality threshold (size and shape) below which fruits would not be of commercial value (marketable fruit set/weight) dramatically increased both the pollinator dependence ratio and subsequent economic valuations of pollination service derived from it. Our study shows that, for raspberry, estimates of the pollinator dependence ratio, and therefore, the economic value of insect pollinators, are highly sensitive to the choice of yield metric and can change between years and cultivars. Many economic decisions about pollinator management, at farm, regional and national scales rely on estimates of pollinator dependence. We, therefore, recommend that for estimating pollinator dependence ratios, pollinator exclusion studies are conducted over three or more years and use yield metrics that incorporate quality criteria linked to actual market values and commercial thresholds.

Faecal metabarcoding reveals pervasive long-distance impacts of garden bird feeding.

Supplementary feeding of wildlife is widespread, being undertaken by more than half of households in many countries. However, the impact that these supplemental resources have is unclear, with impacts largely considered to be restricted to urban ecosystems. We reveal the pervasiveness of supplementary foodstuffs in the diet of a wild bird using metabarcoding of blue tit (Cyanistes caeruleus) faeces collected in early spring from a 220 km transect in Scotland with a large urbanization gradient. Supplementary foodstuffs were present in the majority of samples, with peanut (Arachis hypogaea) the single commonest (either natural or supplementary) dietary item. Consumption rates exhibited a distance decay from human habitation but remained high at several hundred metres from the nearest household and continued to our study limit of 1.4 km distant. Supplementary food consumption was associated with a near quadrupling of blue tit breeding density and a 5-day advancement of breeding phenology. We show that woodland bird species using supplementary food have increasing UK population trends, while species that do not, and/or are outcompeted by blue tits, are likely to be declining. We suggest that the impacts of supplementary feeding are larger and more spatially extensive than currently appreciated and could be disrupting population and ecosystem dynamics.

Gradients in richness and turnover of a forest passerine's diet prior to breeding: A mixed model approach applied to faecal metabarcoding data.

Rather little is known about the dietary richness and variation of generalist insectivorous species, including birds, due primarily to difficulties in prey identification. Using faecal metabarcoding, we provide the most comprehensive analysis of a passerine's diet to date, identifying the relative magnitudes of biogeographic, habitat and temporal trends in the richness and turnover in diet of Cyanistes caeruleus (blue tit) along a 39 site and 2° latitudinal transect in Scotland. Faecal samples were collected in 2014-2015 from adult birds roosting in nestboxes prior to nest building. DNA was extracted from 793 samples and we amplified COI and 16S minibarcodes. We identified 432 molecular operational taxonomic units that correspond to putative dietary items. Most dietary items were rare, with Lepidoptera being the most abundant and taxon-rich prey order. Here, we present a statistical approach for estimation of gradients and intersample variation in taxonomic richness and turnover using a generalised linear mixed model. We discuss the merits of this approach over existing tools and present methods for model-based estimation of repeatability, taxon richness and Jaccard indices. We found that dietary richness increases significantly as spring advances, but changes little with elevation, latitude or local tree composition. In comparison, dietary composition exhibits significant turnover along temporal and spatial gradients and among sites. Our study shows the promise of faecal metabarcoding for inferring the macroecology of food webs, but we also highlight the challenge posed by contamination and make recommendations of laboratory and statistical practices to minimise its impact on inference.

A Spatial Perspective on the Phenological Distribution of the Spring Woodland Caterpillar Peak.

A classic system for studying trophic mismatch focuses on the timing of the spring caterpillar peak in relation to the breeding time and productivity of woodland passerine birds. Most work has been conducted in single-site oak woodlands, and little is known about how insights generalize to other woodland types or across space. Here we present the results of a 3-year study on the species composition and temporal distribution of the spring caterpillar peak on different tree taxa across 40 woodland sites spanning 2° of latitude in Scotland. We used molecular barcoding to identify 62 caterpillar species, with winter moth (Operophtera brumata) being the most abundant, comprising one-third of the sample. Oak (Quercus sp.) and willow (Salix sp.) hosted significantly higher caterpillar abundances than other tree taxa, with winter moth exhibiting similar trends and invariantly proportionate across tree taxa. Caterpillar peak phenology was broadly similar between tree taxa. While latitude had little effect, increasing elevation increased the height of the caterpillar peak and retarded timing by 3.7 days per 100 m. These findings extend our understanding of how mismatch may play out spatially, with caterpillar peak date varying with elevation and tree taxa varying in the caterpillar resource that they host.

The environmental predictors of spatio-temporal variation in the breeding phenology of a passerine bird.

Establishing the cues or constraints that influence avian timing of breeding is the key to accurate prediction of future phenology. This study aims to identify the aspects of the environment that predict the timing of two measures of breeding phenology (nest initiation and egg laying date) in an insectivorous woodland passerine, the blue tit (Cyanistes caeruleus). We analyse data collected from a 220 km, 40-site transect over 3 years and consider spring temperatures, tree leafing phenology, invertebrate availability and photoperiod as predictors of breeding phenology. We find that mean night-time temperature in early spring is the strongest predictor of both nest initiation and lay date and suggest this finding is most consistent with temperature acting as a constraint on breeding activity. Birch budburst phenology significantly predicts lay date additionally to temperature, either as a direct cue or indirectly via a correlated variable. We use cross-validation to show that our model accurately predicts lay date in two further years and find that similar variables predict lay date well across the UK national nest record scheme. This work refines our understanding of the principal factors influencing the timing of tit reproductive phenology and suggests that temperature may have both a direct and indirect effect.