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Global food demand will rise substantially over the coming decades. Meeting this demand while decreasing the environmental footprint of agriculture is one of largest challenges of the twenty-first century1,2,3. A growing world population and changing diets are projected to double4 meat and dairy consumption between 2000 and 2050. As one of the main feed sources for livestock, grasslands play a key role in meeting this demand. With over 33 million km2, permanent grasslands account for ~ 25% of the world’s land cover. Over two thirds of this area is utilised for agriculture, making it the most dominant land use5. Sustainably increasing grassland productivity is therefore crucial to ensure future global food security6,7.
Phosphorus (P) is an essential nutrient, often limiting plant growth8. P fertilisation is therefore needed to sustain productivity in agricultural systems across the world. Because the world’s P reserves are decreasing, the importance of judicious P use will increase over the coming century. Although estimates of global P reserves vary, the costs of high quality P fertilisers will increase, as will the global demand for these fertilisers9,10,11,12. Differences in climate, geography, agricultural development, and fertilisation practices have led to great global imbalances of P in agricultural land13,14,15,16. In parts of Europe, North America, and China, historical applications of manure and fertilisers have resulted in positive P balances and increased risk of eutrophication of surface waters17. In many other regions, predominantly in tropical areas, farmers struggle to maintain soil P availability to sustain optimal rates of crop production18. Recent predictions suggest that global P inputs in grasslands will have to increase fourfold to support an 80% increase in grass yield projected for 205015, which implies an urgent need to increase use efficiency of P fertiliser sources.
The large diversity in agronomic P status of soils across the world and the projected increase in cost and demand of P fertilisers necessitate a rethink of the use of P resources: are we applying fertilisers at the right rates to the right soils? The success of fertiliser application depends on conditions created by climate and management19,20 and is strongly governed by soil properties such as pH and concentrations of metal oxides and Ca in soil that can impact P availability to plants8,21,22. However, data for these relationships are fragmentary and country- or region-specific, and global assessments are lacking23,24. Here we use a meta-analysis on a global database of 67 studies and 1227 observations with a wide range of soil properties and climatic conditions to assess the general effect of P fertilisation on grassland production across the world. Furthermore, we identify soil-related driving factors that determine the success of fertiliser applications.
Our dataset included data from field grasslands all over the world (Supplementary Fig. 1). Most studies originated from Europe and North America, but due to several studies with many observations from the Australian continent, there were almost as many observations from Oceania. We analysed our dataset using two different metrics: the response ratio (RR) as measure for the relative increase in dry matter yield as a result of P fertilisation, and P agronomic efficiency (PAE) expressing the absolute yield increase per unit of P applied.
Factors controlling the success of phosphorus fertilisation
P fertilisation increased grassland yield by 37% (95% confidence interval: 33 to 40%; Fig. 1; Supplementary Table 3) averaged over all grasslands, soil types, and fertility levels, resulting in a PAE of 32 kg kg−1 (Fig. 2; Supplementary Table 4). In other words, dry matter yields increased by 32 kg per kg of P applied on average. Yield responses to P additions increased with P application rates: rates below 25 kg P ha−1 increased yields by 40% on average, whereas applying over 100 kg P ha−1 increased grass yield by 65%. An exception to this pattern were grasslands fertilised with 25–50 kg P ha−1, which responded to a lesser extent than those in other categories. This is likely an artefact due to a relatively high average soil P status of studies included in this category (Supplementary Fig. 3), which may have led to high yields in the control treatments. The PAE, on the other hand, decreased with P application rates (Supplementary Table 4): yields increased by 53 kg per kg P applied at rates lower than 25 kg P ha−1, but only by 12 kg kg−1 P at rates higher than 100 kg P ha−1. This indicates that finding a balance between P input and yield response is crucial for optimising fertiliser effectivity, as the agronomic efficiency decreases with higher application rates.
Figure 1

Impact of phosphorus (P) fertilisation for the controlling factors crop, P rate, climate, and mean annual temperature expressed as relative yield increase per category. The 95% confidence intervals are represented by the error bars, and the number of studies and observations per category are between parentheses; *,**,***Significant controlling factor effect at an α of 0.05, 0.01 and 0.001, respectively.

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Figure 2

The Phosphorus agronomic efficiency (PAE) for different controlling factors per subgroup. The effect is expressed for crop, climate, and P status (Olsen-equivalent) × P rate (c). Low SPT: ≤ 10 mg P kg−1; high SPT:  > 10 mg P kg−1; low rate: P rate ≤ 50 kg P ha−1; high rate: P rate  > 50 kg P ha−1 The 95% confidence intervals are represented by the error bars, and the number of studies and observations per category are between parentheses; *, **,***Significant controlling factor effect at an α of 0.05, 0.01 and 0.001, respectively.

Full size image

Systems that included legumes responded more strongly to P fertilisation than systems without legumes (Fig. 1). On average, P fertiliser increased yield in grass/legume systems by 54%, but only by 25% in grassland systems without legumes. These numbers corresponded with a PAE of 46 kg kg−1 for grass/legume and 22 kg kg−1 for grass-only systems, meaning that P fertilisation was roughly twice as effective in grasslands with legumes than in those without legumes. Legumes like alfalfa and clover are regularly included in grassland mixtures, mainly because they provide extra N inputs to the plant-soil system by establishing a symbiosis with N-fixing microorganisms23. These results likely reflect that legumes generally require more P than grasses, and can acquire it less easily due to thicker roots and shorter root hairs11,25,26.
In our database, more than half (36) of the studies included more than one N treatment. Overall, the N application rate had little effect on the response of grasslands to P fertilisation. There was no significant effect of N rate on the PAE (Supplementary Table 4). Yield responses to P fertiliser at N application rates over 200 kg N ha−1 were slightly but significantly smaller than at lower N rates (Supplementary Table 3). However, if N limitation of the grasslands would have played a prominent role, a general increase in response to P fertiliser with increasing N rate would have been observed. These results suggest that differences in yield responses were mainly driven by a response to P fertilisation rather than to N fertilisation.
Geographical variation in responses
P application increased grassland yields in tropical regions (i.e. latitudes ≤ 35°) significantly more strongly than in temperate grasslands (Fig. 1, Supplementary Table 3). However, because yields of tropical grasslands were relatively low, the PAE of fertiliser application did not differ significantly between the two regions (34 and 31 kg kg−1 for tropical and temperate regions, respectively; Fig. 2, Supplementary Table 4). These results likely reflect that soils in (sub)tropical regions are often highly weathered, nutrient-poor, and have a low P availability due to high abundancy of adsorbents like Al and Fe oxides8. In contrast, decades of manure and fertiliser applications have resulted in a build-up of soil P levels well beyond crop requirements and a corresponding decrease in yield response to P fertiliser application17,27 in many temperate regions (e.g. North America, Europe, and New Zealand). The differences in response of temperate and tropical grasslands are also reflected in the results for mean annual temperature (MAT; Fig. 1, Supplementary Table 3), with grasslands in colder regions (MAT  20 °C reacting the strongest. Higher temperatures may lead to more rapid plant production and to an increase in mineralisation of organic matter. Correlation analysis of the controlling factors showed that MAT and latitude among our studies were strongly correlated (Supplementary Fig. 4; Spearman’s ρ = -0.95).
Yield responses to P fertilisation were significantly smaller in Asia, North America, and Europe (+ 15 to + 29%) than in South America, Oceania, and Africa (+ 58 to + 94%). The PAE ranged from 12 kg kg−1 for studies in Asia to 74 kg kg−1 for studies in Oceania and even 117 kg kg−1 for the one African study included in our dataset (Supplementary Table 4). The continents with grasslands that showed a strong response to P fertilisation roughly coincide with the areas that have relatively low P inputs and outputs, as modelled by Sattari et al.15. Taken together, these results imply that Africa and Oceania with low P inputs responded strongly to P fertilisation whereas grasslands in Europe, North America and Asia with relatively high P inputs over the past decades, showed a weak response to P fertilisation.
Do we apply phosphorus fertilisers to the right soils?
We used various soil parameters as controlling factors (Table 1) to identify what soil properties drive differences in yield response to P fertilisation. One of the most important parameters is the agronomic P status of the soil, which is commonly determined with a soil P test (SPT). Because soil type, climate, and crop response vary considerably across the world, each country and sometimes even region has its own SPT method and classification system28,29. Given this large variety of SPT procedures (and resulting P concentrations) in use, we applied conversion formulas published in peer-reviewed papers to express reported SPT values in our database as ‘Olsen-equivalent’ P values wherever possible (see Supplementary Methods).
Table 1 Controlling factors and categories distinguished in the meta-analysis.
Full size table

Grasslands on soils with low SPT values (≤ 5 mg P kg−1) responded strongest to P fertilisation with a yield increase of 110% on average (Fig. 3, Supplementary Table 3). Conversely, P additions to soils with SPT values  > 5 mg P kg−1 increased yields by 7–25%. Although yield response decreased dramatically with increasing SPT values, the responses at relatively high SPT values (10–25 and  > 25 mg P kg−1) were still statistically significant. Critical values (that is, SPT levels for which the yield is 95% of the maximum yield) for grass of 23–25 mg kg−1 Olsen P have been reported previously for English grasslands30, which coincides with the limited yield response for soils in the highest SPT category. A study of 25 Spanish soils also showed an average critical SPT of 24 mg Olsen P kg−1 for ryegrass, although there was a wide spread for individual soils, ranging from 11 to 46 mg kg−131. For a range of Australian grassland species, however, lower critical SPT values (between 9 and 15 mg kg−1) have been determined32. This variety of critical SPT values found in literature illustrates that the effect of P fertilisation is strongly dependent on soil, climate, and even grassland species. Therefore, our results here do not give a hard SPT limit beyond which further P applications are rendered ineffective, but do indicate a strong decrease in effectiveness at higher SPT values.
Figure 3

Effect of different soil characteristics on the impact of phosphorus (P) fertilisation. The effect is expressed for soil P status based on Olsen P-equivalent, soil pH, organic matter content, and clay content. The 95% confidence intervals are represented by the error bars, and the number of studies and observations per category are between parentheses; *, **,***Significant controlling factor effect at an α of 0.05, 0.01 and 0.001, respectively.

Full size image

The strong yield response to P fertilisation on soils with low SPT values was not merely the result of a low yield of the control treatments. PAE was also highest (75 kg kg−1) for soils with SPT ≤ 5 mg P kg−1 (Supplementary Table 4) and fertilisation on these soils was 3 to 8 times as effective as on soils with higher SPT values in terms of absolute yield increases. Without correcting for the P application rate, absolute yield responses (average yield of treated plots minus average yield of control plots) to P fertilisation varied substantially (− 2.7 to 11.3 tonnes ha−1; Supplementary Fig. 5). The largest response (on average 2.7 tonnes ha−1 increase) and variation to P fertilisation were found for soils in the lowest SPT category. The yield response decreased with higher SPT (Supplementary Fig. 5). Figure 2 shows that both relatively low (≤ 50 kg P ha−1) and relatively high ( > 50 kg P ha−1) P application rates on soils with a low P status (≤ 10 mg P kg−1 Olsen-equivalent) were more effective than any P fertilisation rate on soils with a relatively high P status ( > 10 mg P kg−1 Olsen-equivalent). The high PAE of large application rates on soils with a low P status (Fig. 2) may be the result of the binding behaviour of P in soil: in soils with a low P status (where relatively more P adsorption occurs), relatively high P inputs are required to raise the level of plant-available P, so grassland on these soils will benefit relatively more from high application rates. Conversely, applying large amounts of P to soils with a relatively high P status ( > 10 mg P kg−1) showed a low PAE.
Yield responses to P applications were highest on grasslands with a soil pH of 5–6 (60% yield increase; Fig. 3) whereas lower and higher pH levels resulted in lower (11–26%) yield responses. We observed the same pattern for PAE, where studies with a pH of 5 to 6 had a 50 kg yield increase per kg of P fertilised, whereas for soils with a pH above 7 this was only 11 kg (Supplementary Table 4). Soil pH is a crucial parameter in determining the availability of P to crops8. In acidic mineral soils, binding of P to Fe and Al (hydr)oxides is often the main factor that governs the level of plant available P. In contrast, in soils with pH values above 7, P is more likely to form poorly soluble Ca-P precipitates, decreasing plant available P. The relative availability of soil P is highest at soil pH levels of 5 to 733,34, which would imply that around this pH fertiliser P application would yield the strongest responses.
We found a positive correlation between the soil organic matter (OM) content and yield response to P fertilisation (Fig. 3). On average, P application increased yield by only 11% on soils with an OM content below 2% (PAE was 7.2 kg kg−1 on average and this effect was not statistically significant). Yield responses were much higher (41–80%) in soils with an OM content of  > 5%. The PAE was 9 times as high in soils with  > 5% OM as in soils with  More

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We estimated the abundance of desert bighorn sheep in a captive facility located within the Chihuahuan Desert of New Mexico (Fig. 1). The area is arid, mountainous, with steep cliffs punctuated by ravines. The entire facility is enclosed by a high fence, preventing desert bighorn sheep ingress or egress. These animals are wild, unconditioned to humans, and used by The New Mexico Game and Fish Department (NMDGF) to establish and bolster other desert bighorn sheep populations within New Mexico16.
NMDGF leads an annual spring census to enumerate desert bighorn sheep numbers within the facility. Methodologically, the census uses a ground crew of spaced individuals walking in a line perpendicular to pen fencing, within the facility (i.e., drive count or census). Each individual keeps track of neighboring individuals to space the line, and to count any desert bighorn sheep breaking past them. Most desert bighorn sheep are herded ahead of the line. Other biologists at high topographical sites use spotting-scopes and binoculars to count and age class the moving sheep. Census counts are classified by animal ages and sex. We consider young sheep any animal ≤ 1.5 years old. Adult rams and ewes consist of males and females aged  > 1.5 years old, respectively. The group “adult” includes all sheep  > 1.5 years old.
We established 11 motion activated camera traps (Bushnell Trophy Cam) at the centers of an 800-m grid with a random geographical start. Cameras were secured to T-posts or existing vegetation when rocky areas thwarted post establishment. Most cameras were oriented north, to minimize sun exposure in the imagery. When vegetation or rock blocked the camera view, the camera orientation was rotated eastward until a clear view was obtained. Camera heights were 0.9–1.2 m with declination perpendicular to the ground. Cameras were checked at 6 month intervals and the retrieved SD cards were never full. Cameras were motion activated and set at the shortest delay possible (10 s; meaning that the camera waits at least 10 s after recording a picture before it will record another). In practice, the fastest trigger speed that cameras recorded imagery was a mean of 14.92 s (N = 6 cameras; SD = 0.94), a value we rounded to 15 s and employed in our analyses. Cameras recorded one image per trigger. We deployed cameras by 15 May 2017 and retrieved cameras no earlier than 30 April 2018. Our analyses period began on 1 October 2017 and ended on 1 May 2018. We employed a 5 month acclimation period to avoid the cameras serving as a novel attractant for desert bighorn sheep, which would violate distance sampling assumptions. Further, we experienced 1 camera failure during this acclimation period, and relocated 2 misplaced cameras. Lastly, some desert bighorn sheep gathered in shady locations near cameras during hot months (June–September) which created extreme variation in the encounter rate. The 1 October 2017–1 May 2018 period lacked all of these issues.
Imagery of desert bighorn sheep were identified and then classified by sex and age class: rams, ewes, young, adults (an adult-sized animal with sex undiscernible), and unknown [undiscernible (e.g. picture of a hoof, or animals blocking a clear view another animal)17]. To quantify distances, an individual stood at each camera and used the printed images of desert bighorn sheep to position another person at the exact locations that an animal was imaged. Distances between individuals were measured with a laser rangefinder and metric tape (Fig. 2; the authors confirm that informed consent was obtained to publish the identifying information/images in an online open-access publication). We supplemented these measurements by recording distances between the camera and several recognizable objects in the images (e.g. rocks, plants), to ensure accurate distance delineations for the imaged sheep.
The sensitivity of a trail camera’s passive infrared sensor (PIR) will decline as radial distance from the camera increases. Other factors, like vegetation and topography, also cause animal detections to decline with distance. This situation makes distance sampling an appealing approach, as its’ foundational premise is that the probability of detecting an animal declines with distance from the observation point15. The technique relies on measured distances between animals and the observation point. Therefore, we used the distance measures of desert bighorn sheep to the respective trail camera imaging them, to estimate sheep abundances using the ‘distance’ package (version 0.9.812) in program R. We analyzed data using a fall period (1 October 2017–31 January 2018) and a spring period (1 March 2018–1 May 2018). Dates for these periods were selected by season while ensuring sufficient sample sizes.
The camera trap operates like a point count sample. The sampling angle for each camera’s field of view was 50°18. Therefore, we multiplied sampling effort by this fraction (50/360) to correctly represent the amount of sampling area.
When analyzing data within a distance sampling approach, obtaining unbiased density estimates relies on correspondence between the sampling period and the period describing when the target species are active and therefore available for detection10,15. In our situation, this means aligning the sampling period of distance measurements with the period of time that desert bighorn sheep were active and able to trigger the trail camera10. In our study, we acquired very few nocturnal images of desert bighorn sheep (Fig. 4). Therefore, we censored study effort and distance data to the period defined by 1 h before sunrise through 2 h after sunset for each month, on a daily basis. Doing so removed  More

A comprehensive description of the methodological aspects used during the field surveys and image analysis have been published in González-Rivero et al.23,25,35. Therefore, here we include a synopsis of how this dataset was generated and made available to the wider community.
Our approach involved the rapid acquisition of high-resolution imagery over large extent of reefs and efficient image analysis to provide key information about the state of coral reef benthic habitat across multiple spatial scales23. The data generation and processing involved three main components: (1) photographic surveys, (2) post-processing of images and (3) image analysis, which are described and summarised below in Fig. 1.
Fig. 1

The workflow for generating the global dataset of coral reef imagery and associated data. The 860 photographic surveys from the Western Atlantic Ocean, Southeast Asia, Central Pacific Ocean, Central Indian Ocean, and Eastern Australia, were conducted between 2012 and 2018. Reef locations are represented by points colour-coded according to the survey region. Surveys images were post-processed in order to transform raw fish-eye images into 1 × 1 m quadrats for manual and automated annotation (inset originally published in González-Rivero et al.23 as Figure S1). For the image analysis, nine networks were trained. For each network, images were divided in two groups: Training and Testing images. Both sets were manually annotated to create a training dataset and verification dataset. The training dataset was used to train and fine-tune the network. The fully trained network was then used to classify the test images, and contrast the outcomes (Machine) against the human annotations (Observer) in the test dataset during the validation process. Finally, the non-annotated images (photo-quadrats) were automatically annotated using the validated network. The automated classifications were processed to originate the benthic covers that constitute this dataset. QGIS software was used to generate the map using the layer “Countries WGS84” downloaded from ArcGIS Hub (http://hub.arcgis.com/datasets/UIA::countries-wgs84).

Full size image

Photographic surveys
An underwater propulsion vehicle customised with a camera system (“SVII”, Supplementary Fig. 1), consisting of three synchronised DSLR (Digital Single-Lens Reflex) cameras (Cannon 5D-MkII cameras and Nikon Fisheye Nikkor lens with 10.5 mm focal length), was used to survey the fore-reef (reef slope) habitats from five major coral reef regions: Central Pacific Ocean, Western Atlantic Ocean, Central Indian Ocean, Southeast Asia and Eastern Australia in 23 countries or territories (Table 1, Supplementary Fig. 2). Within each region, multiple reef locations were surveyed aiming to capture the variability and status of fore-reefs environments across regions and within each region. Sampling design varied according to particular environmental and socioeconomic factors potentially influencing the distribution and structure of coral reef assemblages at each region and/or country. Overall, prior to field expeditions, reef localities were selected considering factors such as wave exposure, reef zones (i.e. fore-reefs), local anthropogenic stressors (e.g. coastal development), fishing pressures, levels of management (e.g. marine park, protected areas), and presence of monitoring sites.
Table 1 Summary of the photographic surveys conducted between 2012 and 2018.
Full size table

Underwater images were collected in each reef location once every three seconds, approximately every 2 m apart, following a transect along the seascape at a standard depth of 10 m (±2 m). Although overlap between consecutive images is possible, the process for extracting standardised photo-quadrats from an image ensures that the photo-quadrats are non-overlapping between and within images (see further details next section). Each transect averaged 1.8 km in length, hereafter referred to as a “survey”. See Supplementary Fig. 3 for an explanation of the hierarchical structure of the photographic surveys. No artificial illumination was used during image capture, but light exposure was manually adjusted by modifying the ISO during the dive, using an on-board tablet computer encased in an underwater housing (Supplementary Fig. 1). This computer enabled the diver to control camera settings (exposure and shutter speed) according to light conditions. Images were geo-referenced using a surface GPS unit tethered to the diver (Supplementary Fig. 1). Altitude and depth of the camera relative to the reef substrate and surface were logged at half-second intervals using a Micron Tritech transponder (altitude, Supplementary Fig. 1) and pressure sensor (depth) in order to select the imagery within a particular depth and to scale and crop the images during the post-processing stage. Further details about the photographic surveys are provided in González-Rivero et al.25,35.
Post-processing of images for manual and automated annotation
The post-processing pipeline produced images with features required for manual and automated annotation in terms of size and appearance. The process involved several steps that transformed the raw images from the downward facing camera into photo-quadrats of 1 m2, hereafter referred to as a “quadrat” (Fig. 1). As imagery was collected without artificial light using a fisheye lens, each image was processed prior to annotation in order to balance colour and to correct the non-linear distortion introduced by the fisheye lens23 (Fig. 1). Initially, colour balance and lens distortion correction were manually applied on the raw images using Photoshop (Adobe Systems, California, USA). Later, in order to optimise the manual post-processing time of thousands of images, an automatic batch processing was conducted on compressed images23 (jpeg format) using Photoshop and ImageMagick, the latter an open-source software for image processing (https://imagemagick.org/index.php). In addition, using the geometry of the lens and altitude values, images were cropped to a standardised area of approximately 1 m2 of substrate23,35 (Fig. 1). Thus, the number of nonoverlapping quadrats extracted from one single raw image varied depending on the distance between the camera and the reef surface. Figure 1 illustrates a situation where the altitude of the camera allowed for the extraction of two quadrats from one raw image. Further details about colour balance and lens distortion correction and cropping are provided in González-Rivero et al.23,35.
Image analysis: manual and automated annotation for estimating covers of benthic categories
Manual annotation of the benthic components by a human expert took at least 10 minutes per quadrat, creating a bottleneck between image post-processing and the required data-product. To address this issue, we developed an automated image analysis to identify and estimate the relative abundance of benthic components such as particular types of corals, algae, and other organisms as well as non-living components. To do this, automated image annotation based on deep learning methods (Deep Learning Convolutional Neural Networks)23 were applied to automatically identify benthic categories from images based on training using human annotators (manual annotation). The process for implementing a Convolutional Neural Network (hereafter “network”) and classify coral reef images implied three main stages: (i) label-set (benthic categories) definition, (ii) training and fine-tuning of the network, and (iii) automated image annotation and data processing.
Label-set definition
As a part of the manual and automated annotation processes to extract benthic cover estimates, label-sets of benthic categories were established based on their functional relevance to coral reef ecosystems and their features to be reliably identified from images by human annotators25. The labels were derived, modified and/or simplified from existing classification schemes40,41, and were grouped according to the main benthic groups of coral reefs including hard coral, soft coral, other invertebrates, algae, and other. Since coral reef assemblages vary in species composition at global and regional scales, and surveys were conducted at different times between 2012 and 2018 across the regions, nine label-sets accounted for such biogeographical and temporal disparity. In general, a label-set was developed after each main survey expedition to a specific region. The label-sets varied in complexity (from 23 to 61 labels), considering the differential capacity to visually recognise (in photographs) corals to the lowest possible taxon between the regions. While label-sets for the Atlantic and Central Pacific (Hawaii) included categories with coral genus and species, for the Indian Ocean (Maldives, Chagos Archipelago), Southeast Asia (Indonesia, Philippines, Timor-Leste, Solomon Islands, and Taiwan), and Eastern Australia, corals comprised labels based on a combination of taxonomy (e.g., family and genus) and colony morphology (e.g., branching, massive, encrusting, foliose, tabular).
The other main benthic groups were generally characterised by labels reflecting morphology and/or functional groups across the regions. “Soft Corals” were classified into three groups: 1) Alcyoniidae (soft corals), the dominant genera; 2) Sea fans and plumes from the family Gorgoniidae; and 3) Other soft corals. “Algae” groups were categorised according to their functional relevance: 1) Crustose coralline algae; 2) Macroalgae; and 3) Epilithic Algal Matrix. The latter is a multi-specific algal assemblage smothering the reef surface of up to 1 cm in height (dominated by algal turfs). “Other Invertebrates” consisted of labels to classify sessile invertebrates different to soft corals (e.g., Millepora, bryozoans, clams, tunicates, soft hexacorrallia, hydroids) and some mobile invertebrates observed in the images (mostly echinoderms). The remaining group, “Other”, consisted of sand, sediments, and occasional organisms or objects detected in the images such as fish, human debris (e.g., plastic, rope, etc.), and transect hardware. The exception within these main groups were the “Sponges”, which were classified and represented by multiple labels only in the Atlantic (given their abundance and diversity in the Caribbean), including categories with sponge genus and species, and major growth forms (rope, tube, encrusting, massive).
Training and fine-tuning of the network
The deep learning approach used relies on a convolutional neural network architecture named VGG-D 1642. Details on the initialisation and utilisation of this network are provided in González-Rivero et al.23. A total of nine networks were used, one for each country within the regions, except for the Western Atlantic Ocean, where the network was trained using data from several countries, and the Philippines and Indonesia, where the network was trained using data from those two countries. (Table 2). The first step in implementing a network was to randomly select a subset of images from the whole regional set to be classified, which were then divided into training and testing sets (Fig. 1). Human experts manually annotated both sets using the corresponding label-set under CoralNet43, an online platform designed for image analysis of coral reef related materials (https://coralnet.ucsd.edu/). The number of images and points manually annotated per network is presented in Table 2 (generally 100 points per image for training sets and 40 or 50 points per image for testing sets).
Table 2 Summary of the images, manual point annotations, and test transects used during the train and test processes of each network.
Full size table

Each training and testing data set were exported from CoralNet43 and used along with the associated quadrats to support an independent training and fine-tuning process aimed to find the network configuration that produced the best outcomes. Initially, each quadrat used from the training and testing sets was converted to a set of patches cropped out around each annotation point location. The patch area to crop around each annotation point was set to 224 × 224 pixels to align with the pre-defined image input size of the VGG-D architecture. The fine-tuning exercise ran in general for 40 K iterations to establish the best combination of model parameters or weights that minimised the cross-entropy loss while the overall accuracy increased. An independent 20% subset from the original set of quadrats was used to assess the performance of the final classification (% of accuracy). In addition, parameters of learning rate and image scale were independently optimised for each network by running an experiment using different values for such parameters in order to select the values that derived the smallest errors per label. Further details of the model parametrisation for each network are provided in González-Rivero et al.23 (see Supplementary Material).
Automated image annotation and data processing
Once optimised, a network was used to automatically annotate the corresponding set of non-annotated quadrats. The quadrats were processed through the network, where for each quadrat, 50 points (input patches) were classified using the associated labels. Upon completion of automated image annotation for a specific region/country, the annotation outputs containing locations of 50 pixels (i.e., their x and y coordinates) with their associated labels per quadrat (a csv file per quadrat) were incorporated and collated into a MySQL database along with information about the field surveys. In addition to the manual and automated annotations tables (raw data), we provide two levels of aggregation for the benthic data. First, the relative abundance (cover) for each of the benthic labels per quadrat, which was calculated as the ratio between the numbers of points classified for a given label by the total number of points evaluated in a quadrat. Second, the relative abundance for each of the main benthic groups (hard coral, soft coral, other invertebrates, algae, and other) per survey, which involved three calculations: 1) summarise the quadrat covers by image averaging all the quadrats from one single image per label, 2) summarise image covers by survey averaging all the images across one survey per label, and 3) merge survey data by main benthic groups summing the covers of all labels belonging to the same group across one survey. More

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