Daniel S. Maynard’s research while affiliated with University College London and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (7)


The large-scale study of Paraserianthes lophantha macroclimatic niche dynamics involves a comparison between niches from its Australian native range and its Australian invasive range (AN-AI comparison), from its Australian native range and its European invasive range (AN-EI), and from its full Australian range (native and invasive) and its European invasive range (AF-EI). The fine-scale study of niche overlap between P. lophantha and Quercus lusitanica involves comparisons between the niches occupied by both species in the local area of Monte Pindo in northwest Spain. Source: Reproduced from Santamarina et al..
Editorial: The ecological niche at different spatial scales
  • Article
  • Full-text available

November 2023

·

89 Reads

·

3 Citations

·

·

Daniel S. Maynard

·

Download

Global coverage of forest inventory locations (GFBi data) and plot-level leaf-type proportions
a, A total of 9,781 forest inventory plots (green points) were used for geospatial modelling of forest leaf types. b, Number of plots in relation to their proportion of evergreen vs deciduous and broadleaved vs needle-leaved individuals.
The global distribution of forest leaf types
a, The global distribution of tree leaf type as predicted by a random forest model built from area-based leaf-type data (see Methods). Pixels are coloured in the red, green and blue spectrum according to the percentage of total tree basal area occupied by broadleaved evergreen, broadleaved deciduous and needle-leaved tree types, as indicated by the ternary plot. Needle-leaved evergreen and needle-leaved deciduous forests were combined due to the low global coverage of needle-leaved deciduous trees. b–e, Predicted relative coverage of each leaf type from random forest models. Ref. ⁸¹ was used to mask non-forest areas. b, Broadleaved evergreen coverage. c, Broadleaved deciduous coverage. d, Needle-leaved evergreen coverage. e, Needle-leaved deciduous coverage.
Variable importance of environmental covariates on forest leaf-type variation
a,b, Cumulative importance of the first six principal components of climate, soil, topographic and vegetation covariates in the variation of leaf habit (a) and leaf form (b). c,d, Variable importance of selected environmental features on variation in leaf habit (c) and leaf form (d). Bars in c and d represent the mean ± 95% CI; relative importance based on the 10 best random forest models (n = 10; see Methods). Area-based leaf-type proportions were used to represent forest (plot-level) leaf-type variation.
The global proportion of evergreen broadleaved, deciduous broadleaved, needle-leaved evergreen and needle-leaved deciduous trees
The relative proportions of trees that occur within tropical, temperate, boreal and arid regions are shown as separate pie charts for each leaf type.
Forested areas where future climates may no longer support prevailing leaf types
If a pixel’s forest area was predominantly (>60%) covered by one leaf type, it was classified as that specific leaf type. Pixels where no leaf type exceeded 60% coverage were classified as mixed forest. To determine the relative proportion of each leaf type per plot, we considered the basal area of individual trees (area-based leaf type). Coloured pixels on the map indicate areas that, by the end of the century (2071–2100), will face climate conditions that currently support a different forest type. The future climate conditions were represented using three climate change scenarios: low-emission (SSP1–RCP2.6; a,b), business-as-usual (SSP3–RCP7; c,d) and high-emission (SSP5–RCP8.5; e,f) for the period 2071–2100. Panels a, c and e show the present forest types. In contrast, panels b, d and f show the type of forest expected under the projected future climate of each respective pixel.
The global biogeography of tree leaf form and habit

October 2023

·

3,388 Reads

·

17 Citations

Nature Plants

·

·

·

[...]

·

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.



Figure 2. SEED biocomplexity index. Example visualization of the SEED framework for an area of interest, showing the dimensionality-reduced values across 3 axes for each of the 3 scales of variation (genetic, species, and ecosystems). For each axis, the visualization shows the difference between the current state (yellow), and the potential natural state based on a comparable, minimallydisturbed ecosystem (white). The distance between the current and potential measures creates a score -the SEED biocomplexity index -which ranges from 0 and 1, where 0 represents a complete absence of biocomplexity, and 1 represents an area that is equivalent in complexity to its potential natural state. Arrows indicate the relationships between the three scales of variation and the Essential Biodiversity Variables (EBVs).
Figure 3. Assessing the potential state of ecosystems through the identification of relevant reference areas. a) and b) Reference areas representing the 5% least disturbed areas within each combination of ecoregion and land cover type. c) Potential land cover obtained from 62 , substituting artificial ecosystems with the potential layer from the same study. The artificial class is composed of plantations, arable land, pasture, urban areas and rural gardens. d) Potential land cover types at a local scale in Gabon, indicating which reference areas from (b) are used to calculate biodiversity values for those areas. e) Ecosystem structural integrity, obtained by comparing ecosystems with their reference. Structural components include canopy height, homogeneity, LAI, and forest cover, above-ground and below-ground biomass. f) Violin plots of SEED structural absolute values and integrity across different land-use categories.
Figure 4. Comparative analysis of the SEED Index. a) Global representation of the SEED index. b) Comparisons between the SEED index and MSA (left) or BII (right). c) The SEED index in the country of Gabon and surrounding areas. d) SEED index densities across six land cover classes in Gabon. The artificial class is composed of plantations, arable land, pasture, urban areas and rural gardens.
Figures
Assessing the multidimensional complexity of biodiversity using a globally standardized approach

August 2023

·

1,726 Reads

·

1 Citation

Quantifying biodiversity across the globe is critical for transparent reporting and assessment under the Kunming-Montreal Global Biodiversity Framework. Understanding the full complexity of biodiversity requires consideration of the variation of life across genetic, species and ecosystem levels. Achieving this in a globally-standardized way remains a key international challenge for biodiversity monitoring efforts. Here, we present the Sustainable Ecology and Economic Development (SEED) framework, which consolidates multiple dimensions of biodiversity into a single measure of biocomplexity as a holistic estimate of the current state of nature at a given location. The SEED framework continuously integrates state-of-the-art datasets and maps of the biological variation in plants, microbes, animals, and ecosystems to estimate the local biocomplexity across the planet relative to a comparable, minimally-disturbed ‘reference’ ecosystem. The SEED framework allows an assessment of ecological health in response to positive and negative human impacts, and informs decision makers who strive to improve the global state of nature.


Distribution of the study data
Distribution of the full study dataset, coded for non-native severity (n = 471,888 plots). The map shows average per cent invasion across a 1-degree hexagonal grid, from non-invaded (0%) pixels in green to completely invaded (100%) pixels in purple. Plots are considered invaded if there is any non-native tree present.
Anthropogenic drivers are more important than native diversity in determining invasion occurrence
a,b, Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on non-native presence (whether a plot is invaded or not) for global models of phylogenetic (a) and functional (b) diversity (phylogenetic diversity, n = 17,640 plots; functional diversity, n = 17,271 plots). All results shown are from random forest models. Note that y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.
Native diversity is the most important driver of invasion severity
a,b, Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on invasion severity for global models of phylogenetic (a) and functional (b) diversity (phylogenetic diversity, n = 3,498 plots; functional diversity, n = 3,368 plots). Plots are shown for the severity of invasion measured as non-native species abundance (proportion of basal area with non-native plant species); plots for non-native species richness (proportion of non-native plant species) are shown in Extended Data Fig. 4. All results shown are from random forest models. Note that the y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.
Environmental filtering at temperature extremes
a,c, Estimates of overlapping variables included in temperate and tropical GLM models (forest plot) for phylogenetic (a) and functional (c) diversity models (phylogenetic diversity, n = 3,498; functional diversity, n = 3,368). Values to the left of the zero line indicate negative model estimates, and those to the right indicate positive estimates. b,d, Relationship between mean annual temperature and invasion strategy for phylogenetic (b) and functional (d) diversity models, showing that at extreme temperatures invasion occurs through similarity (Supplementary Table 7; phylogenetic diversity: P(1) = 9.69 × 10⁻¹⁴, P(2) = 2.13 × 10⁻¹¹; functional diversity: P(1) < 2 × 10⁻¹⁶, P(2) = 1.07 × 10⁻⁴, where P(1) and P(2) represent each temperature and temperature squared P values, respectively). Note for functional diversity, this pattern only holds at low temperatures. Error bars and bands represent standard error.
Proximity to ports weakens environmental filtering in the temperate bioclimate zone
a,b, In temperate plots far from ports, temperature is positively correlated with an invasion strategy of increasing dissimilarity for phylogenetic (a) and functional (b) diversity (phylogenetic diversity: n = 2,710 plots, P = 6.37 × 10⁻⁶; functional diversity: n = 2,603, P < 2 × 10⁻¹⁶). c,d, This relationship between temperature and invasion strategy weakens for phylogenetic (c) and functional (d) diversity with proximity to ports (Supplementary Table 7; phylogenetic diversity: P = 0.0001; functional diversity: P = 2.71 × 10⁻¹³). Lines and points represent the lowest (c,d) and highest (a,b) 10% of data. Error bands represent standard error.
Native diversity buffers against severity of non-native tree invasions

August 2023

·

2,637 Reads

·

40 Citations

Nature

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.


Four hypotheses describing how evenness interacts with the relationship between richness and productivity. The four levels of evenness are indicated with different colours from low evenness (yellow) to high evenness (red), and lower colour intensity indicates a lower data density. The dashed black line indicates the average relationship across the system. In all hypotheses, it is assumed that species richness has a positive effect on productivity (Balvanera et al., 2006; Cardinale et al., 2007; Hooper et al., 2005). If community evenness interacts with the effect of species richness on productivity, then (1) the effect of species richness on productivity depends on the evenness of the community, and (2) richness is correlated with evenness across the system (either positively or negatively). If there is no interaction between richness and evenness (Hypothesis A) or if there is no correlation between evenness and richness (Hypothesis B) (Ma, 2005), then the average effect (dashed line) of richness on productivity will neither attenuate nor increase at high richness levels. In such instances, the observed decrease in productivity at high richness levels is more likely a by‐product of other ecological processes (e.g. functional redundancy). If, however, there is a significant interaction between richness and evenness, such that uneven communities have lower productivity at high richness (Hypothesis C), then a negative correlation between richness and evenness (Cook & Graham, 1996; Hanlin et al., 2000; Symonds & Johnson, 2008) would lead to an attenuation in productivity at high richness level: the marginal trend (dashed line) first tracks the high evenness isocline at low richness levels but then bends down towards the low evenness isoclines at high richness. Conversely, if uneven communities exhibit higher productivity at high richness levels (Hypothesis D), then a positive correlation between richness and evenness (Cotgreave & Harvey, 1994; Manier & Hobbs, 2006; Tramer, 1969) would explain this reduction in productivity: the marginal trend first tracks the low evenness isocline at low richness levels but then bends down towards the high evenness isoclines at high richness.
(a) Location of the Global Forest Biodiversity Initiative (GFBI) plots used in this study, where the density of forest plots is indicated from low density (blue) to high density (red). (b) The distribution of evenness and richness in the boreal, temperate and tropical biomes. An evenness value of one resembles either a monospecific stand or an even abundance of species. The tail of richness values of the tropical biomes extends to 380 species (not shown in the graph). The majority of our dataset is composed of secondary forests (mean age is 52 years), and especially the monospecific and relatively species‐poor stands were affected by human activity in some degree.
The relationship between evenness and logged species richness, where the data density is indicated from low density (blue) to high density (red). The Pearson correlation is highly significant for all relationships, we therefore describe the adjusted r² as a measure of effect size. (a) The global relationship between evenness and richness (N = 20,272, r = −0.52, r² = 0.28). (b) The relationship between evenness and richness for the boreal (N = 61,712, r = −0.42, r² = 0.18), temperate (N = 374,142, r = −0.53, r² = 0.28), and tropical (N = 9570, r = −0.48, r² = 0.23) biomes.
(a) Positive (green) and negative (blue) regression coefficients, and (b) variable importance of evenness, richness, the interaction of evenness and richness and climate, soil and human impact variables on biomass and productivity. Only the results for the boreal, temperate, tropical moist forest and all the biomes globally are visualized, and variables causing multicollinearity are taken out (see Section 2). The open circles in (a) indicate non‐significant coefficients, while the filled circles indicate significant coefficients. The adjusted r² values of the linear models are displayed in (a).
The hypothesized effect of different levels of evenness on the relationship between species richness and mean annual biomass accumulation (a–d) or productivity (h–e). The graphs visualize predicted values based on the results of a linear model, with the covariates held constant (see methods), and as a cut‐off point the third quantile of the biomass and NPP values to avoid overfitting. The data are projected on the graph (black line), and the 95% upper and lower confidence intervals are visualized in grey. At the right side of every graph the scaled variable importance, according to a linear model including covariates, of richness (green), evenness (dark blue), and the interaction between richness and evenness (light blue) is visualized. In the graphs at the left side, the global effect is visualized, while at the right side the data are split among boreal, temperate and moist tropical forests. The uncertainty of the biomass calculations and estimated productivity are visualized in Figure S7.
Evenness mediates the global relationship between forest productivity and richness

May 2023

·

2,018 Reads

·

18 Citations

1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale. 2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness–productivity relationship. 3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive. 4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity–ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions.


Rock-Paper-Scissor Dynamics and Intransitive Competition link Ecology and Evolution

September 2022

·

17 Reads

Rock-paper-scissors (RPS) dynamics have been shown to affect the evolutionary relationships within populations. These processes are analogous to the ways in which intransitive competition modifies ecological outcomes and the co-existence between species within communities. Here we explore the similarities between rock-paper-scissor dynamics and intransitive competition and how this link opens new avenues of research into eco-evolutionary processes. Intransitivity can drive the stable coexistence of phenotypes within species, as well as the diversity of species within communities. In addition, the links between these dynamics highlight possible feedback mechanisms that might operate across these evolutionary and ecological scales. Using simulations, we show that greater intraspecific intransitivity within a population can lead greater levels of intransitivity at the community-level, with direct implications for community diversity and stability. As such, RPS dynamics and intransitivity can serve as an ideal conceptual framework to understand the feedback mechanisms that drive diversity across evolutionary and ecological scales.

Citations (6)


... The nutritional niche reflects a species' function and position within an ecosystem, influenced not only by its biological characteristics but also by interactions with other species and environmental factors [44]. Variations within a species' nutritional niche highlight the diversity in resource utilization and environmental adaptation among individuals or groups, which is crucial for adapting to environmental changes and maintaining population stability [45]. ...

Reference:

Feeding Habits of Scomber japonicus Inferred by Stable Isotope and Fatty Acid Analyses
Editorial: The ecological niche at different spatial scales

... In contrast, the leaves of evergreen woody plants are durable and remain throughout the year, so more stable nutrient concentrations with no significant seasonal trends are helpful to maintain the function of the evergreen leaves (Givnish, 2002;Reich & Oleksyn, 2004;Wang et al., 2023). Moreover, evergreen broadleaf plants generally grow in warmer climate conditions with weaker seasonality and this may also benefit the weaker seasonality of foliar nutrients in comparison with deciduous broadleaf woody plants (Axelrod, 1966;Givnish, 2002;Ma et al., 2023;Reich & Walters, 1992). In addition, the seasonality of foliar N concentration in evergreen conifers was found to be significantly higher ECOLOGY than in evergreen broadleaf woody plants. ...

The global biogeography of tree leaf form and habit

Nature Plants

... Consequently, random forest algorithms have been widely used in regression prediction problems [66] and feature classification [67] in the ecological field. In addition, the random forest model can effectively assess and rank the importance of each variable [68]. Therefore, it is also possible to further determine the degree of importance of each factor to the RSEI of mining cities, and this method has been applied in the study of ecological quality changes in mainland China [69]. ...

Author Correction: Native diversity buffers against severity of non-native tree invasions

Nature

... Globally, aggregated metrics are often difficult to understand or relate to, but disaggregation can allow stakeholders to understand policy targets relating to both national and international commitments. Many metrics, however-such as MSA and the Sustainable Ecology and Economic Development frameworl(SEED) [15,17]-are not readily disaggregated, as species identity is not retained through computation. (4) Finally, to be useful in guiding real-world actions that vary in area, it is important that biodiversity metrics provide information that is scalable without the need for extensive additional analysis. ...

Assessing the multidimensional complexity of biodiversity using a globally standardized approach

... Additionally, canopy height plays a pivotal role in characterizing habitat structural heterogeneity as an important factor in explaining biodiversity spatial patterns Marselis et al., 2022;Torresani et al., 2023). Endemic forests represent one of the global biodiversity hotspots and must-preserved ecosystems (Delavaux et al., 2023), but climate change and human pressure are jeopardizing the capability of species to adapt fast enough to resist disturbances due to stand replacement or prolonged heat waves (Anderegg et al., 2015;Hartmann et al., 2018). In the Mediterranean basin, the landscape is undergoing transformations driven by droughts, extreme heat episodes and increasingly recurrent wildfires, impacting carbon fluxes and threatening the habitats of endemic species (Grünig et al., 2023;Moreira et al., 2011;Ruffault et al., 2020). ...

Native diversity buffers against severity of non-native tree invasions

Nature

... In this study, we focused on species richness as a main proxy for species diversity. Yet, it is worth mentioning that other facets of diversity may be impactful on DPRs, such as species evenness (Hordijk et al., 2023). We also added a 'country' effect in the statistical model to take into account differences in sampling design (Table A). ...

Evenness mediates the global relationship between forest productivity and richness