Biology subjects

Knight, T. M.

Publications and source records attributed to Knight, T. M..

13 recordsLinked to original sources

bioRxiv2026

Effects of temperature gradient on flower and fruit traits: a meta analysis

Pollination and seed dispersal by animals are key drivers of terrestrial biodiversity and ecosystem functioning. Effective mutualistic interactions rely heavily on temporal and functional- trait matching between plants and animals. While global warming is known to induce shifts in plant traits, the extent and direction in which higher temperatures may systematically alter flower and fruit traits across species in natural habitats remains poorly understood. Using elevation as a proxy for temperature across natural populations, we conducted a meta-analysis evaluating 21 quantitative functional traits (15 floral, 6 fruit) across 82 studies and 161 species. Standardized mixed-effects linear regression models revealed widespread, systemic responses to elevational temperature gradients in both reproductive structures. In flowers, higher temperatures were systematically associated with changes in petal and sepal width and length (and hence morphology), longevity, nectar volume, number of flowers, and inflorescence length. In fruits, elevation was associated with changes in vitamin C content, crop size, weight and width. Taken together, these results demonstrate that warming temperatures exert widespread, multi-axis effects on the morphology, availability, timing, and nutritional quality of both flowers and fleshy fruits. Given that flower and fruit traits are developmentally linked and co-determine animal visitor dynamics, these temperature-driven phenotypic shifts are likely to propagate cascading disruptions throughout plant-pollinator and plant-frugivore interaction networks under continued climate change.

ecology↗

bioRxiv2025

Climate-driven specialisation in plant-pollinator networks peaks outside the tropics

Pollination is a key ecological process sustaining biodiversity and food security, yet global patterns of plant-pollinator specialisation have remained unresolved. Using the largest global dataset of quantitative networks (>3,400 networks, >110,000 interactions), we show that the latitudinal specialisation gradient (LSG) exists, but it is non-linear, hemispherically asymmetric, and strongly taxon-dependent. Network-level and pollinator specialisation were lowest in the tropics and peaked at northern mid-latitudes, whereas plants tended to become more specialised toward higher latitudes. Climate consistently outperformed latitude, species richness, and environmental productivity as a predictor of these patterns. Specialisation declined with increasing temperature, rose with moderate rainfall before declining at the wettest sites, and increased with temperature seasonality, but plants and pollinators responded differently to these drivers. Functional groups diverged strongly: ectothermic insects were most specialised in cooler, seasonal climates, while birds showed weaker links to latitude but reduced specialisation in wetter regions. These findings demonstrate that climate, rather than latitude or species richness, structures global variation in specialisation. Because warmer and less seasonal climates promote generalisation, climate change is likely to disrupt the most specialised pollination systems, unevenly across taxa and regions, with important consequences for biodiversity and ecosystem stability.

ecology↗

bioRxiv2025

Utilizing CNNs for classification and uncertainty quantification for 15 families of European fly pollinators

Pollination is essential for maintaining biodiversity and ensuring food security, and in Europe it is primarily mediated by four insect orders (Coleoptera, Diptera, Hymenoptera, Lepidoptera). However, traditional monitoring methods are costly and time consuming. Although recent automation efforts have focused on butterflies and bees, flies, a diverse and ecologically important group of pollinators, have received comparatively little attention, likely due to the challenges posed by their subtle morphological differences. In this study, we investigate the application of Convolutional Neural Networks (CNNs) for classifying 15 European pollinating fly families and quantifying the associated classification uncertainty. Our dataset comprises a wide range of morphological and phylogenetic features, such as wing venation patterns and wing shapes. We evaluated the performance of three state-of-the-art CNN architectures, ResNet18, MobileNetV3, and EfficientNetB4, and demonstrate their effectiveness in accurately distinguishing fly families. In particular, EfficientNetB4 achieved an overall accuracy of up to 95.61%. Furthermore, cropping images to the bounding boxes of the Diptera not only improved classification accuracy but also increased prediction confidence, reducing misclassifications among families. This approach represents a significant advance in automated pollinator monitoring and has promising implications for both scientific research and practical applications.

ecology↗

bioRxiv2024

Utilising affordable smartphones and open-source time-lapse photography for monitoring pollinators

Monitoring plant-pollinator interactions is crucial for understanding factors that influence these relationships across space and time. While traditional methods in pollination ecology are time-consuming and resource-intensive, the growing availability of photographic technology, coupled with advancements in artificial intelligence classification, offers the potential for non-destructive and automated techniques. However, it is important that the photographs are of high enough quality to enable insects to be identified at lower taxonomic levels, preferably genus or species levels. This study assessed the feasibility of using smartphones to automatically capture images of insects visiting flowers and evaluated whether the captured images offered sufficient resolution for precise insect identification. Smartphones were positioned above target flowers from various plant species to capture time-lapse images of any flower visitor in urban green areas around Leipzig and Halle, Germany. We present the proportions of insect identifications achieved at different taxonomic levels, such as order, family, genus, and species, and discuss whether limitations stem from the automated approach (e.g., inability to observe distinguishing features in images despite high image quality) or low image quality. Practical recommendations are provided to address these challenges. Our results indicate that for bee families, nearly three quarters of all cases could be identified to genus level. Flies were more difficult, due to the small size of many individuals and the more challenging features needed for identification (e.g., in the wing veins). Overall, we suggest that smartphones are an effective tool when optimised by researchers. As technology continues to advance, smartphones are becoming increasingly accessible, affordable, and user-friendly, rendering them an appealing option for pollinator monitoring.

ecology↗

bioRxiv2023

Low land-use intensity buffers grasslands against future climate and inter-annual climate variability in a large field experiment

Climate and land-use change are key drivers of global change. Full-factorial field experiments in which both drivers are manipulated are essential to understand and predict their potentially interactive effects on the structure and functioning of grassland ecosystems. Here, we present eight years of data on grassland dynamics from the Global Change Experimental Facility (GCEF) in Central Germany. On large experimental plots, temperature and seasonal patterns of precipitation are manipulated by superimposing regional climate model projections onto background climate variability. Climate manipulation is factorially crossed with agricultural land-use scenarios, including intensively used meadows and extensively used (i.e. low-intensity) meadows and pastures. Inter-annual variation of background climate during our study years was high, including three of the driest years on record for our region. The effects of this temporal variability far exceeded the effects of the experimentally imposed climate change on plant species diversity and productivity, especially in the intensively used, species-poor grasslands. These changes in productivity and diversity in response to alterations in climate were due to immigrant species replacing the target forage cultivars. This shift from forage cultivars to immigrant species may impose additional economic costs in terms of a decreasing fodder value and the need for more frequent management measures. In contrast, the extensively used, species-rich grasslands showed weaker responses to both experimentally manipulated future climate and inter-annual climate variability, suggesting that these diverse grasslands are more resistant to climate change than intensively used, species-poor grasslands. We therefore conclude that an extensive management of agricultural grasslands, together with other measures to increase species diversity, can stabilize primary productivity under climate change.

ecology↗

bioRxiv2023

A standard protocol to report discrete stage-structured demographic information

O_LIStage-based demographic methods, such as matrix population models (MPMs), are powerful tools used to address a broad range of fundamental questions in ecology, evolutionary biology, and conservation science. Accordingly, MPMs now exist for over 3,000 species worldwide. These data are being digitised as an ongoing process and periodically released into two large open-access online repositories: the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. During the last decade, data archiving and curation of COMPADRE and COMADRE, and subsequent comparative research, have revealed pronounced variation in how MPMs are parameterized and reported. C_LIO_LIHere, we summarise current issues related to the parameterisation and reporting of MPMs that arise most frequently and outline how they affect MPM construction, analysis, and interpretation. To quantify variation in how MPMs are reported, we present results from a survey identifying key aspects of MPMs that are frequently unreported in manuscripts. We then screen COMPADRE and COMADRE to quantify how often key pieces of information are omitted from manuscripts using MPMs. C_LIO_LIOver 80% of surveyed researchers (n=60) state a clear benefit to adopting more standardised methodologies for reporting MPMs. Furthermore, over 85% of the 300 MPMs assessed from COMPADRE and COMADRE omitted one or more elements that are key to their accurate interpretation. Based on these insights, we identify fundamental issues that can arise from MPM construction and communication and provide suggestions to improve clarity, reproducibility, and future research utilising MPMs and their required metadata. To fortify reproducibility and empower researchers to take full advantage of their demographic data, we introduce a standardized protocol to present MPMs in publications. This standard is linked to www.compadre-db.org, so that authors wishing to archive their MPMs can do so prior to submission of publications, following examples from other open-access repositories such as DRYAD, Figshare, and Zenodo. C_LIO_LICombining and standardising MPMs parameterized from populations around the globe and across the tree of life opens up powerful research opportunities in evolutionary biology, ecology, and conservation research. However, this potential can only be fully realised by adopting standardised methods to ensure reproducibility. C_LI

ecology↗

bioRxiv2022

Antecedent effect models as an exploratory rather than predictive tool to understand the response of plant populations to climate drivers

Understanding mechanisms and predicting natural population responses to climate is a key goal of Ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction). Thus, these models can serve as both a predictive and exploratory tool. We compare the predictive performance of antecedent effect models against simpler models, and showcase their exploratory analysis potential by selecting a case study with high predictive power. We fit three antecedent effect models: (1) weighted mean models (WMM), which weigh the importance of monthly anomalies based on a Gaussian curve, (2) stochastic antecedent models (SAM), which weigh the importance of monthly anomalies using a Dirichlet process, and (3) regularized regressions using the Finnish Horseshoe prior (FHM), which estimate a separate effect size for each monthly anomaly. We compare these approaches to a linear model using a yearly climatic predictor and a null model with no predictors. We use demographic data from 77 natural populations of 34 plant species ranging between seven and 36 years of length. We then fit models to the asymptotic population growth rate ({lambda}) and its underlying vital rates: survival, development, and reproduction. We find that models including climate do not consistently outperform null models. We hypothesize that the effect of yearly climate is too complex, weak, and confounded by other factors to be easily predicted using monthly precipitation and temperature data. On the other hand, in our case study, antecedent effect models show biologically sensible correlations between two precipitation anomalies and multiple vital rates. We conclude that, in temporal datasets with limited sample sizes, antecedent effect models are better suited as exploratory tools for hypothesis generation. Open Research statementData and code to reproduce the analyses are available on zenodo at https://dx.doi.org/10.5281/zenodo.7839199.

ecology↗

bioRxiv2020

Increasing temperature threatens an already endangered coastal dune plant

Climate change has the potential to reduce the abundance and distribution of species and threaten global biodiversity, but it is typically not listed as a threat in classifying species conservation status. This likely occurs because demonstrating climate change as a threat requires data-intensive demographic information. Moreover, the threat from climate change is often studied in specific biomes, such as polar or arid ones. Other biomes, such as coastal ones, have received little attention, despite being currently exposed to substantial climate change effects. We forecast the effect of climate change on the demography and population size of a federally endangered coastal dune plant (Lupinus tidestromii). We use data from a 14-year demographic study across seven extant populations of this endangered plant. Using model selection, we found that survival and fertility measures responded negatively to temperature anomalies. We then produced forecasts based on stochastic individual based population models that account for uncertainty in demographic outcomes. Despite large uncertainties, we predict that all populations will decline if temperatures increase by 1{degrees} Celsius. Considering the total number of individuals across all seven populations, the most likely outcome is a population decline of 90%. Moreover, we predict extinction is certain for one of our seven populations. These results demonstrate that climate change will profoundly decrease the current and future population growth rates of this plant, and its chance of persistence. Thus, our study provides the first evidence that climate change is an extinction threat for a plant species classified as endangered under the USA Endangered Species Act.

ecology↗

bioRxiv2020

Short-lived plants have stronger demographic responses to climate

To mitigate and adapt to climate change, there is an urgent need to synthesize the state of our knowledge on plant responses to climate. The availability of open-access data, combined with our understanding of plant physiology and life history theory provide opportunities to examine quantitative generalizations regarding which biomes and species are most responsive to climate drivers. Here, we synthesized time series of structured population models from 165 populations from 62 plants around the globe to link plant population growth rates to precipitation and temperature drivers. We expected: (1) more pronounced demographic responses to precipitation than temperature, especially in arid biomes; (2) a higher climate sensitivity in short-lived rather than long-lived species; and (3) a stronger response to climate by species that reproduce more frequently. We found that precipitation anomalies have a nearly three-fold larger effect on{lambda} than temperature. Precipitation has substantially more pronounced effects in more arid sites, but large noise makes this relationship non-significant. Species with shorter generation time have much stronger absolute responses to climate anomalies, while the degree of iteroparity does not correlate with population responses to climate. We conclude that key species-level traits can predict plant population responses to climate, and discuss the relevance of this generalization for conservation planning and evolutionary theory.

ecology↗

bioRxiv2020

Automated conservation assessment of the orchid family using deep learning

IUCN Red List assessments are essential for prioritizing conservation needs but are resource-intensive and therefore only available for a fraction of global species richness. Tropical plant species are particularly under-represented on the IUCN Red List. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. Here, we present automated conservation assessments for 13,910 species of the diverse and globally distributed Orchid family (Orchidaceae), based on a novel method using a deep neural network (IUC-NN), most of which (13,049) were previously unassessed by the IUCN Red List. We identified 4,342 (31.2 % of the evaluated orchid species) as Possibly Threatened with extinction (equivalent to the IUCN categories CR, EN, or VU) and point to Madagascar, East Africa, south-east Asia, and several oceanic islands as priority areas for orchid conservation. Furthermore, the Orchid family provides a model, to test the sensitivity of automated assessment methods to issues with data availability, data quality and geographic sampling bias. IUC-NN identified threat-ened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias compared to the IUCN Red List, and was robust against low data availability and geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in achieving goals of identifying the species that are at greatest risk of extinction.

ecology↗

bioRxiv2019

Global analysis reveals complex demographic responses of mammals to climate change

Approximately 25 % of mammals are threatened globally with extinction, a risk that is amplified under climate change1. Persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, on population dynamics2. Thus, to quantify which species and places on Earth are most vulnerable to climate-driven extinction, a global understanding of how demographic rates respond to climate is needed3. We synthesise information on such responses in terrestrial mammals, where extensive demographic data are available4. Given the importance of assessing the full spectrum of responses, we focus on studies that quantitatively link climate to multiple demographic rates. We identify 106 such studies, corresponding to 86 mammal species. We reveal a strong mismatch between the locations of demographic studies and the regions and taxa currently recognised as most vulnerable to climate change5,6. Moreover, we show that the effects of climate change on mammals will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others. Assessments of population viability under climate change therefore need to account for multiple demographic responses. We advocate to prioritise coordinated actions to assess mammal demography holistically for effective conservation worldwide.

ecology↗

bioRxiv2019

A multiscale framework for disentangling the roles of evenness, density and aggregation on diversity gradients

Disentangling the drivers of diversity gradients can be challenging. The Measurement of Biodiversity (MoB) framework decomposes changes in species diversity into three components of community structure: the species abundance distribution (SAD), the total community abundance, and the within-species spatial aggregation. Here we extend MoB from categorical treatment comparisons to quantify variation along continuous geographic or environmental gradients. Our approach requires sites along a gradient, each consisting of georeferenced plots of abundance-based species composition data. We demonstrate our method using a case study of ants sampled along an elevational gradient of 28 sites in mixed deciduous forest of the Great Smoky Mountains National Park, USA. MoB analysis revealed that ant species richness decreased along the elevational gradient because of changes in the SAD and in spatial aggregation, but not because of changes in the number of individuals. Specifically, with increasing elevation, species evenness was lower and species were less aggregated. These results do not support the more-individuals hypothesis; alternative hypotheses are required to explain why evenness and aggregation decrease with elevation. Our extension of MoB has the potential to elucidate the drivers of diversity along environmental gradients and should be useful for a variety of assemblage-level data collected along gradients.

ecology↗

bioRxiv2018

Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities

Because biodiversity is multidimensional and scale-dependent, it is challenging to estimate its change. However, it is unclear (1) how much scale-dependence matters for empirical studies, and (2) if it does matter, how exactly we should quantify biodiversity change. To address the first question, we analyzed studies with comparisons among multiple assemblages, and found that rarefaction curves frequently crossed, implying reversals in the ranking of species richness across spatial scales. Moreover, the most frequently measured aspect of diversity--species richness--was poorly correlated with other measures of diversity. Second, we collated studies that included spatial scale in their estimates of biodiversity change in response to ecological drivers and found frequent and strong scale-dependence, including nearly 10% of studies which showed that biodiversity changes switched directions across scales. Having established the complexity of empirical biodiversity comparisons, we describe a synthesis of methods based on rarefaction curves that allow more explicit analyses of spatial and sampling effects on biodiversity comparisons. We use a case study of nutrient additions in experimental ponds to illustrate how this multi-dimensional and multi-scale perspective informs the responses of biodiversity to ecological drivers.\n\nStatement of AuthorshipJC and BM conceived the study and the overall approach, and all authors participated in multiple working group meetings to develop and refine the approach. BM collected the data for the meta-analysis that led to Fig. 2,3; JC collected the data for the metaanalysis that led to Figure 4 and S1; SB and FM did the analyses for Figures 2-4; DM, FM and XX wrote the code for the analysis used for the recipe and case study in Figure 6. JC, BM and NG wrote first drafts of most sections, and all authors contributed substantially to revisions.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/275701_fig2.gif\" ALT=\"Figure 2\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@485725org.highwire.dtl.DTLVardef@151643corg.highwire.dtl.DTLVardef@8bc9e0org.highwire.dtl.DTLVardef@1729374_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Bivariate relationships between N, SPIE and S for 346 communities across the 37 datasets taken from McGill (2011b)(see Appendix 1). (A) S as a function of N; (B) S as a function of SPIE. (N vs SPIE not shown). Black lines depict the relationships across studies (and correspond to R2 fixed); colored points and lines show the relationships within studies. All axes are log-scale. Insets are histograms of the study-level slopes, with the solid line representing the slope across all studies. Gray bars indicate the study-level slope did not differ from zero, blue indicates a significant positive slope, and red indicates a significant negative slope.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC=\"FIGDIR/small/275701_fig3.gif\" ALT=\"Figure 3\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@12ee0e9org.highwire.dtl.DTLVardef@affc46org.highwire.dtl.DTLVardef@1db6453org.highwire.dtl.DTLVardef@97ab06_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO Representative rarefaction curves, the proportion of curves that crossed, and counts of how often curves crossed. (A) Rarefaction curves for different local communities within two datasets: marine invertebrates (nematodes) along a gradient from a waste plant outlet (Lambshead 1986), and trees in a Ugandan rainforest (Eggeling 1947); axes are log-transformed. (B) Counts of how many times pairs of rarefaction curves (from the same community) crossed; y-axis is on a log-scale.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC=\"FIGDIR/small/275701_fig4.gif\" ALT=\"Figure 4\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@15d497borg.highwire.dtl.DTLVardef@18354fforg.highwire.dtl.DTLVardef@1413a54org.highwire.dtl.DTLVardef@15c8451_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 4.C_FLOATNO Results of a meta-analysis of scale-dependent responses to a number of different ecological drivers (see Appendix 2). Points represent the log response ratio comparing species richness in control compared to treatments in a given comparison measured at the smallest (x-value) and largest (y-value) scale. The solid line indicates the 1: 1 line expected if effect sizes were not scale-dependent. Points above and below this line indicate effect sizes that are larger or smaller, respectively, as scale increases; points in the upper left and lower right quadrats represent cases where the direction of change shifted from positive to negative, or vice versa, with increasing scale. The dashed line indicates the best fit correlation, which is significantly different than the 1:1 line (P<0.01), indicating that overall, effect sizes tend to be larger at smaller scales than at larger scales. Colors for points indicate categorizations into different ecological drivers.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/275701_figS1.gif\" ALT=\"Figure 1\">\nView larger version (15K):\norg.highwire.dtl.DTLVardef@f30150org.highwire.dtl.DTLVardef@1db2db9org.highwire.dtl.DTLVardef@970507org.highwire.dtl.DTLVardef@cb37e6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure S1.C_FLOATNO Results showing the ratio of log-response ratio effect sizes from experiments where species richness responses were measured at two spatial scales (small scale/large scale). The dashed line at 0 would indicate studies where the effect sizes were the same at the smaller and larger scale. For each category of ecological driver, the means are above 1, indicating that the measured effect size is larger at the smaller relative to larger size, and this difference is statistically significant for land use, invasive species, and grazing/predation.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC=\"FIGDIR/small/275701_fig6.gif\" ALT=\"Figure 6\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@1c313d0org.highwire.dtl.DTLVardef@49df71org.highwire.dtl.DTLVardef@1ec92a3org.highwire.dtl.DTLVardef@8f1693_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO Effect of nutrient additions on several measurements of biodiversity from Table 1 (see data in Appendix 3). Each biodiversity measure was calculated at the -scale (1 mesocosm) (Panels A,B), {gamma}-scale (15 mesocosms) (Panels C,D), as well as the -scale (i.e. turnover across scales, {gamma}/)(Panels D,E,F). See Chase (2010) for details on the experimental design and the mobr package (McGlinn et al. submitted, https://github.com/MoBiodiv/mobr) for details on the statistical methods\n\nC_FIG\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC=\"FIGDIR/small/275701_fig1.gif\" ALT=\"Figure 1\">\nView larger version (15K):\norg.highwire.dtl.DTLVardef@133d65org.highwire.dtl.DTLVardef@123b3faorg.highwire.dtl.DTLVardef@fce7ccorg.highwire.dtl.DTLVardef@1d62dab_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO A. Individual-based rarefaction curves of three hypothetical communities (labelled A,B, C) where ranked differences between communities are consistent across scales. B. Individual-based rarefaction curves of three hypothetical communities (labelled A,B, C) where rankings between communities switch because of differences in the total numbers of species, and their relative abundances. Dotted vertical lines illustrate sampling scales where rankings switch. These curves were generated using the sim_sad function from the mobsim R package (May et al. 2018).\n\nC_FIG\n\n\n\nData accessibility statementAll data for meta-analyses and case study will be deposited in a publically available repository with DOI upon acceptance (available in link for submission).

ecology↗