Variability within the 10-year pollen rain of a seasonal neotropical forest

The effect of seasonal and multiannual environmental variability on the abundance and composition of Neotropical pollen rain was investigated using ten years of seasonal aerial pollen samples from Barro Colorado Island (BCI), Panama. Using canonical correspondence analysis, we identified the environmental variables that most account for intra-annual and inter-annual variability in tropical pollen production and found that pollen rain samples effectively captured the strong seasonality and stratification of pollen flow within the forest canopy. Inter-annual variation in pollen rain composition varied most strongly with the preceding year’s precipitation, with a smaller secondary effect of the current year’s temperature and photosynthetically active radiation (PAR). Our results demonstrate the relative stability of the long-term pollen rain as a larger regional signal of flowering response to climatic variability, while also suggesting that paleoecological interpretations of modern pollen assemblages need to adjust for skewed short-term variability in pollen influx from neighboring pollen taxa

Climatic, environmental, and phenological analyses of diverse lowland neotropical pollen rain data using ecoinformatic and machine learning tools

Analyses of modern pollen rain data from the Neotropics have traditionally been used to help interpret compositional changes observed in sedimentary pollen data. Comparisons of modern pollen assemblage data produced by different forest environments are compared to compositional changes observed within the fossil pollen record to improve interpretations of how plant communities have changed over time and under different climatic and environmental conditions. While modern pollen records provide an invaluable resource to improve paleoecological interpretations, most records are limited to short-term (1-3 year) sampling durations. These short-term sampling durations can potentially misrepresent comparisons of pollen and vegetation in different forest communities by not accounting for the full range of natural variability in phenological pollen outputs. For my dissertation, I counted and analyzed three ≥ 10-year pollen rain records obtained from two lowland Panamanian forests: Barro Colorado Island (BCI) and Parque Nacional San Lorenzo (PNSL). Together, these records represent the three longest continuous collections of airborne pollen data analyzed from the Neotropics to date. This dissertation explores novel approaches to the analysis of hyperdiverse Neotropical pollen rain assemblage data. A machine-based ecoinformatic analysis was used to correlate seasonal and annual variability in pollen abundance data to a suite of climatic variables. The analysis explores how climatic variability influences the composition of pollen assemblage data in forest sites characterized by differences in seasonality. A 15-year pollen rain collected within a forest dynamics plot located on BCI was used to analyze relationships between pollen abundances, biomass, and flowering patterns. The extended pollen rain highlights the extent to which the relationship between pollen abundance data and standing biomass can vary on a year- to-year basis and the potential for aerial pollen trapping data to supplement the study of tropical flowering patterns. Using pollen identifications from the BCI plot pollen rain, a machine learning method using convolutional neural nets was developed to fully automate the process of pollen identification

Accuracy and consistency of grass pollen identification by human analysts using electron micrographs of surface ornamentation

• Premise of the study: Humans frequently identify pollen grains at a taxonomic rank above species. Grass pollen is a classic case of this situation, which has led to the development of computational methods for identifying grass pollen species. This paper aims to provide context for these computational methods by quantifying the accuracy and consistency of human identification. • Methods: We measured the ability of nine human analysts to identify 12 species of grass pollen using scanning electron microscopy images. These are the same images that were used in computational identifications. We have measured the coverage, accuracy, and consistency of each analyst, and investigated their ability to recognize duplicate images. • Results: Coverage ranged from 87.5% to 100%. Mean identification accuracy ranged from 46.67% to 87.5%. The identification consistency of each analyst ranged from 32.5% to 87.5%, and each of the nine analysts produced considerably different identification schemes. The proportion of duplicate image pairs that were missed ranged from 6.25% to 58.33%. • Discussion: The identification errors made by each analyst, which result in a decline in accuracy and consistency, are likely related to psychological factors such as the limited capacity of human memory, fatigue and boredom, recency effects, and positivity bias

Climatic, environmental, and phenological analyses of diverse lowland neotropical pollen rain data using ecoinformatic and machine learning tools

Analyses of modern pollen rain data from the Neotropics have traditionally been used to help interpret compositional changes observed in sedimentary pollen data. Comparisons of modern pollen assemblage data produced by different forest environments are compared to compositional changes observed within the fossil pollen record to improve interpretations of how plant communities have changed over time and under different climatic and environmental conditions. While modern pollen records provide an invaluable resource to improve paleoecological interpretations, most records are limited to short-term (1-3 year) sampling durations. These short-term sampling durations can potentially misrepresent comparisons of pollen and vegetation in different forest communities by not accounting for the full range of natural variability in phenological pollen outputs. For my dissertation, I counted and analyzed three ≥ 10-year pollen rain records obtained from two lowland Panamanian forests: Barro Colorado Island (BCI) and Parque Nacional San Lorenzo (PNSL). Together, these records represent the three longest continuous collections of airborne pollen data analyzed from the Neotropics to date. This dissertation explores novel approaches to the analysis of hyperdiverse Neotropical pollen rain assemblage data. A machine-based ecoinformatic analysis was used to correlate seasonal and annual variability in pollen abundance data to a suite of climatic variables. The analysis explores how climatic variability influences the composition of pollen assemblage data in forest sites characterized by differences in seasonality. A 15-year pollen rain collected within a forest dynamics plot located on BCI was used to analyze relationships between pollen abundances, biomass, and flowering patterns. The extended pollen rain highlights the extent to which the relationship between pollen abundance data and standing biomass can vary on a year- to-year basis and the potential for aerial pollen trapping data to supplement the study of tropical flowering patterns. Using pollen identifications from the BCI plot pollen rain, a machine learning method using convolutional neural nets was developed to fully automate the process of pollen identification.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Variability within the 10-Year Pollen Rain of a Seasonal Neotropical Forest and Its Implications for Paleoenvironmental and Phenological Research

<div><p>Tropical paleoecologists use a combination of mud-water interface and modern pollen rain samples (local samples of airborne pollen) to interpret compositional changes within fossil pollen records. Taxonomic similarities between the composition of modern assemblages and fossil samples are the basis of reconstructing paleoclimates and paleoenvironments. Surface sediment samples reflect a time-averaged accumulation of pollen spanning several years or more. Due to experimental constraints, modern pollen rain samples are generally collected over shorter timeframes (1–3 years) and are therefore less likely to capture the full range of natural variability in pollen rain composition and abundance. This potentially biases paleoenvironmental interpretations based on modern pollen rain transfer functions. To determine the degree to which short-term environmental change affects the composition of the aerial pollen flux of Neotropical forests, we sampled ten years of the seasonal pollen rain from Barro Colorado Island, Panama and compared it to climatic and environmental data over the same ten-year span. We establish that the pollen rain effectively captured the strong seasonality and stratification of pollen flow within the forest canopy and that individual taxa had variable sensitivity to seasonal and annual changes in environmental conditions, manifested as changes in pollen productivity. We conclude that modern pollen rain samples capture the reproductive response of moist tropical plants to short-term environmental change, but that consequently, pollen rain-based calibrations need to include longer sampling periods (≥7 years) to reflect the full range of natural variability in the pollen output of a forest and simulate the time-averaging present in sediment samples. Our results also demonstrate that over the long-term, pollen traps placed in the forest understory are representative samples of the pollen output of both canopy and understory vegetation. Aerial pollen traps, therefore, also represent an underutilized means of monitoring the pollen productivity and reproductive behavior of moist tropical forests.</p> </div

Canonical correspondence analysis of seasonal pollen influx and seasonal climatic conditions.

<p>CCA plot of the 111 seasonal pollen assemblages (restricted to the 50 most abundant pollen taxa) and nine environmental variables. The loadings of the nine environmental variables for the first two axes are plotted as vectors (top). Sample loadings for the first two axes are below, with dry season samples depicted as open boxes, wet season samples as filled boxes, and the 1998 samples as filled inverse triangles. Dry and wet season samples ordinate to the right and left, respectively.</p

Seasonal pollen influx.

<p>Pollen diagram illustrating the compositional variability of the seasonal pollen influx, by sampling height. Pollen taxa exhibiting the most intra-annual variability in abundance over the 10-year pollen rain are graphed as their relative percentage of the total pollen influx for each season from 1996–2005. Influx data from the 1998 pollen samples are excluded because the pollen traps were not collected seasonally.</p

Representation of pollen taxa with parent plants in the surrounding community.

<p>Calculations of basal area (%BA), R-rel, and p/v for taxa found in both the pollen samples and the vegetation census. Relative pollen influx provided by “% Pollen”, based on the relative representation of each taxon in the full ten-year pollen record. Relative abundance and basal area of individual taxa within 50 m given by “% Stems”, based on the May 2011 census (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053485#pone.0053485.s003" target="_blank">Table S2</a>). R-rel and p/v values were calculated annually and are shown as averages and one standard deviation (σ).</p

Canonical correspondence analysis of annual non-local pollen influx and current and previous years’ climatic conditions.

<p>Environmental and pollen taxon loadings for the first two ordination axes from the CCA of annual non-local pollen influx. Loadings for axis one (top left, bottom left) and loadings for axis two (top right, bottom right). Environmental loadings (top left, top right) and taxon loadings (bottom left, bottom right) are displayed as horizontal bars; the longer the bar, the stronger the correspondence of the variables for each axis. Environmental variables are denoted by the following abbreviations: SH (sampling height), MnP (mean precipitation), MnET (mean evapotranspiration), DD (proportion dry days), DT (diurnal temperature), MaxT (max temperature), MnT (mean daily temperature), MinT (minimum temperature), and MnPAR (mean photosynthetically active radiation). Light bars represent the current years’ climatic conditions; dark bars represent the previous years’ conditions. The first axis is characterized most strongly by variability in sampling height and the previous year’s precipitation. The second axis is characterized by covariance with measures of the current sampling year’s mean ET, PAR and temperature variables. CCA loadings in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053485#pone.0053485.s006" target="_blank">Table S5</a>.</p

Correspondence analysis of time-averaged pollen influx by sampling height.

<p>Results of the CA for pollen samples aggregated over one, three, five, seven, and ten years, for each sampling height. Sampling heights are color-coded as follows: light blue (0 m), dark blue (5 m), light green (20 m), dark green (25 m), light red (40 m), and dark red (42/45 m). Sampling length is represented as crosses (1 year), circles (3 years), diamonds (5 years), triangles (7 years), and squares (10 years). The eigenvalues of axes one and two were 0.134 and 0.086 respectively. The largest sample-to-sample variation occurs at shorter sampling lengths and lower sampling heights.</p