Biology subjects

Lake, T. A.

Publications and source records attributed to Lake, T. A..

3 recordsLinked to original sources

bioRxiv2025

Best practices and challenges for urban tree detection, classification, and geolocation with street-level images across North American cities

Accurate, up-to-date catalogs of urban tree populations are crucial for quantifying ecosystem services and enhancing the quality of life in cities. However, mapping tree species cost-effectively remains challenging. In response, remote sensing researchers are developing general-purpose tools to survey plant populations across broad spatial scales. In this study, we developed computer vision models to detect, classify, and map 100 tree genera across 23 cities in North America using Google Street View (GSV) and iNaturalist images. We validated our predictions in independent portions of each city. We then compared our predictions to existing street tree records to evaluate the spatial context of errors using generalized linear mixed-effects models. Our computer vision models identified most ground-truthed street trees (67.1%). Performance varied across the 23 cities (67.4% {+/-} 9.3%) and 100 genera (50.9% {+/-} 23.0%) and improved denser street-view coverage, simpler stand structure, and greater training representation, particularly from the focal city. We found that genus classification performed better in continental cities with lower relative diversity, and that seasonal changes in the appearance of trees provided visual cues that moderate classification rates. Using widely available street-level imagery is a generalizable and promising avenue for mapping tree distributions across urban environments.

ecology↗

bioRxiv2025

Two decades of satellite images reveal the spatial and temporal dynamics of leafy spurge invasion and improve species distribution models

Developing accurate and cost-effective methods to detect and predict the spread of invasive species remains an ongoing challenge. Species distribution models (SDMs) are used to predict invasion under current and future climates. However, occurrence datasets are often spatially and temporally biased because they are collected in an unstructured and opportunistic manner. Remote sensing individual species over broad spatial and temporal scales has become increasingly feasible with the accumulation of satellite images and the development of convolutional neural networks. In this study, we used a 21-year archival Landsat satellite imagery (2000-2020) to train a temporal convolutional neural network model to predict the probability of occurrence of the invasive species, leafy spurge (Euphorbia virgata), across Minnesota. We validated our predictions with an independent dataset. First, we show that that leafy spurge has expanded from 1,067 km{superscript 2} occupied in 2000-2002 to 7,156 km{superscript 2} in 2018-2020, a 570% increase. Surprisingly, drought severity modulated the predicted area invaded and was associated with fluctuations over the study period. Second, we tracked changes in probability over time for individual pixels and showed that invasion has been concentrated in two largely disjunct regions of Minnesota. Third, our remotely-sensed occurrence dataset and community science dataset were biased to roadsides, although the latter was more severely biased. Last, we showed that SDMs built using remotely-sensed occurrences had higher discrimination, were less overfit, and had higher performance outside of urban areas. Overall, twenty one years of archival satellite imagery provided valuable insight into the spatial and temporal population dynamics of leafy spurge invasion and improved forecasts of future invasion by reducing spatial bias.

ecology↗

bioRxiv2023

Chronosequence of invasion reveals minimal losses of population genomic diversity, niche expansion, and trait divergence in the polyploid, leafy spurge.

Rapid evolution may play an important role in the range expansion of invasive species and modify forecasts of invasion, which are the backbone of land management strategies. However, losses of genetic variation associated with colonization bottlenecks may constrain trait and niche divergence at leading range edges, thereby impacting management decisions that anticipate future range expansion. The spatial and temporal scales over which adaptation contributes to invasion dynamics remains unresolved. We leveraged detailed records of the [~]130 year invasion history of the invasive polyploid plant, leafy spurge (Euphorbia virgata), across [~]500km in Minnesota, U.S.A. We examined the consequences of range expansion for population genomic diversity, niche breadth, and the evolution of germination behavior. Using genotyping-by-sequencing, we found some population structure in the range core, where introduction occurred, but panmixia among all other populations. Range expansion was accompanied by only modest losses in sequence diversity, with small, isolated populations at the leading edge harboring similar levels of diversity to those in the range core. The climatic niche expanded during most of range expansion, and the niche of the range core was largely non-overlapping with the invasion front. Ecological niche models indicated that mean temperature of the warmest quarter was the strongest determinant of habitat suitability and that populations at the leading edge had the lowest habitat suitability. Guided by these findings, we tested for rapid evolution in germination behavior over the time course of range expansion using a common garden experiment and temperature manipulations. Germination behavior diverged from early to late phases of the invasion, with populations from later phases having higher dormancy at lower temperatures. Our results suggest that trait evolution may have contributed to niche expansion during invasion and that distribution models, which inform future management planning, may underestimate invasion potential without accounting for evolution.

evolutionary biology↗