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Abstract Purpose of ReviewArtificial intelligence (AI) is disrupting science and discovery across disciplines, offering new modes of inquiry that are changing how questions are asked and answered and upsetting established norms. In this paper, we review the state of the art of AI in landscape ecology and offer six areas of opportunity for landscape ecologists to capitalize on AI tools moving forward. These areas include geospatial AI (GeoAI), geometric AI, Explainable AI (xAI), generative AI (GenAI), Natural Language Processing (NLP), and robotics. Recent FindingsLandscape ecology has a long history of using AI, notably machine learning methods for image classification tasks, agent-based modeling, and species distribution modeling but also knowledge representation and automated reasoning for landscape generation and spatial planning. Methods have become more diverse and complex in recent years, with a new generation of AI-based tools rapidly emerging. These new tools have potential to improve how landscape ecologists map, measure, and model landscape patterns and processes as well as improve the explainability of model outputs. SummaryThere are many untapped opportunities for landscape ecologists to leverage emerging AI-based tools in research and practice including generating virtual landscapes for simulating processes such as wildfires and leveraging natural language processing to generate new insights from text data. Regardless of the application, researchers using AI tools must also consider the ethical implications of data and algorithmic biases and critically assess how these methods can be used responsibly. 

Abstract Sustaining biodiversity requires measuring the interacting spatial and temporal processes by which environmental factors shape wildlife community assembly. Declines in bird communities due to urban development and changing climate conditions are widely documented. However, the combined impacts of multiple environmental stressors on biodiversity remain unclear, especially in urbanized desert ecosystems. This is largely due to a lack of data at the scales necessary for predicting the consequences of environmental change for diverse species and functional groups, particularly those that provide ecosystem services such as seed dispersal, pest control, and pollination. Trends in the prevalence and diversity of different functional groups contribute to understanding how changes in bird communities impact well‐being through the lens of ecosystem services. Across the rapidly developing drylands of the metropolitan Phoenix, Arizona, USA, we ask the following question: How have inter‐ and intra‐annual landscape changes associated with urbanization and climate shaped the dynamic characteristics of bird communities, specifically the abundance and richness of species and their functional groups? We analyzed long‐term drivers of bird communities by combining a two‐decade, multi‐season spatial dataset of environmental conditions (urbanization, vegetation, temperature, etc.) with biotic data (species richness and abundance) collected seasonally during the same time periods (winter and spring; 2001–2016). Results show that increased impervious surface area and land surface temperature were negatively associated with overall bird abundance and species richness across the study period, especially during winter. However, these relationships varied among functional groups, with potentially mixed outcomes for ecosystem services and disservices provided by urban biodiversity. By improving knowledge of long‐term trends in multiple environmental drivers that shape wildlife community dynamics, these results facilitate effective evaluation of how landscape management practices in drylands influence the outcomes of evolving human‐wildlife relationships. 

Abstract Field-based research in the biological sciences encounters several challenges, including cost, accessibility, safety, and spatial coverage. Drones have emerged as a transformative technology to address these challenges while providing a less intrusive alternative to field surveys. Although drones have mainly been used for high-resolution image collection, their capabilities extend beyond mapping and image production. They can be tailored to track wildlife, measure environmental parameters, and collect physical samples, and their versatility enables researchers to tackle a variety of biodiversity and conservation challenges. In this article, we advocate for drones to be integrated more comprehensively into field-based research, from site reconnaissance to sampling, interventions, and monitoring. We discuss the future innovations needed to harness their full potential, including customized instrumentation, fit-for-purpose software and apps, and better integration with existing online databases. We also support leveraging community scientists and empowering citizens to contribute to scientific endeavors while promoting environmental stewardship via drones. 

Abstract Droughts are a natural hazard of growing concern as they are projected to increase in frequency and severity for many regions of the world. The identification of droughts and their future characteristics is essential to building an understanding of the geography and magnitude of potential drought change trajectories, which in turn is critical information to manage drought resilience across multiple sectors and disciplines. Adding to this effort, we developed a dataset of global historical and projected future drought indices over the 1980–2100 period based on downscaled CMIP6 models across multiple shared socioeconomic pathways (SSP). The dataset is composed of two indices: the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for 23 downscaled global climate models (GCMs) (0.25-degree resolution), including historical (1980–2014) and future projections (2015–2100) under four climate scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The drought indices were calculated for 3-, 6- and 12-month accumulation timescales and are available as gridded spatial datasets in a regular latitude-longitude format at monthly time resolution. 

Abstract BackgroundThe southeastern United States consists of diverse ecosystems, many of which are fire-dependent. Fires were common during pre-European times, and many were anthropogenic in origin. Understanding how prescribed burning practices in use today compare to historic fire regimes can provide perspective and context on the role of fire in critical ecosystems. On the Aransas National Wildlife Refuge (ANWR), prescribed burning is conducted to prevent live oak (Quercus fusiformis) encroachment and preserve the openness of the herbaceous wetlands and grasslands for endangered whooping cranes (Grus americana) and Aplomado falcons (Falco femoralis). This field note builds a digital fire atlas of recent prescribed burning on the refuge and compares it to the historical fire ecology of ANWR. ResultsFindings indicate that the refuge is maintaining fire-dependent ecosystems with an extensive burn program that includes a fire return interval between 2 and 10 years on a majority of the refuge, with some locations experiencing much longer intervals. These fire return intervals are much shorter than the historic burn regime according to LANDFIRE. ConclusionsFollowing the fire return intervals projected by LANDFIRE, which project longer intervals than the prescribed fire program, would likely be detrimental to endangered species management by allowing increased woody plant encroachment and loss of open habitat important to whooping cranes and Aplomado falcons. Since prescribed fire is part of the management objectives on many national wildlife refuges in the United States, quantifying recent and historical fire ecology can provide useful insights into future management efforts, particularly in cases where endangered species are of special concern and management efforts may be counter to historical disturbance regimes. 

Abstract Drones have emerged as a cost‐effective solution to detect and map plant invasions, offering researchers and land managers flexibility in flight design, sensors and data collection schedules. A systematic review of trends in drone‐based image collection, data processing and analytical approaches is needed to advance the science of invasive species monitoring and management and improve scalability and replicability.We systematically reviewed studies using drones for plant invasion research to identify knowledge gaps, best practices and a path toward advancing the science of invasive plant monitoring and management. We devised a database of 33 standardized reporting parameters, coded each study to those parameters, calculated descriptive statistics and synthesized how these technologies are being implemented and used.Trends show a general increase in studies since 2009 with a bias toward temperate regions in North America and Europe. Most studies have focused on testing the validity of a machine learning or deep learning image classification technique with fewer studies focused on monitoring or modelling spread. Very few studies used drones for assessing ecosystem dynamics and impacts such as determining environmental drivers or tracking re‐emergence after disturbance. Overall, we noted a lack of standardized reporting on field survey design, flight design, drone systems, image processing and analyses, which hinders replicability and scalability of approaches. Based on these findings, we develop a standard framework for drone applications in invasive species monitoring to foster cross‐study comparability and reproducibility.We suggest several areas for advancing the use of drones in invasive plant studies including (1) utilizing standardized reporting frameworks to facilitate scientific research practices, (2) integrating drone data with satellite imagery to scale up relationships over larger areas, (3) using drones as an alternative to in‐person ground surveys and (4) leveraging drones to assess community trait shifts tied to plant fitness and reproduction. 

Connectivity is crucial for species conservation, but most assessments define connectivity solely in terms of protected or natural areas and land covers without regard for the underlying thermal environment. As climate change accelerates, it is becoming increasingly important to not only assess land use and land cover changes (LULCC) but also how surface temperatures are evolving and creating more fragmented thermal refuges over time. This research investigates how the surface thermal environment has changed over time in Phoenix, Arizona, USA, a desert city in the southwestern United States, and how the spatial patterns of cooler refuges within the heat landscape, or “heatscape,” may be affecting wildlife habitat availability alongside LULCC. We quantify the structural and functional connectivity of thermal refuges using a suite of connectivity metrics from landscape ecology to demonstrate how the spatial distribution and configuration of these critical areas has changed over the last 35 years and what the implications are for the many wildlife species living in this desert environment. Results show that thermal refuge patches have been shrinking and becoming more fragmented over the past 35 years, with connectivity also declining over the same period. A key inflection point was identified in 2000, when the probability that cooler refuges patches were connected dropped to nearly zero, and it has remained at that low level ever since. These shifts in connectivity are tightly coupled with LULCC in the study area, particularly the loss of irrigated agriculture as it has been replaced by residential and other developed land uses over time. Decreasing water security in the region also threatens to reduce the availability of cooler patches and, simultaneously, the connectivity of those refuges. Introducing cooler patches through engineered materials or artificial shade may help offset some of the losses from irrigated lands. The findings offer a perspective for conservation research with implications for advancing a more formal thermal landscape ecology for understanding and improving the relationship between spatial thermal patterns and ecological processes.