The review began from the difficulty of explaining where jaguars occur in a landscape while accounting for the species’ large home ranges, low population densities, elusive behavior and exposure to an array of human pressures. Jaguars, described in the reviewed literature as the largest felid on the American continent, have been extirpated from parts of their historic range and are classified as Near Threatened. The study focused on peer-reviewed research that either addressed jaguar distribution or correlated it with environmental or anthropogenic factors.

Searches of Web of Science and Google Scholar used the terms “Jaguar,” “Distribution,” “Environmental variables,” “Prey abundance,” “Panthera” and “Occurrence.” For each relevant publication, the authors recorded the data-collection method, analytical approach, geographic scale and predictor variables. Of the 165 peer-reviewed documents retrieved, 95 met the review’s criteria. Almost 98% of this evidence base was published after 2000, and the countries with the largest numbers of included studies were Brazil, with 28 studies; Mexico, with 16; and Belize, with 10.

Researchers used seven categories of occurrence data: telemetry in 16 studies, camera traps in 38, genetics in three, historic records in 21, sign counts in two, interviews in three and geographic information system data in 12. Four analytical categories were distinguished. Basic empirical statistical models accounted for 41 studies, niche or presence-only models for 22, occupancy models for 20 and deductive approaches for 12.

Most studies were local: 62 of the 95 examined a particular landscape or study area, compared with 13 continental, 12 regional and eight country-scale assessments. The review classified 21 anthropogenic variables into four groups: roads, land use, human activities and human population. Environmental variables comprised 18 measures under climate, vegetation, wildlife, topography, water and other categories.

Significance was defined as a variable being reported as having a statistically significant influence on occurrence or distribution in the reviewed study. This approach did not standardize effect sizes, model specifications or thresholds across papers, so the frequency of a significant result cannot be interpreted as a comparable effect magnitude. Road variables appeared in 29 studies, but only eight reported a significant relationship, and distance to roads was the only road metric with more than one significant result.

It was significant in seven of 27 studies that included it; road density and distance to railroads were not significant in the papers that assessed them. Land use was examined in 32 studies. Thirteen reported at least one significant land-use association, and land-cover type was significant in 41% of the 29 papers using it.

Distance to forest appeared in one study, where it was significant, while distance to agriculture appeared in two and was not significant. Human activities were more broadly represented, with 47 papers assessing at least one of 11 metrics. Among the human-activity measures used in at least two models, reported significance ranged from 50% to 67%.

Protection status was significant in five of nine studies, and distance to protected areas was also significant in five of nine, although the latter result does not indicate the direction of the association in the review’s synthesis. Human-population variables were included in 43 studies. Population density was significant in 44% of the 16 papers using it, whereas distance to settlements was significant in 21% of 24 papers.

These mixed findings do not support treating settlement proximity as a uniform barrier to occurrence. They instead show that the measured relationship varied among study areas and modeling approaches. Reviews of the literature describe jaguars as landscape animals whose ranges can cross protected areas and multiple ecosystems, and the analysis notes that a formally protected boundary may not capture enforcement intensity.

Hunting pressure was significant in four of six studies that included it, but the review identifies its measurement and mapping as a continuing difficulty. Environmental predictors also produced variable results. Climate metrics—seasonality, precipitation and temperature—were assessed in 42 studies, of which 14 reported at least one significant climate relationship.

Precipitation was significant in six of 16 studies, temperature in four of 13 and seasonality in four of 13. Vegetation was evaluated more frequently, with 70 studies assessing six related variables. Thirty-seven reported at least one significant association, and vegetation type was significant in 33 papers.

Because vegetation type can simultaneously describe cover, prey habitat and the physical structure of hunting environments, the review treats it as a broad habitat descriptor rather than evidence for one exclusive mechanism. Connectivity was significant in none of the three papers that included it, normalized difference vegetation index in one of four, and tree richness was not significant in the single study that assessed it. Wildlife variables produced the strongest family-level statistical association.

Fifty-four studies assessed prey occurrence or abundance, conspecific occurrence or abundance, or both, and 39 reported a significant influence on jaguar distribution. Prey occurrence or abundance was significant in 23 of 27 studies, or 85%, while the occurrence or abundance of other jaguars was significant in 16 of 27, or 59%. Camera traps offer a practical reason for the availability of these measures: the same surveys can document jaguars, prey and other predators, making their occurrence and abundance comparatively accessible to collect.

Topographic and water variables were less consistent. Forty-four studies examined elevation or slope, and 14 reported a significant relationship. Elevation was significant in 13 of 30 studies, whereas slope was significant in only one of 14.

Elevation can also vary in concert with the distribution of people, protected areas and vegetation, so the review notes that it may represent other conditions rather than a direct influence. Distance to water appeared in 38 studies and was significant in nine, or 24%; runoff was significant in one of three studies. The authors note that water was more often related to occurrence in seasonal ecosystems, but the review does not establish fresh water as a general local-scale driver of jaguar density.

Soil type and geology were not significant in the limited studies that tested them, while distance to the beach was significant in one study where seasonal turtle nesting created a concentrated prey opportunity. Across geographic scales, the review identified 12 variables that were repeatedly reported as important in its synthesis. These were vegetation type, prey occurrence or abundance, conspecific occurrence or abundance, elevation, land-cover type, distance to water, distance to roads, population density, precipitation, distance to protected areas, level of protection and distance to settlements.

At regional and continental scales, the leading set included vegetation type, land cover, distance to roads, population density, precipitation, protection level and prey availability. Local studies more often emphasized prey, conspecifics, elevation, distance to water, distance to protected areas, distance to settlements and finer-scale habitat variables. These differences in variable use underscore the effect of geographic scale, survey methods and data resolution, but they do not prove that a predictor’s influence transfers unchanged between scales.

The central limitation is that occurrence data do not directly establish density, demographic stability or population trends. Even camera-trap photo rates may not correlate well with jaguar density, and studies with small samples may lack the power to detect sex-, age- or behavior-specific influences. The review also notes that available datasets and mapping constraints can determine which predictors enter a model, while coarse geographic layers may not match the ecological processes operating at a local site.

Rather than adding every measurable layer, the authors call for models that identify and quantify specific causal factors affecting jaguar reproduction, survival and dispersal. Connecting habitat use to demographic rates remains difficult and costly, but the review argues that those links are necessary to move beyond mapping where jaguars occur and toward understanding the conditions under which viable populations persist.

Subject of Research: Environmental and anthropogenic predictors used in jaguar occurrence models

Article Title: A review of environmental and anthropogenic variables used to model jaguar occurrence

Article References: Montalvo, V. H., Sáenz-Bolaños, C., Carrillo, E., & Fuller, T. K. (2023). A review of environmental and anthropogenic variables used to model jaguar occurrence. Neotropical Biology and Conservation, 18(1), 31-51. https://doi.org/10.3897/neotropical.18.e98437

Image Credits: AI Generated

DOI: 10.3897/neotropical.18.e98437

Keywords: jaguar occurrence, Panthera onca, species distribution models, habitat modeling, prey abundance, camera trapping, human pressures, land cover, conservation, landscape ecology, review, environmental

The central challenge TreeMap addresses is a familiar one in the geosciences: the mismatch between what we can measure and what we need to know. The Forest Inventory and Analysis program, or FIA, maintains one of the most rigorous forest monitoring systems in the world, with field crews measuring trees on a systematic grid of plots across the country. Those plots are extraordinarily informative, but they are also sparse, typically covering only a tiny fraction of the landscape. Anyone who wants to know the biomass, species composition, or size structure of a forest on a specific hillside, a proposed fuel treatment area, or a fire perimeter must somehow bridge the gap between scattered field measurements and continuous spatial coverage.

TreeMap bridges that gap through a statistical technique called imputation. Using a random forests approach, the model assigns to every forested pixel in the gridded LANDFIRE data the single FIA plot that most closely resembles it, based on a suite of predictor variables describing vegetation, topography, biophysical conditions, and disturbance history. Because each pixel inherits the full tree-level detail of its matched FIA plot, including species identities, diameters, heights, and counts of individual trees, the resulting national map carries a granularity that no direct remote sensing product can currently provide. A pixel is no longer just a green value in a satellite image; it is a stand-in for a real, measured plot of forest with all its ecological texture intact.

The new releases bring three significant methodological upgrades. The first concerns climate. Earlier versions of TreeMap relied on the biophysical variables embedded in LANDFIRE, the national landscape fire and resource management mapping program, to characterize the growing conditions at each pixel. The 2020, 2022, and 2023 versions instead incorporate climatic variables drawn from Daymet, a well-established daily surface weather and climatology dataset. This substitution gives the imputation model a more direct and dynamically updated picture of the temperature and precipitation regimes that shape which trees can grow where, an improvement that matters as climate shifts alter the suitability of landscapes for particular species.

The second upgrade involves validation, the process by which the researchers test how well the imputed maps reproduce reality. Independent validation is notoriously difficult in imputation studies because the model is built from FIA plots, so testing it against the same plots risks circularity. In the new versions, the team refined their independent validation by restricting the analysis to FIA plots where any heterogeneity was administrative rather than ecological, and by excluding plots that had previously been used to validate earlier TreeMap versions spanning heterogeneous forest conditions. The result is a cleaner, more honest assessment of how well the maps perform in forests the model has not effectively already seen.

The third and perhaps most consequential improvement targets species range mapping. In earlier versions, the random forests imputer could, in principle, assign to a pixel an FIA plot dominated by a species that has no plausible business occurring there, a known artifact of purely statistical matching. The new releases implement a constraint on the pool of FIA plots available for imputation within each LANDFIRE zone: only plots documenting species recorded within that zone or its adjacent zones are eligible for matching. This biogeographic sanity check dramatically reduces implausible species assignments across the country, ensuring that a map user in, say, the shortgrass prairie margin does not encounter a phantom stand of boreal conifers.

Why does tree-level detail matter so much? The answer lies in the way forests interact with the Earth system. Carbon cycling models, for instance, need to know not just how much wood a landscape holds but how it is distributed among species and size classes, because decay rates, growth trajectories, and disturbance vulnerability all vary with those attributes. Fire behavior models, which the Missoula team knows intimately, depend on fuel characteristics that emerge from the arrangement of individual trees, snags, and understory vegetation. Habitat distribution models for species ranging from woodpeckers to butterflies hinge on fine-grained structural features, such as canopy closure and the presence of large-diameter trees, that only tree-level data can supply.

The temporal dimension of the new releases adds further value. By producing versions for 2020, 2022, and 2023, the team has created a short time series that captures the rapid changes sweeping through American forests, from insect outbreaks and drought-induced mortality to timber harvests and increasingly frequent wildfires. Researchers can now examine how forest composition and structure are evolving on an annual to biennial cadence at national scale, a capability that becomes ever more critical as disturbance regimes intensify under a warming climate. Each version is publicly available through the Forest Service Research Data Archive, and the model code has been released on GitHub, reflecting a broader push toward reproducibility and open science in federal research.

The implications extend well beyond the research community. Land managers weighing fuel treatments can use TreeMap to estimate how a prescription would alter stand structure across thousands of hectares without visiting every acre. Carbon accounting efforts, including those tied to emerging markets and national greenhouse gas reporting, gain a higher-resolution foundation for estimating stocks and fluxes. Conservation planners can map habitat features at a scale that matches the movements of the organisms they are trying to protect. And because the datasets are freely available, they lower the barrier for state agencies, tribes, universities, and nonprofits to conduct analyses that once required substantial computational and statistical infrastructure.

There are, of course, limits to what imputation can achieve. TreeMap is a model, not a census, and its accuracy depends on the quality of the underlying FIA measurements, the LANDFIRE grids, and the predictor variables that link them. The team’s careful refinements to validation and species constraints acknowledge these limits and push the framework toward greater realism. As the datasets move through peer review and into the hands of the modeling community, they are likely to become a standard reference point for anyone asking what America’s forests look like, tree by tree, in a rapidly changing century. In an era when forest questions have become climate questions, that kind of clarity is not a luxury; it is a necessity.

Subject of Research: Tree-level imputed forest structure and composition mapping across the conterminous United States

Article Title: TreeMap 2020, 2022, and 2023: Improved species range mapping in a tree-level forest dataset for the conterminous United States

Article References: Riley, K., Houtman, R., Zimmer, S., Leatherman, L., Peeler, J., Shaw, J., Grenfell, I., Borja Arboleda, M., Shrestha, A., Housman, I., & Finney, M. (2026). TreeMap 2020, 2022, and 2023: Improved species range mapping in a tree-level forest dataset for the conterminous United States. https://doi.org/10.5194/essd-2026-633

Image Credits: AI Generated

DOI: 10.5194/essd-2026-633

Keywords: TreeMap, forest mapping, FIA, random forests imputation, LANDFIRE, Daymet, species range, wildfire behavior, carbon cycling, habitat modeling, remote sensing, US Forest Service

The research team, led by Bianca S. Santos of Stanford University’s Doerr School of Sustainability together with Larry B. Crowder and Dana K. Briscoe, drew on satellite telemetry from 53 juvenile loggerheads tracked between 2023 and 2025. These animals are part of a broader, decades-long effort known as Project STRETCH, which has involved raising and releasing loggerheads hatched from Japanese nesting beaches and tracking their trans-Pacific journeys. The newly published analysis adds a crucial layer to that legacy: a quantitative description of the ocean conditions the turtles select, and a jurisdictional accounting of where suitable habitat falls under national or international authority.

The technical core of the study rests on two satellite-derived oceanographic variables: sea surface temperature, or SST, and chlorophyll-a concentration, a satellite proxy for the abundance of phytoplankton at the base of the marine food web. By matching each turtle’s transmitted position with contemporaneous measurements of these variables, the researchers characterized the environmental envelope the juveniles actually occupy. The animals were found in waters with a mean sea surface temperature of 17.4 degrees Celsius, with the 10th to 90th percentile range spanning 15.4 to 19.9 degrees Celsius. Chlorophyll-a concentrations averaged 0.14 milligrams per cubic meter, with a 10th to 90th percentile range of 0.07 to 0.22 milligrams per cubic meter.

Those numbers may sound modest, but they encode a great deal of ecology. Loggerhead turtles are ectotherms, meaning their body temperature and activity levels track the surrounding water, so temperature effectively sets the thermal window within which they can forage, grow, and migrate. Chlorophyll-a, meanwhile, marks biologically productive water, the kind of frontal zones and nutrient-rich patches where the small prey items of juvenile loggerheads concentrate. Together, the two variables define a habitat signature: cool but not cold, productive but not eutrophic, characteristic of the transitional waters of the eastern North Pacific where warm subtropical and cooler subarctic water masses meet.

The team then applied these thresholds to a longer archive of oceanographic data spanning 2014 through 2025, a period representing warm ocean conditions in the region, including the influence of large-scale climate oscillations that periodically warm the northeastern Pacific. For each season and year, they mapped where conditions fell within the turtles’ observed environmental envelope, producing inferred patterns of suitable habitat across the entire eastern North Pacific. This approach allowed the researchers to move beyond the limited footprint of individual tracks and ask a broader question: across twelve years of varying ocean conditions, where does juvenile loggerhead habitat actually sit relative to the lines humans draw on the sea?

The answer was strikingly seasonal and strikingly international. Suitable habitat shifted through the year, with between 2 and 31 percent of the United States exclusive economic zone and between 1 and 13 percent of the Mexican exclusive economic zone containing suitable habitat annually. In other words, the amount of turtle-friendly water inside national waters varied enormously from season to season and year to year, expanding and contracting as oceanographic fronts moved and water masses shifted with the climate cycle. A juvenile loggerhead swimming through this dynamic seascape would cross jurisdictional boundaries not once but repeatedly over the course of its development.

The headline figure, however, concerns the high seas. Overall, 86 percent of the suitable habitat for juvenile loggerheads in the eastern North Pacific occurred in areas beyond national jurisdiction, compared with just 6 percent within the United States exclusive economic zone and 8 percent within the Mexican exclusive economic zone. For a population that spends years, possibly decades, in this developmental stage before returning toward western Pacific foraging grounds, the implication is unavoidable: the bulk of its juvenile life unfolds in waters where no single nation holds regulatory authority, and where conservation depends on international cooperation rather than any one country’s domestic protections.

This finding lands at a consequential moment for ocean governance. The high seas have long been a patchwork of sectoral management, with regional fisheries bodies, shipping regulations, and scattered agreements addressing individual activities but no comprehensive framework for biodiversity protection. The recent agreement under the United Nations Convention on the Law of the Sea on the conservation and sustainable use of marine biological diversity of areas beyond national jurisdiction, often called the BBNJ Agreement, aims to change that by enabling the establishment of marine protected areas on the high seas and requiring environmental impact assessments for activities there. The new loggerhead analysis provides exactly the kind of species-specific, jurisdictionally explicit evidence that such frameworks will need to prioritize particular regions for protection.

The study also underscores why migratory marine species pose such a persistent governance challenge. Unlike seabirds that return to fixed colonies or marine mammals tied to predictable coastal haul-outs, juvenile loggerheads drift through a moving mosaic of thermal fronts and productivity gradients that respect no maritime boundary. As climate change warms and reorganizes ocean conditions, the location and extent of suitable habitat will continue to shift, potentially drawing turtles into new areas, new fisheries interactions, and new management gaps. The authors’ use of a warm-conditions baseline for 2014 through 2025 is a deliberate acknowledgment of this reality: the habitat map of the recent past may not be the habitat map of the coming decades.

For the endangered North Pacific loggerhead population, the practical stakes are concrete. Where juveniles concentrate, they risk entanglement in fishing gear, and the effectiveness of any bycatch mitigation measure depends on knowing when and where turtles and fisheries overlap. The new environmental thresholds, a temperature window centered near 17 degrees Celsius and a chlorophyll-a range around 0.14 milligrams per cubic meter, offer fishery managers and conservation planners a transferable tool: satellite data, freely available in near real time, can now be used to anticipate where suitable juvenile habitat is likely to appear across both national waters and the high seas. Combined with the finding that the overwhelming share of that habitat lies beyond any single nation’s reach, the study makes a clear case that protecting this population’s youngest members is inherently a shared, international enterprise, one that the emerging high seas conservation architecture is only beginning to equip the world to undertake.

Subject of Research: Environmental preferences and jurisdictional distribution of juvenile loggerhead turtles in the eastern North Pacific

Article Title: Environmental preferences and jurisdictional distribution of juvenile loggerheads in the eastern North Pacific

Article References: Santos, B. S., Crowder, L. B., Balazs, G. H., Polovina, J. J., Abreu-Grobois, A., Gaspar, P., Lee Hing, C. A., Jim, L., Kurita, M., Mori, M., Parker, D. M., Rice, M. R., Saito, T., Yamaguchi, N., & Briscoe, D. K. (2026). Environmental preferences and jurisdictional distribution of juvenile loggerheads in the eastern North Pacific. npj Ocean Sustainability. https://doi.org/10.1038/s44183-026-00250-w

Image Credits: AI Generated

DOI: 10.1038/s44183-026-00250-w

Keywords: loggerhead turtles, satellite telemetry, eastern North Pacific, high seas, exclusive economic zone, sea surface temperature, chlorophyll-a, marine conservation, habitat modeling, BBNJ Agreement, endangered species, ocean governance

The study also carries a broader methodological message for the field of ancient DNA research. Because cold, permafrost-rich environments preserve genetic material far better than warmer regions, ancient genomic studies have historically concentrated on northern Eurasia, leaving vast stretches of the species’ southern range largely unexplored. The new findings underscore how much evolutionary history may be hidden in these overlooked southern landscapes, where diversity may have originated and persisted even as northern populations waxed and waned with the ice ages.

The woolly rhinoceros inhabited Eurasia for more than 100,000 years before its extinction near the beginning of the Holocene, persisting through the repeated glacial and interglacial cycles that defined the Pleistocene epoch. Despite this remarkable longevity across dramatic environmental swings, scientists have understood surprisingly little about how these climate fluctuations shaped the animal’s evolution, its population structure, and its eventual decline. Reconstructing that history has been hampered by a fundamental gap in the available data: nearly all previously sequenced woolly rhinoceros genomes came exclusively from Late Pleistocene Northeastern Siberia, a narrow sample that left major blind spots across the rest of the species’ vast range, particularly in mid-latitude Eurasia.

To close those gaps, a team led by Xiao-Le Lei screened 183 woolly rhinoceros fossils collected from across Eurasia. From this material, the researchers successfully recovered 29 mitochondrial genomes and 14 nuclear genomes, drawn from specimens spanning East Asia, the Altai region, and Europe. These newly generated data were then combined with previously published genomes from woolly rhinoceroses and related species, creating by far the most geographically and temporally comprehensive genetic dataset ever assembled for the species.

Dating the material required several complementary methods, which together placed the fossils across multiple stages of the Ice Age, extending back more than 200,000 years. This deep temporal reach allowed the team to track genetic lineages across successive glacial and interglacial periods, rather than viewing the species only through the narrow window of the Late Pleistocene, the final chapter of its existence. The result is a far richer picture of how woolly rhinoceros populations moved, mixed, and diverged over hundreds of millennia.

The researchers did not stop at genetics. They also modeled suitable woolly rhinoceros habitat across the past 500,000 years, drawing on the fossil record and climate data covering Pleistocene temperature, rainfall, and food availability. By overlaying this habitat reconstruction onto the genetic data, the team could evaluate how long-term environmental shifts influenced changes in the rhinoceros’s population history and genetic structure, connecting the movement of ice sheets and steppe ecosystems directly to the fates of individual breeding populations.

Taken together, the analyses reveal a species with a complex genetic history profoundly shaped by climate and shifting habitats. Mitochondrial evidence shows that the major maternal lineages of the woolly rhinoceros emerged roughly 460,000 years ago, and that a clear genetic divide separated populations in East and West Eurasia. Crucially, the greatest genetic diversity was concentrated in mid-latitude regions rather than in Siberia, inverting the picture suggested by the earlier, geographically skewed genomic record.

The nuclear genomic data sharpen this conclusion further. They indicate that the species’ greatest genetic diversity arose in mid-latitude East Asia, not in the high-latitude Siberian refuges often imagined as the species’ heartland. Meanwhile, the Altai region appears to have served as a long-term refugium and a dispersal hub during interglacial periods, a staging ground from which populations expanded and through which lineages exchanged genes as climates warmed and cooled. In this view, temperate Eurasia was not merely a peripheral habitat for the woolly rhinoceros but a cradle in which much of its diversity was born and maintained.

The study also uncovered an unexpected twist in the species’ family tree: clear evidence of gene flow between the woolly rhinoceros and Merck’s rhinoceros, a more temperate-adapted relative. The genetic signal indicates that the two species interbred during the Late Pleistocene, adding the woolly rhinoceros to a growing list of Ice Age megafauna whose boundaries were more permeable than their skeletons suggest. For conservation-minded scientists studying today’s rhinoceroses, the findings offer a reminder that even seemingly well-understood Ice Age icons still hold surprises, and that the southern edges of ancient ranges may preserve the deepest secrets of how species survive planetary change.

Subject of Research: Ancient genomic and habitat modeling analysis of woolly rhinoceros population history across Pleistocene Eurasia

Article Title: Temperate Eurasia was a cradle of woolly rhinoceros genetic diversity

Article References: Temperate Eurasia was a cradle of woolly rhinoceros genetic diversity. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: woolly rhinoceros, ancient DNA, Pleistocene, paleogenomics, Eurasia, Siberia, Altai, genetic diversity, climate cycles, Merck's rhinoceros, habitat modeling, extinction