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Site selectionWe selected 104 sites covering a range of ecological, land cover, and climate conditions across North America (Table 1). These sites were selected because they are part of either the National Ecological Observatory Network (NEON) or AmeriFlux network, all have PhenoCams, and each has at least one year of available flux data between 2017 and 2021. Among the included sites, 44 are part of the NEON.Table 1 List of AmeriFlux and NEON sites included in the dataset. .Full size tablePlanetScope image database compilationThe LSP metrics included in the dataset are derived from a database of daily 3 m PlanetScope imagery. To compile this database, a Python script was created to search, request, and download imagery using Planet’s RESTful API interface (https://developers.planet.com/docs/apis/data/). For each site, the area of interest (AOI) was defined using a GeoJSON file that prescribed a 10 by 10 km box centered over the flux tower at each site. Each GeoJSON was then used to submit search requests to the API. As part of the search process, the following filters were applied to ensure that good quality images with adequate clear sky views and high-accuracy geolocation were downloaded: (1) quality category identified as ‘standard’; (2) cloud cover less than or equal to 50%; and (3) ground control is ‘true’. Filtering was performed using all available PlanetScope ‘PSScene4Band’ imagery from 2016 to 2022. Once the API completed the search, the Python script read the search results, submitted orders, and the selected imagery was downloaded from Planet’s cloud-based system to local storage. During execution of the Python script, a log file was created to keep track of successful and failed orders. If an order failed, the script was run again targeting the specific order that failed. The resulting dataset included over 1.8 million unique files with, on average, 3,885 scene images for each site (i.e., the number of images, on average, that overlap part of each 10 by 10 km site), and had a total volume of 62.2 TB.Image processingTo ensure that high-quality image time series were used to generate LSP metrics, we used PlanetScope per-pixel quality assurance information to exclude pixels that had low quality in all 4 bands (i.e., blue, green, red, and near-infrared). Specifically, we excluded pixels where the Unusable Data Mask (layer ‘umd’) was not 0 (i.e., we retained pixels that were not cloud contaminated or located in non-image areas) and pixels where the Usable Data Mask (layer ‘umd2’) is 0 (i.e., we retained pixels that were not contaminated by snow, shadow, haze, or clouds). We then cropped all the images to exclude pixels outside of the 10 by 10 km window centered over each tower. We selected this window size based on published results showing that 80% of the average monthly footprint at eddy covariance towers ranges from 103 to 107 square meters22. Note that the swath for PlanetScope imagery often did not cover entire sites and some sites (e.g., the tall tower at US-Pfa) have larger footprints than other sites. Similarly, most sites had multiple PlanetScope image acquisitions on the same day. To create image time series, we mosaiced all available imagery at each site on each date, and, under the assumption that geolocation error was non-systematic and modest, we created a single image for each date using the mean surface reflectance for pixels with multiple values on the same day. The resulting database of daily surface reflectance images were sorted in chronological order, sub-divided into 200 sub-areas at each site (i.e., 0.5 km2 each), and then stored as image stacks to facilitate parallel processing to estimate LSP metrics, where each image stack included all images from July 1, 2016 through January 31, 2022.Creation of daily EVI2 time seriesTo estimate LSP metrics we adapted the algorithm described by Bolton et al.19, which was originally implemented to estimate LSP metrics from harmonized Landsat and Sentinel-2 (HLS) imagery, for use with PlanetScope imagery. Prior to LSP estimation, daily images of the two-band Enhanced Vegetation Index30 (EVI2) data were generated from PlanetScope imagery and then interpolated to create smooth time series of daily EVI2 values at each pixel in three main steps. First, sources of variation related to clouds, atmospheric aerosols, and snow were detected and removed from the EVI2 time series at each pixel based on data masks provided with PlanetScope imagery (described above) and outlier detection criteria (i.e., de-spiking and removing negative EVI2 values). Second, we identified the ‘background’ EVI2 value (the minimum EVI2 value outside of the growing season) based on the 10th percentile of snow-free EVI2 values at each pixel. Any dates with EVI2 values below the background value were replaced with the background EVI2. Third, penalized cubic smoothing splines were used to gap-fill and smooth the data to create daily EVI2 time series across all years of available data. Complete details on these steps are given in Bolton et al.19. This approach has been tested and shown to yield PlanetScope EVI2 time series that are consistent with both EVI2 time series from HLS imagery and time series of the Green Chromatic Coordinate (GCC) from PhenoCam imagery26. We used the EVI2 instead of other vegetation indices such as the Enhanced Vegetation Index (EVI) or the Normalized Difference Vegetation Index (NDVI) because EVI2 is less sensitive to noise from atmospheric effects relative to EVI and is less prone to saturation over dense canopies and noise from variation in soil background reflectance over sparse canopies relative to the NDVI30,32. Thus, phenological metrics from EVI2 time series tend to have better agreement with PhenoCam observations than corresponding metrics from NDVI33.Identifying phenological cyclesPrior to estimating LSP metrics, we first identity unique growth cycles by searching the period before and after each local peak in the daily PlanetScope EVI2 time series. To be considered a valid growth cycle, the difference in EVI2 between the local minimum and maximum was required to be at least 0.1 and greater than 35% of the total range in EVI2 over the 24-month period centered on the target year ± 6 months. The start of each growth cycle is restricted to occur within 185 days before the peak of the cycle and at least 30 days after the previous peak. The same procedure was applied in reverse at the end of the cycle to constrain the range of end dates for each growth cycle. This procedure is applied recursively over the time series until each local peak has been assessed and all growth cycles (with associated green-up period, peak greenness, and green-down period) are identified in the time series at each pixel. As part of this process, the algorithm provides the number of growth cycles identified for each year in the time series.Retrieving LSP metricsLSP metrics are estimated for each pixel in up to two growth cycles in each year. If no growth cycles are detected, the algorithm returns fill values for all timing metrics, but does report values for the four annual metrics: EVImax, EVIamp, EVIarea, and numObs (see below). If more than two growth cycles are detected, which is rare but does occur (e.g., alfalfa, which is harvested and regrows multiple times in a year), the algorithm records 7 LSP metrics for each of the two growth cycles with the largest EVI2 amplitudes. The resulting dataset includes seven ‘timing’ metrics that identify the timing of greenup onset, mid-greenup, maturity, peak EVI2, greendown onset, mid-greendown, and dormancy. These metrics record the day of year (DOY) when the EVI2 time series exceeds 15%, 50%, and 90% of EVI2 amplitude during the greenup phase, reaches its maximum, and goes below 90%, 50%, and 15% of EVI2 amplitude during the greendown phase. In addition, the algorithm records three complementary metrics that characterize the magnitude of seasonality and total ‘greenness’ at each pixel in each growth cycle: the EVI2 amplitude, the maximum EVI2, and the growing season integral of EVI2, which is calculated as the sum of daily EVI2 values between the growth cycle start- and end-dates (i.e., from greenup onset to dormancy).Quality assurance flagsQuality Assurance (QA) values are estimated at each pixel based on the density of observations and the quality of spline fits during each phenophase of the growing season. A QA value of 1 (high quality) is assigned if the correlation between observed versus fitted daily EVI2 values is greater than 0.75 and the maximum gap during each phase is less than 30 days. A QA value of 2 (moderate quality) is assigned if the correlation coefficient is less than 0.75 or the length of the maximum gap over the segment is greater than 30 days. A QA value of 3 (low quality) is assigned if the correlation coefficient is less than 0.75 and the length of the maximum gap over the segment is greater than 30 days. A QA value of 4 is assigned if no growth cycles were detected or insufficient data were available to run the algorithm. More

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Our research involved work with animal subjects (unionid mussels and Round Goby fishes) and was conducted following relevant regulations and standard procedures. The field collections were carried out under Pennsylvania Fish and Boat Commission permits (# 2018-01-0136 and 2019-01-0026). The experimental protocols were approved by Penn State University’s Institutional Animal Care and Use Committee (IACUC# 201646941 and 201646962). All new DNA sequencing data are made publicly available in GenBank (with accession numbers provided in Table 1) and a BioProject (# PRJNA813547) of the National Center for Biotechnology Information40.Propensity of Round Goby to consume unionid mussels in a controlled lab settingStream table setupWe conducted lab experiments to observe the potential predation of juvenile freshwater mussels by the Round Goby, following standard research protocols for work with animal subjects (IACUC# 201646962, Penn State University). We constructed four artificial stream tables in an aquatic laboratory, each measuring 3 × 2 m and featuring two run and two pool sections (each 0.63 × 0.56 × 0.46 m). Water flow was produced using eight Homsay 920 GPH submersible water pumps, which pumped water from a central reservoir tub into each table at the start of each run section. The water flow direction was clockwise for stream tables 1 and 3, and counterclockwise for stream tables 2 and 4. Water pumped into the stream tables exited via two drains located medially of each run section, where it flowed back to the central reservoir tub. Each stream table was filled with a 6 mm layer of substrate consisting of a mixture of sand, gravel (4–6 mm), and crushed stone (size 2B, with an average size of ~ 19 mm). The day before each experiment, field technicians traveled to local streams and collected macroinvertebrates using one minute D-frame kick net samples for each of the four stream tables. The macroinvertebrates and associated substrate were transported back to the facility and were introduced into each stream table system.Preferential feeding experimentsBefore each experiment commenced, juvenile Plain Pocketbook mussels (Lampsillis cardium) were introduced into each stream table (with 165 mussel specimens for experiment 1 and 100 mussels for experiments 2 and 3). This  widespread and abundant species is not imperiled in Pennsylvania, and mussels were provided for this study by the White Sulphur Springs National Fish Hatchery located in southeast West Virgina. The mussels were allowed to acclimate in the stream tables for 2 h before commencing each experiment. Ten Round Gobies were introduced into each stream system (stream tables 1 and 2 for experiment 1, and all tables for experiments 2 and 3). The total length (from nose tip to caudal tip) of each fish was measured prior to introduction and after the termination of experiments 2 and 3. Experiment 1 was conducted for 3 weeks, while experiments 2 and 3 were conducted for 8 days. During these experiments, Round Gobies were allowed to exist in the systems and feed preferentially, on the mussels and macroinvertebrates, for the allotted time before each investigation concluded. We acknowledge that in these experiments, the mussel abundances are higher and macroinvertebrate densities lower and less rich than commonly occur in the natural stream environment. Further, the Round Goby fish densities used are much higher than currently in the French Creek watershed, though are comparable to what is currently seen in parts of the Great Lakes basin. Nonetheless, the experiment scenarios allowed us to observe if Round Gobies would consume the mussels when given the choice to feed on a variety of food items.Evaluation of unionids consumed by fishRound Gobies were removed from the stream tables upon completion of each experiment. They were euthanized using  > 250 mg/L buffered (pH ~ 7) tricaine-S (MS222) solution. The fish were submerged for 10 min beyond the cessation of opercular movement to ensure proper euthanasia, and tissues were collected after we confirmed complete euthanasia—compliant with AVMA guidelines and approved by the IACUC protocol. The Round Gobies were placed in a 10% solution of formalin for preservation, and after 2 weeks, they were rinsed with clean water and were placed in 70% ethanol for long-term storage. After fish were removed from the system, the water was drained, and the substrate was sifted to recover the remaining mussels. Mussels were counted, and live individuals were returned to holding tanks for use in subsequent experiments. To further assess whether Round Gobies had consumed mussels during the investigation, Round Gobies were x-rayed using a Bruker Skyscan 1176 micro-CT scanner. After that, the stomachs of each fish were excised, and the contents examined using a Leica CME dissection scope to confirm the identity of Plain Pocketbook mussels. Contents posterior to the stomach were not analyzed because they could not be reliably counted and identified.DNA metabarcoding to identify mussel species consumed by Round Goby in a stream settingFish and mussel sample acquisitionWe collected 39 Round Gobies directly from streams within the French Creek watershed—their newly invaded natural stream habitats—in June 2018. We aimed to quantify which species, if any, of unionid mussels they consumed. Fish collection locations included LeBoeuf Creek at Moore Road and 100 m below the confluence of French Creek and LeBoeuf Creek. The unionid mussel populations and the environmental field settings at these locations are detailed by Clark et al.19. A team of field technicians collected fish by kick seining (3 m × 1 m × 9.5 mm nylon mesh) while moving downstream. Seining was the sampling method of choice compared to electrofishing to avoid possible regurgitation of food items prior to excision of fishes’ stomachs. The stream reaches sampled at each location were between 100 and 200 m in length and included riffle, run, and pool habitats. In addition to fish samples, unionid mussel samples from French Creek were also collected for analysis (under Pennsylvania Fish & Boat Commission collectors permits # 2018-01-0136 and 2019-01-0026). Following standard research protocols (under IACUC# 201646941, Penn State University), the Round Gobies collected were euthanized using buffered Tricaine-S (MS222) solution; and stomachs were excised using sterilized utensils before being placed in sterilized tubes filled with 97% ethanol. After excision of stomachs, fishes were placed in a 10% formalin solution for preservation. After 2 weeks, fishes were rinsed with clean water and transferred to 70% ethanol for storage. The stomach samples were immediately placed in ethanol and on ice in the field. Samples were stored in a freezer before being shipped to the US Geological Survey’s Eastern Ecological Science Center for various molecular ecology analyses. Once the fish and mussel samples arrived at this lab, they were recorded and stored at four °C until analysis.Primer developmentSpecific primers targeting a moderately conserved region of the mitochondrial COI gene for 25 species of unionids inhabiting French Creek were designed. Previously a PCR-based amplification method utilizing restriction enzyme digests was used to identify genetic fingerprints of 25 unionid species inhabiting French Creek41. Here, we designed a new degenerate PCR primer set modified with sequencing overhangs to facilitate compatibility with a MiSeq amplicon sequencing method previously designed for 16S Amplicon sequencing. We targeted the locus of the mitochondrial COI gene of unionids known to inhabit the Atlantic Slope Drainage. Consensus sequences were derived using Multalin analysis and a tiling method to identify conserved primer binding regions flanking an ~ 300 bp region of the COI gene. This gene was targeted in part due to the availability of partial or complete sequences representing these target species in the NCBI reference database40. Cytochrome oxidase sequences were downloaded for the 25 unionid mussel species of interest. However, a COI sequence for the Rabbitsfoot (Theliderma cylindrica) mussel was absent from the NCBI database, which required us to sequence this region for an in-house reference (which is described later in the paper). We designed a degenerate primer cocktail specific to all mussel species of interest that amplified a ~ 289 bp product, with forward and reverse primers used for the amplification of unionid specific COI presented as supplemental information (see Table S-230. We evaluated the suitability of the primers using samples from field identified mussels. For primer optimization, PCR was run across a gradient of annealing temperatures to determine suitability. In addition, we used Round Goby DNA as a template to evaluate specificity. In addition to Round Goby stomach samples, mussel samples of several species collected from French Creek were included as positive controls.DNA extraction from tissue samplesFollowing the manufacturer’s protocols, tissue samples (including fish stomach and mussel tissue) were extracted with the Zymo Research ZymoBIOMICS 96 MagBead DNA Kit (San Diego, CA). Random samples of DNA extracts were analyzed on an Agilent 2100 Bioanalyzer using a high-sensitivity assay kit. Fragments in the target amplicon range were apparent (albeit not known to be of mussel origin). All samples were stored at − 20 °C until PCR was performed. DNA from both the T. cylindrica and L. complanata samples were analyzed for DNA quality.Rolling circle amplification of mitochondrial genomesTo acquire COI sequences for T. cylindrica and L. complanata, we subjected archived DNA samples to rolling circle amplification (RCA) followed by amplicon sequencing on the MiSeq. In short, 2 µl of DNA template was added to 2 µl Equiphi29 DNA polymerase reaction buffer containing 1 µl of Exonuclease-resistant random primers (ThermoFisher). Samples were denatured by heating to 95 °C for 3 min followed immediately by cooling on ice for more than 5 min. A volume of 5 µl was added to an RCA master mix containing 1.5 µl of 10 × Equiphi29 DNA polymerase reaction buffer, 0.2 µl of 100 mM dithiothreitol, 8 µl of 2.5 mM dNTPs, 1 µl of Eqiphi29 DNA polymerase (10U) and 4.3 µl of nuclease-free water. The samples were heated to 45 °C for 3 h and then 65 °C for 10 min. Samples were then placed in ice and then frozen at − 20 °C. All RCA products were normalized to 0.2 ng/µl in 10 mM Tris–HCl, pH 8.5. Normalized RCA product was utilized as a template for an Illumina Nextera XT library preparation. Sequencing libraries were prepared following the Nextera XT Library Preparation Reference Guide (CT# 15031942 v01) using the Nextera XT Library Preparation Kit (Illumina, San Diego, CA). Final libraries were analyzed for size and quality using the Agilent BioAnalyzer with the accompanying DNA 1000 Kit (Agilent, Santa Clara, CA). Libraries were quantified using the Qubit H.S. Assay Kit (Invitrogen, Carlsbad, CA) and normalized to 4 nM using 10 mM Tris, pH 8.5. Libraries were pooled and run on the Illumina MiSeq at a concentration of 10 pM with a 5% PhiX spike with run parameters of 1 × 150. Bioinformatic processing of this data is outlined below.Amplification of the cytochrome oxidase 1 geneExtracted genomic DNA was used as template for end-point PCR. Samples evaluated were from mussels and round gobies (see supporting Table S-330). The ~ 289 bp COI region was amplified with the mussel primers as follows. The amplification reaction contained 0.15 µM of each primer, 1 µL of the initial amplification product, and Promega Go Taq Green Master Mix following manufacturer recommendations for a 25 µL reaction. The thermocycler program consisted of an initial denaturing step of 95 °C for 3 min, followed by 30 cycles of 30 s at 95 °C, 30 s at 52 °C, and 1 min at 72 °C. Products were subjected to a final extension of 72 °C for 5 min then held until collection at 12 °C. An appropriately sized amplification product was confirmed for each reaction by electrophoresis of 5 µL of the reaction product through a 1.5% I.D. N.A. agarose gel (FMC Bioproducts) at 100 V for 45 min. PCR products were cleaned with the Qiagen Qiaquick PCR purification kit (Valencia, CA) and quantified using the Qubit dsDNA H.S. Assay Kit (Thermofisher Scientific, Grand Island, NY). Samples were diluted in 10 mM Tris buffer (pH 8.5) to a final concentration of 5 ng/µL.Generation of mock mussel samplesTo better understand and minimize sources of error or bias in the taxonomic assignment, we created a mock extraction by mixing sequences from known mussel taxa at defined concentrations. For each mussel, approximately 25-mg of tissue was extracted with the ZymoBIOMICS 96 MagBead DNA Kit (San Diego, CA) following the manufacturer’s protocol. The COI sequence was amplified from each species using the same primer-protocol combination described above. A total of 5 PCR products were mixed at equal concentration (mass/volume) to generate the mock sample (“Mock” hereafter). To confirm the identity of these inputs, each COI region was amplified and sequenced on the Illumina MiSeq during the same run as the Mock and samples.Sequencing library preparation and quality assessmentNext-generation sequencing was performed on the Illumina MiSeq platform to observe species-specific sequences and determine the diet of the Round Goby. Inclusion of the overhangs on the amplification primers allowed us to utilize the Illumina 16S Metagenomic Sequencing Library Preparation protocol42. Amplicon libraries were prepared following the same manufacturer’s protocol. All samples were indexed using the Illumina Nextera XT multiplex library indices. DNA read size spectra were determined with the Agilent 2100 Bioanalyzer using the Agilent DNA 1000 Kit (Santa Clara, Calif.). Libraries were quantified with the Qubit dsDNA H.S. Assay Kit (ThermoFisher Scientific, Grand Island, N.Y.) and normalized to 4 nM (nM) using 10 mM (mM) Tris (hydroxymethyl) aminomethane buffer pH 8.5. A final concentration of 10 picomolar library with a 6.5% PhiX control spike was created with the combined pool of all indexed libraries. All bioinformatic operations were completed on CLC Genomic Workbench v20 (Qiagen, Valencia, Calif.).Read filtering, trimming, and RNAseq metabarcoding assemblyFASTQ files from the sequencing runs were imported as paired-end reads into CLC Genomics Workbench v20.0.4 (Qiagen Bioinformatics, Redwood City, Calif.) for initial filtering of exogenous sequence adaptors and poor-quality base calls. The trimmed overlapping paired-end reads were mapped to the 25 target unionid sequences specific for the species of interest. Several mapping iterations were run using different levels of stringency. We utilized + 2/− 3 match-mismatch scoring and set the length fraction to 0.90. Analyses were iterated using different similarity fractions ranging from 0.90 to 0.99. Reads were annotated, and relative abundance was determined using a curated reference library (see supporting Datasets S-1 and S-230). More

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By pooling the results of the entire known range analysis of 14 GPS-collared elephants living in the Kinabatangan, our study suggests that this populations range covers at least 628 km2 (Table 3). Nine different locations were identified as hot spots, representing 266.9 km2 or 43% of this range, suggesting that just under half is highly used and/or frequented (Fig. 1). We found that the size of individual’s hot spots was positively related to the size of the entire range, meaning the larger the entire range the larger the summed area of an elephants hot spots. On average, hot spots represented a relatively small percent of an animal’s entire range (ranging from 4 to 20%, averaging 12%, Table 3). However, time spent within these hot spots ranged from 10 to 60% (averaging 34% across elephants, Table 5), with time spent in hot spots being related to the overall size of the hot spots (the larger the hot spot the more time elephants spent in them).Identifying the location of these hot spots is essential in designing appropriate management practices in collaboration with land users and identifying the best location for elephant corridors. In the last 25 years, forest cover in the Lower Kinabatangan has been drastically reduced and fragmented46, eroding the biodiversity value of this landscape. Today, this region has little remaining forests, and what is left is insufficient for sustaining the local elephant population10. Moreover, forests are highly fragmented along the Kinabatangan River, with a number of bottlenecks constraining elephant movements9. The situation in this landscape is getting worse because of further land clearances for agriculture, namely oil palm; as well as for the highly controversial Sukau Bridge and new road/highway that is planned for the region.Our analyses revealed a highly significant difference between the average proportions of protected area, unprotected forest, and oil palm estate extents within the elephant’s entire range; and a substantive, but not significant, difference across these land use/land cover types within hot spots (Table SI 4). At the individual level, there was a highly significant negative relationship between the proportion of protected areas and oil palm estates both within the elephant’s entire range and within the hot spots.At the pooled level, we found that around 45% of the entire known range and hot spots were within forested environments (280.44 km2 and 120.29 km2 respectively). Our results showed strong fidelity of certain elephants to these forested habitats. Our k-means cluster analysis found that within elephant entire ranges and hot spots, two out of the three cluster groups had high or very high usage of forests. Both cluster 1, for the entire range, and cluster 1 for hot spots extents, had five females that on average used forest environments 90% of their time, with protected areas being used 64% and 59%, and unprotected forested being used on average 26% and 31%, respectively (Table 7).Individuals in cluster 2, for the entire range analysis, on average, spent 73% of their time in forests (57% of this in protected areas and 16% in unprotected forests; Table 7). For the hot spot analysis, the individuals in cluster 2 spent on average 65% of their time in forests (52% of this in the unprotected forests and 13% in protected forests; Table 7). Elephants within these clusters were all females. Our results suggest that forest may be of particular importance for females as they had forest as their dominant land cover type within their entire range, hot spot extents and time spent analyses (Fig. 3, Table 5). Several studies have shown that adult females influence and guide the movement patterns and habitat utilization for their family group and that females in family units tend to inhabit less risky areas, such as within natural forest habitat60,61,62.However, the unprotected forest is at risk. We identified about 8% (or 49 km2) of forest identified within the pooled entire known range were not protected, with half potentially being on state land, and the remaining half on land titles of various types (Table SI 4). For the pooled hot spot areas, unprotected forest was proportionally higher, comprising of 11% (or 29 km2) of the total extent, with 54% being potentially on State land and 46% on land titles (Table SI 4). Protecting these forests would be an essential and efficient way to secure key elephant habitat since all collared individuals were using these forest fragments in their entire range (averaging 11%, and ranging from 8 to 18%), and hot spot extents (averaging 20%, and ranging from 0 to 53%) (Table SI 4, Fig. 3). On average, 24% of time was spent in unprotected forests within hot spots, though this varied widely from 0% (for the male elephant known as Gading) to 61% (for the female matriarch named Jasmine) (Table 5). In fact, five females had large proportions of their hot spot extents (24–53%) in unprotected forests, spending substantial periods of their time (33–61%) within these threatened areas.Our findings show that unprotected forests around the villages of Bilit and Sukau, were of particular significance (Figs. 1, 2). These unprotected forests largely consist of lowland dry forest, seasonally flooded swamp forest, and swamp forest, which are considered important habitats for elephants for feeding, resting and moving47,63. Within these forests, and along the forest margins and river banks there are also natural open grasslands that consist of Phragmites karka and Dinochloa scabrida that provide essential forage, mainly in the riparian areas for elephants9,21,23. Forested environments are also considered to be important in providing natural refugee from human activities and disturbance. For example, elephants have been documented to form significantly larger group sizes, as well as engaging in significantly more social interactions, in natural forest habitat compared to, for example, oil palm landscapes63. Adult females, generally, avoid areas considered unsafe for their respective social units, are more selective in the resources they use, and require regular access to water because of the presence of young64,65,66. This may be why our results, strongly suggest that forest habitats seem to be most important for adult females.Another significant issue faced by these elephants is the threat from the controversial planned Sukau bridge and road/highway that is set out in the Sabah Structure Plan, an overarching policy document for the State58. Currently, a new road/highway is under construction on the northern bank of the village of Sukau, and this has already cleared areas of unprotected forest. This public road could link to a potential new bridge that would cross over the Kinabatangan River, cutting through unprotected forest and a protected area (Lower Kinabatangan Wildlife Sanctuary), before going through oil palm estates then through another protected area to the south and through the Tabin elephant population range. For the Kinabatangan, creating a public highway will cut the elephant population range into two parts (Figs. 2, 3). All collared elephants use this area, as it is a key bottleneck and the only alternative option to pass around Sukau village9. We found that nine elephants have hot spots that intersect or meet up with the current road (which will be up-graded and get considerably busier) and/or the planned road/highway alignment (Figs. SI 1 and 2). For these elephants, we calculated that they spent from 2 to 44% (average 14%) of their time within these hot spots (Table 4). Our statistical analyses suggest that if the road/highway goes ahead it will have a significant impact on the elephants’ behaviour with respect to time spent in the hot spots. Indeed, this infrastructure project could have dire consequences for these elephants and their family groups, by disrupting their ranging patterns and segmenting the entire elephant range into two (Figs. 2, 4). The existing road in Batu Putih has already proven to be an impassable barrier for this elephant population, as no elephants have been observed crossing this road since the early 2000s14. For elephants that do try and cross, vehicle collisions may become a significant threat to elephants and drivers alike67, and potentially increasing human–elephant conflict in the nearby villages, as well as in plantations14,68,69, exacerbating an already difficult situation for this small and fragmented population.Results from the pooled analysis show that about 53% of the entire known population range is within oil palm estates; and 51% for the pooled hot spots (Fig. 3, Table SI 4). Our k-means clustering analysis grouped 6 elephants into cluster 3 that on average spent 57% of time in oil palm estates; and 7 elephants into cluster 2 within the hot spot analysis that on average spent 73% of their time in oil palm estates (Table 6). All the males, were clustered within these groups (Table 5). In fact, the three collared males were amongst the highest users of oil palm estates (Fig. 3, Table SI4, and 5). This could be related to a ‘‘high risk, high gain’’ strategy, often adopted by males to increase body size and enhance reproductive success32,33,60. However, it is interesting to see that three females (Ita, Ratu and Koyah) and their respective social units, also seemed to have high levels of oil palm use, while other individuals had zero or very little use of oil palm (e.g. Aqeela, Jasmin, Sandi, Kasih; Table SI 4, Fig. 3). Differential choices may result from differences in individual knowledge and experience with people during past encounters, for example70,71.We identified that collared elephants were ranging in 11 known oil palm estates, with the five most regularly used being Melangking Oil Palm Plantation (with 12 elephants entire range overlapping with this estate and six hot spots), IOI Corporation (with 11 overlapping entire ranges, and eight hot spots), Genting Plantations (14 and seven, respectively), Sime Darby Plantation (five and two, respectively), and Karangan Agriculture (8 and 2, respectively) (Table 6; Fig. 4). Presence of bottlenecks and barriers (e.g. electric fences) may explain hot spot occurrences in these estates, as well as feeding opportunities, management strategies of specific estates, and historical and seasonal ranges.Linear features like major highways, electric fences and drainage ditches hamper elephant movements within the Lower Kinabatangan9. A previous study identified 20 bottlenecks in the Lower Kinabatangan with the two main ones (of 9 km and 6.5 km in length) found around the village of Sukau9. In addition, the unplanned and chaotic erection of electric fences by large estates and smallholdings has disrupted significantly elephant movement patterns and resulted in artificial hot spots for certain individuals (e.g. Liun, Ita, Gading and Sejati)35,72. Electric fences have widely been used to mitigate human–elephant conflicts. The establishment of fences rarely consider the traditional elephant routes nor the location of existing fences in neighbouring estates. If elephants manage to enter such areas, they often become trapped and experience difficulties in returning to nearby forests, exacerbating conflicts with people35.Certain estates such as Melangking Oil Palm Plantation have allowed elephants to roam freely in their estate (Muhammad Al-Shafieq, personal communication). Since 2017, this plantation has shown a drastic reduction in damages to their oil palms following the removal of their permanent electric fences surrounding their entire estate. Instead, this plantation is using a temporary electric fencing regime around newly planted palm areas. Concurrently, they now do not push elephants out of their estate, which can explain why Melangking Oil Palm Plantation is a significant hotspot in the region.Another reason why elephant ranges incorporate oil palm estates is to move between forest patches that are becoming completely isolated following forest conversion, as is the case close to Sukau (Fig. SI1 and SI2; Fig. 1). Unlike other elephant species that increase their speed of movement rates in highly disturbed areas27,30,66, the Bornean elephant has been observed doing the opposite, which may explain some of the hot spots within oil palm estates. This movement strategy may allow for better vigilance as seen on a few occasions when elephants spent 2–5 days in the Bukit Melapi-Yu Kwang Corridor, near the village of Sukau, before leaving the area (Othman, personal observation).Hot spots in the oil palm landscape can also be explained by feeding opportunities, since elephants feed on palm shoots, leaves and hearts73. Elephants are known to eat the shoots of newly planted oil palms, often killing the palms and causing significant economic damages35. Since 2010, many estates located in the Lower Kinabatangan have started a new palm rotation. Palms are replanted every 25 years. A new rotation includes land clearing, bole and root mass removal, and the shredding or chipping of felled palms. Elephants are attracted to the shredded palm hearts since it gives them easy access to one of their favourite food72. This particular behaviour does not cause economic damage, and some estate managers allow the elephants to stay and forage in the chipping areas. This was documented for several collared elephants, whose hot spots and time spent were particularly high within oil palm (e.g. Gading and Sandy, two males; and Ratu and Ita, two females). Once the shredded palms have dried, however, elephants will leave these areas and move elsewhere. Within oil palm estates, some elephants have been found to travel more directly and rapidly suggesting ‘exploratory’ behaviour, which could be associated with searching for young palms or areas of palm felling and chipping of palm hearts15.Lastly, elephants may still be using their historical range that used to be covered with forest before conversion to oil palm. Other factors potentially explaining the relatively high use of oil palm estates include seasonal variations of ranging patterns. Indeed events of drought or floods limit the access to various parts of the floodplain and will tend to confine the animals in some areas9,63.In Sabah the state authorities have recorded at least 200 elephant deaths from the year 2010 to 2021 and most of these have occurred on, or near, oil palm estates14,74,75,76. Deaths from non-natural causes are largely due to poisoning (both accidental and intentional), gunshot wounds, poaching for tusks and other body parts, and snares35. Stopping killing and enabling a safe coexistence between people and elephants within multiple-use landscapes that are dominated by oil palm is one of the key strategies developed in the Bornean Elephant Action Plan for Sabah (2020–2029), which was endorsed by the State14. Based on our results in Lower Kinabatangan, a series of recommendations are proposed.This study underscores the importance of remaining forested areas for the Lower Kinabatangan elephant population. Full protection of all forest fragments left in the Lower Kinabatangan is urgently needed. Several official mechanisms are available to fulfil this request that has been proposed for the past 20 years by various organizations46.The current network of forests available in the Lower Kinabatangan is too small and fragmented to sustain a viable elephant population. Forest corridors must be created across the landscape through reforestation exercises, whilst concurrently undertaking enrichment planting of native understory forage within forested areas as this may minimize the need for elephants to search for easily accessible food in high-risk oil palm landscapes21,22,23.Current governmental plans to build a road bridge and public road/highway linking the southern bank of the Kinabatangan River to Tabin Wildlife Reserve to the south will irreversibly impact the Lower Kinabatangan elephant population by cutting the current range into two isolated parts. This will impact the elephants ranging patterns, potentially even fragmenting the already small population into two groups, and potentially leading to elephant deaths by vehicle collisions (which is becoming increasingly common in Peninsular Malaysia), and increase the risk of poaching activities, all resulting in a decrease in the genetic diversity of the, already small and isolated, population14,67.Eventually, the future of the Kinabatangan elephant population resides in improving land use and management practices within oil palm estates currently used by elephants. We recommend that priority should be given at improving elephant movements in oil palm estates by removing unnecessary man-made barriers and only cautiously installing temporary electric fences to protect sensitive areas. For example, the use of electric fences around mature oil palm and areas whereby palms are being removed and chipped could be prohibited, and electric fences permitted solely for protecting oil palm nurseries, new plantings and young oil palms (e.g. up to 7–8 years old), and staff and office quarters. This would greatly allow for landscape permeability for elephants, and other species that need to cross the landscape for their ecological and biological needs14.A handful of guidelines exist to assist oil palm managers and staff in managing elephant populations in their respective estates72,77. However, there is a need for a more comprehensive set of guidelines, which delineate better practices with the aim to increase the protection of people and elephants outside protected areas. Guidelines should specify “do’s” and “don’ts” (based on best available data and knowledge) of actions needed before, during and after elephants visit oil palm estates and smallholdings.Sabah now is in an interesting transition within their palm oil sector. On the 21st October 2015, the Sabah State Cabinet committed to produce 100% certified sustainable palm oil, by 2025, under the Roundtable for Sustainable palm Oil (RSPO) Jurisdictional Certification approach. Under this approach, areas of High Conservation Value and areas identified within the High Carbon Stock Approach need specific management and monitoring, in order to comply with RSPO principles and criteria78,79,80. Sabah government can use this platform to build an integrated landscape level approach to better manage landscapes within known elephant ranges (which is considered a High Conservation Value species) to allow for a safe and permeable movement through the landscape.Eventually, long-term survival of the Bornean elephant will mainly depend on how people and elephants can co-exist. It is our hope that this study illustrates the importance of protecting all forested habitat and effectively managing areas outside of protected areas to allow for long-term elephant coexistence with humans in this landscape. More