Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring.

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

Linking microphytobenthos distribution and mudflat geomorphology under varying sedimentary regimes using unoccupied aerial vehicle (UAV)-acquired multispectral reflectance and photogrammetry.

Microphytobenthic (MPB) biofilms play significant roles in the ecology of coastal mudflats, including provision of essential food resources to shorebird species. In these ecosystems, water-divergence structures like jetties and causeways can drastically alter sedimentation patterns and mudflat topography, yet their effects on MPB biofilm biomass and distribution are poorly understood. Here, we used a combination of unoccupied aerial vehicle (UAV) technologies, photogrammetric processing, and sediment field samples to compare biofilm and mudflat characteristics between areas of the Fraser River Estuary with varying sedimentary regimes and shorebird use. Our aims were to: (1) demonstrate the use of fine spatial resolution UAV-acquired multispectral imagery (cm2) with extensive spatial coverage (>km2) and a co-alignment photogrammetric processing techniques to survey MPB biofilm and mudflat topography at spatial scales and detail relevant to foraging shorebirds; and, (2) investigate the effects of water-divergence structures on mudflat elevation and microtopography, as well as MPB biofilm biomass, distribution, and spatial patterning. From a technical perspective, co-alignment allowed us to analyze aligned and continuous fine-resolution elevation models and orthomosaics for large areas of the estuary, while the normalized difference vegetation index was a good predictor of sediment chlorophyll-a (R2 = 0.9). Using these data products, we found that mudflats in close proximity to water-divergence structures have cross-shore profiles characteristic of low sediment supply as well as decreased microtopographic variability. At disturbed sites, elevation and microtopography had a weaker influence on biofilm biomass compared to intact estuarine ecosystem sites. Analysis of biofilm patch showed that sites either had a relatively small number of large, contiguous patches, or a large number of smaller, isolated patches; however, less disturbed sites did not necessarily have larger biofilm patches than more disturbed sites. We conclude that UAV-acquired multispectral imagery and co-alignment-based workflow are promising new tools for ecologists to map, monitor, and understand MPB biofilm dynamics in ecologically sensitive estuaries.

Quantifying forest disturbance regimes within caribou (Rangifer tarandus) range in British Columbia.

Habitat disturbance is a major driver of the decline of woodland caribou (Rangifer tarandus caribou) in Canada. Different disturbance agents and regimes negatively impact caribou populations to different degrees. It is therefore critical that land managers and scientists studying caribou have a detailed understanding of the disturbance regimes affecting caribou habitat. In this work we use recent advances in satellite-based disturbance detection to quantify polygonal forest disturbance regimes affecting caribou ecotypes and herds in British Columbia (BC) from 1985 to 2019. Additionally, we utilize this data to investigate harvesting rates since the implementation of the Species at Risk Act (SARA) and publication of recovery strategies for caribou in BC. Southern Mountain caribou herds are the most threatened yet experienced the highest rates of disturbance, with 22.75% of forested habitat within their ranges disturbed during the study period. Over the study period, we found that in total, 16.4% of forested area was disturbed across all caribou herd ranges. Our findings indicate that caribou in BC face high, and in many cases increasing, levels of habitat disturbance. Our results provide a detailed understanding of the polygonal disturbance regimes affecting caribou in BC at the herd scale, and highlight the need for effective implementation of policies aimed at preserving caribou habitat.

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning.

Stream temperatures are influenced by the amount of solar insolation they receive. Increasing stream temperatures associated with climate warming pose detrimental health risks to freshwater ecosystems. In British Columbia (BC), Canada, timber harvesting along forested streams is managed using riparian buffer zones of varying widths and designations. Within buffer zones, depending on distance from the stream, selective thinning may be permitted or harvest may be forbidden. In this study, we used airborne laser scanning (ALS) point cloud data acquired via a remotely piloted aircraft system (RPAS) to derive forest canopy characteristics that were then used to estimate daily incoming summer and fall solar insolation for five stream reaches in coastal conifer-dominated temperate forests in Vancouver Island, BC, Canada. We then examined empirical relationships between estimated insolation and actual instream temperature measurements. Based on these empirical relationships, the potential effects of timber harvest on instream temperatures were simulated by comparing scenarios of different riparian forest harvest intensities. Our results indicated that modeled solar insolation explained 43-90 % of the variation in observed stream reach temperatures, and furthermore, when a single cold-water stream reach was excluded explained an overall 81 % of variation. Simulated harvesting scenarios generally projected increases in maximum stream reach temperatures 1-2 °C in summer and early fall months. However, in a full clearcut scenario (i.e. where all trees were removed), maximum stream reach temperatures increased as much as 5.8 °C. Our results emphasize the importance of retaining riparian vegetation for the maintenance of habitable temperatures for freshwater-reliant fish with thermal restrictions. In addition, we demonstrate the feasibility of RPAS-based monitoring of stream reach shading and canopy cover, enabling detailed assessment of environmental stressors faced by fish populations under climate warming.

Mapping canopy traits over Québec using airborne and spaceborne imaging spectroscopy.

The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. However, to date most imaging spectroscopy studies exploiting satellite spectrometers have been constrained to the landscape scale. In this paper we present a new method to map vegetation traits at the landscape scale and upscale trait maps to the continental level, using historical spaceborne imaging spectroscopy (Hyperion) to derive estimates of leaf mass per area, nitrogen, and carbon concentrations of forests in Québec, Canada. We compare estimates for each species with reference field values and obtain good agreement both at the landscape and continental scales, with patterns consistent with the leaf economic spectrum. By exploiting the Hyperion satellite archive to map these traits and successfully upscale the estimates to the continental scale, we demonstrate the great potential of recent and upcoming spaceborne spectrometers to benefit plant biodiversity monitoring and conservation efforts.

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca).

Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2 = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2 = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.

Evolutionary history explains foliar spectral differences between arbuscular and ectomycorrhizal plant species.

Leaf spectra are integrated foliar phenotypes that capture a range of traits and can provide insight into ecological processes. Leaf traits, and therefore leaf spectra, may reflect belowground processes such as mycorrhizal associations. However, evidence for the relationship between leaf traits and mycorrhizal association is mixed, and few studies account for shared evolutionary history. We conduct partial least squares discriminant analysis to assess the ability of spectra to predict mycorrhizal type. We model the evolution of leaf spectra for 92 vascular plant species and use phylogenetic comparative methods to assess differences in spectral properties between arbuscular mycorrhizal and ectomycorrhizal plant species. Partial least squares discriminant analysis classified spectra by mycorrhizal type with 90% (arbuscular) and 85% (ectomycorrhizal) accuracy. Univariate models of principal components identified multiple spectral optima corresponding with mycorrhizal type due to the close relationship between mycorrhizal type and phylogeny. Importantly, we found that spectra of arbuscular mycorrhizal and ectomycorrhizal species do not statistically differ from each other after accounting for phylogeny. While mycorrhizal type can be predicted from spectra, enabling the use of spectra to identify belowground traits using remote sensing, this is due to evolutionary history and not because of fundamental differences in leaf spectra due to mycorrhizal type.

A novel post-fire method to estimate individual tree crown scorch height and volume using simple RPAS-derived data.

Background: An accurate understanding of wildfire impacts is critical to the success of any post-fire management framework. Fire severity maps are typically created from satellite-derived imagery that are capable of mapping fires across large spatial extents, but cannot detect damage to individual trees. In recent years, higher resolution fire severity maps have been created from orthomosaics collected from remotely piloted aerial systems (RPAS). Digital aerial photogrammetric (DAP) point clouds can be derived from these same systems, allowing for spectral and structural features to be collected concurrently. In this note, a methodology was developed to analyze fire impacts within individual trees using these two synergistic data types. The novel methodology presented here uses RPAS-acquired orthomosaics to classify trees based on a binary presence of fire damage. Crown scorch heights and volumes are then extracted from fire-damaged trees using RPAS-acquired DAP point clouds. Such an analysis allows for crown scorch heights and volumes to be estimated across much broader spatial scales than is possible from field data. Results: There was a distinct difference in the spectral values for burned and unburned trees, which allowed the developed methodology to correctly classify 92.1% of trees as either burned or unburned. Following a correct classification, the crown scorch heights of burned trees were extracted at high accuracies that when regressed against field-measured heights yielded a slope of 0.85, an R-squared value of 0.78, and an RMSE value of 2.2 m. When converted to crown volume scorched, 83.3% of the DAP-derived values were within ± 10% of field-measured values. Conclusion: This research presents a novel post-fire methodology that utilizes cost-effective RPAS-acquired data to accurately characterize individual tree-level fire severity through an estimation of crown scorch heights and volumes. Though the results were favorable, improvements can be made. Specifically, through the addition of processing steps that would remove shadows and better calibrate the spectral data used in this study. Additionally, the utility of this approach would be made more apparent through a detailed cost analysis comparing these methods with more conventional field-based approaches.

Antecedentes: Un preciso entendimiento de los impactos de los incendios de vegetación es crítico para el éxito de cualquier esquema de manejo posterior. Los mapas de severidad del fuego son creados típicamente desde imágenes derivadas de satélites, siendo capaces de mapear incendios de extensiones espaciales muy grandes, pero no pueden detectar daños en árboles individuales. En años recientes, los mapas de severidad de más alta resolución han sido creados desde ortomosaicos colectados desde sistemas aéreos pilotados de manera remota (RPAS). Los puntos de nubes fotogramétricas digitales pueden ser derivados de esos mismos sistemas, permitiendo que las características espectrales y estructurales sean colectados de manera conjunta. En esta nota, fue desarrollada una metodología para analizar los impactos de los incendios en árboles individuales usando dos tipos de datos sinérgicos. La metodología novedosa aquí presentada usa ortomosaicos adquiridos mediante RPAS para clasificar árboles basados en la presencia binaria de daños por fuego. Las alturas y volúmenes del chamuscado de la corona son luego extractados de árboles dañados por el fuego usando datos de DAP de la nube adquiridos mediante RPAS. Este tipo de análisis para altura y volumen del chamuscado de la corona permite su estimación a través de una escala espacial mucho más amplia de lo que es posible mediante datos de campo. Resultados: Hay dos diferencias marcadas en los valores espectrales para árboles quemados y no quemados, lo que permite a la metodología desarrollada clasificar correctamente el 92,1% de los árboles como quemados o no quemados. Siguiendo una correcta clasificación, la altura de las coronas chamuscadas de los árboles quemados fueron extraídas con una alta exactitud, que cuando se realizó la regresión con datos de altura medidos a campo dio una pendiente de 0,85, un R2 de 0,78 y un valor de RMSE de 2,2 m. Cuando fueron convertidos a volumen de corona chamuscada, el 83,3% de los valores de DAP derivados estuvieron dentro de ± 10% de los valores medidos a campo. Conclusiones: Esta investigación presenta una metodología post fuego novedosa y costo-efectiva que utiliza datos adquiridos mediante RPAS para caracterizar la severidad del fuego a nivel de árboles individuales a través de una estimación del chamuscado del volumen y altura de la corona. Aunque estos resultados fueron aceptables, algunos mejoramientos pueden ser realizados. Específicamente, a través de la adición de procesos escalonados que podrían remover las sombras y calibrar mejor los datos espectrales usados en este estudio. Adicionalmente, la utilidad de esta aproximación puede hacerse más aparente a través de un detallado análisis de costos, comparando este método con las aproximaciones más convencionales que usan datos de campo.

Predicting leaf traits across functional groups using reflectance spectroscopy.

Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2  = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance.

Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software's over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.

Measuring industrial lumber production using nighttime lights: A focus study on lumber mills in British Columbia, Canada.

Nighttime lights (NTL) are the procurement of remotely sensed artificial illumination from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite. NTL provides a unique perspective on anthropogenic activity by characterizing spatial and temporal patterns related to economic trends and human development. In this study, we assess the ability of NTL to characterize trends associated with industrial lumber production in British Columbia, Canada. We establish the presence of a logarithmic relationship between NTL and lumber mill production capacity (R2 = 0.69-0.82). The ability of NTL to temporally identify mill closures is then demonstrated by differentiating pairs of active and closed mills. We also identify Granger causality and co-integration between NTL and monthly lumber production, highlighting the predictive capability of NTL to forecast production. We then utilize this relationship to build linear regression models that utilize NTL data to estimate monthly (R2 = 0.33), quarterly (R2 = 0.58), and annual (R2 = 0.90) lumber production without reported data.

Effects of climate change on forest plantation productivity in Chile.

Forest plantations in Chile occupy more than 2.2 million ha and are responsible for 2.1% of the GDP of the country's economy. The ability to accurately predictions of plantations productivity under current and future climate has an impact can enhance on forest management and industrial wood production. The use of process-based models to predict forest growth has been instrumental in improving the understanding and quantifying the effects of climate variability, climate change, and the impact of atmospheric CO2 concentration and management practices on forest growth. This study uses the 3-PG model to predict future forest productivity Eucalyptus globulus and Pinus radiata. The study integrates climate data from global circulation models used in CMIP5 for scenarios RCP26 and RCP85, digital soil maps for physical and chemical variables. Temporal and spatial tree growth inventories were used to compare with the 3-PG predictions. The results indicated that forest productivity is predicted to potentially increase stand volume (SV) over the next 50 years by 26% and 24% for the RCP26 scenario and between 73% and 62% for the RCP85 scenario for E. globulus and P. radiata, respectively. The predicted increases can be explained by a combination of higher level of atmospheric CO2 , air temperatures closer to optimum than current, and increases in tree water use efficiency. If the effect of CO2 is not considered, the predicted differences of SV for 2070 are 16% and 14% for the RCP26 scenario and 22% and 14% for RCP85 for the two species. While shifts in climate and increasing CO2 are likely to benefit promote higher productivity, other factors such as lack insufficient availability of soil nutrients, events such as increasing frequency and duration of droughts, longer periods of extreme temperatures, competing vegetation, and occurrence of new pests and diseases may compromise these potential gains.

Generating Douglas-fir Breeding Value Estimates Using Airborne Laser Scanning Derived Height and Crown Metrics.

Progeny test trials in British Columbia are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of parent trees and their offspring. Accurate and timely collection of phenotypic data is critical for estimating BVs with confidence. Airborne Laser Scanning (ALS) data have been used to measure tree height and structure across a wide range of species, ages and environments globally. Here, we analyzed a Coastal Douglas-fir [Pseudotsuga menziesii var. menziesii (Mirb.)] progeny test trial located in British Columbia, Canada, using individual tree high-density Airborne Laser Scanning (ALS) metrics and traditional ground-based phenotypic observations. Narrow-sense heritability, genetic correlations, and BVs were estimated using pedigree-based single and multi-trait linear models for 43 traits. Comparisons of genetic parameter estimates between ALS metrics and traditional ground-based measures and single- and multi-trait models were conducted based on the accuracy and precision of the estimates. BVs were estimated for two ALS models (ALSCAN and ALSACC) representing two model-building approaches and compared to a baseline model using field-measured traits. The ALSCAN model used metrics reflecting aspects of vertical distribution of biomass within trees, while ALSACC represented the most statistically accurate model. We report that the accuracy of both the ALSCAN (0.8239) and ALSACC (0.8254) model-derived BVs for mature tree height is a suitable proxy for ground-based mature tree height BVs (0.8316). Given the cost efficiency of ALS, forest geneticists should explore this technology as a viable tool to increase breeding programs' overall efficiency and cost savings.

Characterizing, mapping and valuing the demand for forest recreation using crowdsourced social media data.

Mapping and valuing of forest recreation is time-consuming and complex, hampering its inclusion in forest management plans and hence the achievement of a fully sustainable forest management. In this study, we explore the potential of crowdsourced social media data in tackling the mapping and valuing of forest recreation demand. To do so, we assess the relationships between crowdsourced social media data, acquired from over 350,000 Flickr geotagged pictures, and demand for forest recreation in British Columbia (BC) forests. We first identify temporal and spatial trends of forest recreation demand, as well as the countries of origin of BC forests visitors. Second, we estimate the average number of annual recreational visits with a linear regression model calibrated with empirically collected secondary data. Lastly, we estimate recreational values by deriving the average consumer surpluses for the visitors of BC forested provincial parks. We find that annually, on average, over 44 million recreational experiences are completed in BC forests, with peaks during the summer months and during the weekends. Moreover, a crowdsourced travel cost approach allowed us to value the recreational ecosystem service in five forested provincial parks ranging from ~2.9 to ~35.0 million CAN$/year. Our findings demonstrate that social media data can be used to characterize, quantify and map the demand for forest recreation (especially in peri-urban forests), representing a useful tool for the inclusion of recreational values in forest management. Finally, we address the limitations of crowdsourced social media data in the study of forest recreation and the future perspectives of this rapidly growing research field.

Assessing representation of remote sensing derived forest structure and land cover across a network of protected areas.

Protected areas (PA) are an effective means of conserving biodiversity and protecting suites of valuable ecosystem services. Currently, many nations and international governments use proportional area protected as a critical metric for assessing progress towards biodiversity conservation. However, the areal and other common metrics do not assess the effectiveness of PA networks, nor do they assess how representative PA are of the ecosystems they aim to protect. Topography, stand structure, and land cover are all key drivers of biodiversity within forest environments, and are well-suited as indicators to assess the representation of PA. Here, we examine the PA network in British Columbia, Canada, through drivers derived from freely-available data and remote sensing products across the provincial biogeoclimatic ecosystem classification system. We examine biases in the PA network by elevation, forest disturbances, and forest structural attributes, including height, cover, and biomass by comparing a random sample of protected and unprotected pixels. Results indicate that PA are commonly biased towards high-elevation and alpine land covers, and that forest structural attributes of the park network are often significantly different in protected versus unprotected areas (426 out of 496 forest structural attributes found to be different; p < 0.01). Analysis of forest structural attributes suggests that establishing additional PA could ensure representation of various forest structure regimes across British Columbia's ecosystems. We conclude that these approaches using free and open remote sensing data are highly transferable and can be accomplished using consistent datasets to assess PA representations globally.

Protein biomarkers in serum as a conservation tool to assess reproduction: a case study on brown bears (Ursus arctos).

Monitoring the reproductive characteristics of a species can complement existing conservation strategies by understanding the mechanisms underlying demography. However, methodology to determine important aspects of female reproductive biology is often absent in monitoring programs for large mammals. Protein biomarkers may be a useful tool to detect physiological changes that are indicative of reproductive state. This study aimed to identify protein biomarkers of reproductive status in serum collected from free-ranging female brown bears (Ursus arctos) in Alberta, Canada, from 2001 to 2018. We hypothesized that the expression of proteins related to reproduction in addition to energetics and stress can be used to answer specific management-focused questions: (i) identify when a female is pregnant, (ii) detect if a female is lactating, (iii) determine age of sexual maturity (i.e. primiparity) and (iv) assess female fertility (i.e. reproduction rate). Furthermore, we investigated if silver spoon effects (favourable early life conditions provide fitness benefits through adulthood) could be determined using protein expression. A target panel of 19 proteins with established relationships to physiological function was measured by peptide-based analysis using liquid chromatography and multiple reaction monitoring mass spectrometry and their differential expression was evaluated using a Wilcoxon signed-rank test. We found biomarkers of pregnancy (apolipoprotein B-100 and afamin), lactation (apolipoprotein B-100 and alpha-2-macroglobulin) and sexual maturity (corticosteroid-binding globulin), but there were no statistically significant relationships with protein expression and fertility. The expression of proteins related to reproduction (afamin) and energetics (vitamin-D binding protein) was associated with the nutritional quality of the individual's present habitat rather than their early life habitat. This study highlights potential biomarkers of reproductive status and provides additional methods for monitoring physiological function in wildlife to inform conservation.