Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

A new view on EU agricultural landscapes: Quantifying patchiness to assess farmland heterogeneity

Mapping and assessment of ecosystem services in agricultural landscapes as required by the EU biodiversity policy need a better characterization of the given landscape typology according to its ecological and cultural values. Such need should be accommodated by a better discrimination of the landscape characteristics linked to the capacity of providing ecosystem services and socio-cultural benefits. Often, these key variables depend on the degree of farmland heterogeneity and landscape patterns. We employed segmentation and landscape metrics (edge density and image texture respectively), derived from a pan-European multi-temporal and multi-spectral remote sensing dataset, to generate a consistent European indicator of farmland heterogeneity, the Farmland Heterogeneity Indicator (FHI). We mapped five degrees of FHI on a wall-to-wall basis (250 m spatial resolution) over European agricultural landscapes including natural grasslands. Image texture led to a clear improvement of the indicator compared to the pure application of Edge Density, in particular to a better detection of small patches. In addition to deriving a qualitative indicator we attributed an approximate patch size to each class, allowing an indicative assessment of European field sizes. Based on CORINE land cover, we identified pastures and heterogeneous land cover classes as classes with the highest degree of FHI, while agroforestry and olive groves appeared less heterogeneous on average. We performed a verification based on a continental and regional scale, which resulted in general good agreement with independently derived data.     

Effect of landscape composition and arrangement on biological control agents in a simplified agricultural system: A cost–distance approach

Landscape simplification has been clearly demonstrated to have negative impacts on the in-crop density and biological-control activity of natural enemies in agricultural landscapes. The role of spatial arrangement of the landscape, however, has not been investigated in agroecosystems. We applied cost–distance modeling to investigate the relationship between the in-crop density of natural enemies and the structural connectivity of non-crop land uses surrounding crops within Australian cotton landscapes. We further compared the explanatory power of this approach with the more commonly used spatially specific proportional-area approach, which considers landscape composition in terms of the proportional area of a given land use within a given radius. Cost–distance metrics offered a more significant explanation of in-crop density for the predatory beetle Dicranolaius bellulus (Coleoptera: Melyridae) than did the proportional-area approach. The in-crop density for this species was positively and significantly correlated with the connectivity of wooded land uses within a 3000 m radius. However, for natural enemy taxa that responded to landscape characteristics at smaller spatial scales (within a 750 m radius), namely Oxyopes spp. (Araneae: Oxyopidae) and Trichogramma spp., (Hymenoptera: Trichogrammatidae), the proportional-area approach gave a more significant explanation of in-crop density. Herbivore taxa responded weakly to proportional area at all scales and showed no correlation to cost–distance metrics. Findings indicate potential for simplified agricultural landscapes to be ‘selectively’ manipulated to enhance colonization of the crop by natural enemies, but not herbivores, by improving connectivity between crops and non-crop resources, through the presence of woody vegetation.     

The effect of land type diversity and spatial heterogeneity on farmland birds in Norway

The decline in farmland birds observed throughout Europe during recent decades has attracted much attention. Agricultural intensification or land abandonment are commonly forwarded as key drivers. Several countries have established agri-environmental schemes (AES) to counter these negative trends among farmland birds. This paper reports a study of the relationship between land use and bird species in the agricultural landscape of Norway. The main objective was to investigate the effect of spatial heterogeneity and diversity of land use on total richness and abundance of farmland birds at a national level.Monitoring the distribution and abundance of birds is part of the Norwegian monitoring programme for agricultural landscapes. The monitoring programme is based on mapping of 1 × 1 km squares distributed across the entire agricultural landscape. Within these squares permanent observation points are established for bird monitoring. Detailed interpretation of aerial photographs provides the land classification. We tested the relationship between landscape metrics at different levels of land type detail and species richness and abundance of farmland and non-farmland birds.There was a positive relationship between species richness and abundance of farmland birds and agricultural area. For non-farmland birds the relationship was negative. Spatial heterogeneity of land use was a significant positive factor for both farmland and non-farmland species. High land type diversity was positive for farmland bird richness, but negative for abundance. Non-farmland bird richness was not affected by land type diversity, but abundance had a negative response.The results presented in this paper highlight the importance of a spatial heterogeneous landscape. However, we also found that land type diversity could negatively affect the abundance of both farmland and non-farmland birds. Our findings suggest a need for different management approaches depending on whether the aim is increased species richness or abundance. Achieving both aims with the same means might be difficult. We thus suggest a need for land use analyses before proper management strategies can be implemented.     

Validating Landsat-based landscape metrics with fine-grained land cover data

ContextModerate-grained data may not always represent landscape structure in adequate detail which could cause misleading results. Certain metrics have been shown to be predictable with changes in scale; however, no studies have verified such predictions using independent fine-grained data.ObjectivesOur objective was to use independently derived land cover datasets to assess relationships between metrics based on fine- and moderate-grained data for a range of analysis extents. We focus on metrics that previous literature has shown to have predictable relationships across scales.MethodsThe study area was located in eastern Connecticut. We compared a 1 m land cover dataset to a 30 m resampled dataset, derived from the 1 m data, as well as two Landsat-based datasets. We examined 11 metrics which included cover areas and patch metrics. Metrics were analyzed using analysis extents ranging from 100 to 1400 m in radius.ResultsThe resampled data had very strong linear relationships to the 1 m data, from which it was derived, for all metrics regardless of the analysis extent size. Landsat-based data had strong correlations for most cover area metrics but had little or no correlation for patch metrics. Increasing analysis areas improved correlations.ConclusionsRelationships between coarse- and fine-grained data tend to be much weaker when comparing independent land cover datasets. Thus, trends across scales that are found by resampling land cover are likely to be unsuitable for predicting the effects of finer-scale elements in the landscape. Nevertheless, coarser data shows promise in predicting fine-grained for cover area metrics provided the analysis area used is sufficiently large.     

Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Chesapeake Bay

The aim of this study was to determine the effects of catchment and riparian stream buffer-wide urban and non-urban land cover/land use (LC/LU) on total nitrogen (TN) and total phosphorus (TP) runoff to the Chesapeake Bay. The effects of the composition and configuration of LC/LU patches were explored in particular. A hybrid-statistical-process model, the SPAtially Referenced Regression On Watershed attributes (SPARROW), was calibrated with year 1997 watershed-wide, average annual TN and TP discharges to Chesapeake Bay. Two variables were predicted: (1) yield per unit watershed area and (2) mass delivered to the upper estuary. The 166,534 km² watershed was divided into 2339 catchments averaging 71 km². LC/LU was described using 16 classes applied to both the catchments and also to riparian stream buffers alone. Seven distinct landscape metrics were evaluated. In all, 167 (TN) and 168 (TP) LC/LU class metric combinations were tested in each model calibration run. Runs were made with LC/LU in six fixed riparian buffer widths (31, 62, 125, 250, 500, and 1000 meters (m)) and entire catchments. The significance of the non-point source type (land cover, manure and fertilizer application, and atmospheric deposition) and factors affecting land-to-water delivery (physiographic province and natural or artificial land surfaces) was assessed. The model with a 31 m riparian stream buffer width accounted for the highest variance of mean annual TN (r² = 0.9366) and TP (r² = 0.7503) yield (mass for a specified time normalized by drainage area). TN and TP loadings (mass for a specified time) entering the Chesapeake Bay were estimated to be 1.449 × 10⁸ and 5.367 × 10⁶ kg/yr, respectively. Five of the 167 TN and three of the 168 TP landscape metrics were shown to be significant (p-value ≤ 0.05) either for non-point sources or land-to-water delivery variables. This is the first demonstration of the significance of riparian LC/LU and landscape metrics on water quality simulation in a watershed as large as the Chesapeake Bay. Land cover metrics can therefore be expected to improve the precision of estimated TN and TP annual loadings to the Chesapeake Bay and may also suggest changes in land management that may be beneficial in control of nutrient runoff to the Chesapeake Bay and similar watersheds elsewhere.     

Impact of landscape pattern at multiple spatial scales on water quality: A case study in a typical urbanised watershed in China

Buffer zones along rivers and streams can provide water quality services by filtering nutrients, sediment and other contaminants from the surface. Redundancy analysis was used to determine the influence of the landscape pattern at the entire catchment scale and at multiple buffer zone scales (100 m, 300 m, 500 m, 1000 m and 1500 m) on the water quality in a highly urbanised watershed. Change-point analysis was further applied to estimate the specific locations along a gradient of landscape metric that result in a sudden change in the water quality variable. The landscape characteristics for 100 m buffer zones appeared to have a slightly greater influence on the water quality than the entire catchment. The patch density of urban land and the large patch index of water were recognised as the dominant variables influencing the water quality for a 100 m buffer zone. The result of change-point analysis indicated key interval values of the two landscape metrics within the 100 m buffer zone. When the patch density of urban land was >30–40 n/100 ha and the largest patch index of water was >2.5–3.5%, the watershed water quality appeared to be better protected.     

Spatial metrics for detecting ecosystem degradation in the ridge-slough patterned landscape

Indicators of landscape condition should be selected based on their sensitivity to environmental changes and their capacity to provide early warning detection of those changes. We assessed the performance of a suite of spatial-pattern metrics selected to quantify the condition of the ridge-slough landscape in the Everglades (South Florida, USA). Spatial pattern metrics (n = 14) that describe landscape composition, geometry and hydrologic connectivity were enumerated from vegetation maps of twenty-five 2 × 2 km primary sampling units (PSUs) that span a gradient of hydrologic and ecological condition across the greater Everglades ecosystem. Metrics were assessed in comparison with field measurements from each PSU of landscape condition obtained from regional surveys of soil elevation, which have previously been shown to capture dramatic differences between conserved and degraded locations. Elevation-based measures of landscape condition included soil elevation bi-modality (BI_SE), a binary measure of landscape condition, and also the standard deviation of soil elevation (SD_SE), a continuous measure of condition. Metric performance was assessed based on the strength (sensitivity) and shape (leading vs. lagging) of the relationship between spatial pattern metrics and these elevation-based measures. We observed significant logistic regression slopes with BI_SE for only 4 metrics (slough width, ridge density, directional connectivity index – DCI, and least flow cost – LFC). More significant relationships (n = 8 metrics) were observed with SD_SE, with the strongest associations for slough density, mean ridge width, and the average length of straight flow, as well as for a suite of hydrologic connectivity metrics (DCI, LFC and landscape discharge competence – LDC). Leading vs. lagging performance, inferred from the curvature of the association obtained from the exponent of fitted power functions, suggest that only DCI was a leading metric of the loss of soil elevation variation; most metrics were indeterminate, though some were clearly lagging. Our findings support the contention that soil elevation changes from altered peat accretion dynamics precede changes in landscape pattern, and offer insights that will enable efficient monitoring of the ridge-slough landscape as part of the ongoing Everglades restoration effort.     

Acoustic patterns at the Samford Ecological Research Facility in South East Queensland,Australia: The Peri-Urban SuperSite of the Terrestrial Ecosystem Research Network

Acoustic signals that emanate from ecosystems are an important ecological variable which can provide evidence of current ecological condition as well as ecological change over time. The Terrestrial Ecosystem Research Network (TERN) established protocols to record sounds in ten SuperSites distributed throughout Australia with the objective of characterizing the soundscape in a representative landscape in different regions of Australia. This acoustic monitoring system enables a comparison of the soundscapes within and between Australian regions to determine similarities and differences in these landscapes and regions.This research quantifies the soundscape patterns in one of these SuperSites, Samford Ecological Research Facility (TERN-SERF), which is part of the South-East Queensland Peri-Urban SuperSite. An analysis and visualization of patterns in the soundscape was conducted using a continuous acoustic recording collected at TERN-SERF. The recording was made using a Song Meter (SM2) in a representative wooded habitat at TERN-SERF from 1 August to 30 September 2013. The recording was made in 16-bit stereo at 44 kHz and stored in wav file format. The recording was split into 1-minute-long recordings comprising 86,196 records and then sub-sampled at a 30-minute interval, providing 2878 one-minute-long recordings every 1/2 h. Soundscape metrics were computed for each of the two recording intervals. Soundscape power values were computed for each of ten frequency intervals (1–11 kHz) for both the 1-minute and the 30-minute interval recordings. In addition, six acoustic indices were computed from each recording.The acoustics metrics derived from the two sets of recordings (1-minute and 30-minute recording intervals) were examined to determine if they revealed different patterns. Several soundscape metrics were calculated for each recording including ten soundscape power values at 1 kHz frequency intervals and six acoustics indices. The soundscape shows a dynamic but consistent pattern over time of day during the monitoring period, depending on the metric examined. The metrics revealed different soundscape patterns. All soundscape power values at 1 kHz frequency intervals defined the dawn and dusk chorus, some more distinctly than others. Three of six acoustic indices also changed abruptly at the dawn chorus. No significant difference was found when soundscape metrics were compared between the 1-minute (high resolution) and 30-minute (lower resolution) recording intervals. A t-test was used to compare the mean values of ten soundscape power frequency intervals (p = 0.44) and the mean values of six acoustics indices (p = 0.41).Sounds were identified in 180 recordings made at 0530 h, 0600 h and 0630 h in the 1-minute long 30-minute interval recordings each day during the recording period (August and September). Sixty-seven species of birds were identified. Soundscape metrics were correlated with avian species counts and calls by all species using a correlation threshold of r > 0.7. This analysis revealed that soundscape power at the frequency interval 3–4 kHz was correlated with both the number of species (r = − 0.927) and total calls (r = − 0.996) over the three time periods. Three indices, the ADI (r = 0.953, r = 0.709), the AEI (r = 0.978, r = 0.774) and (H) (r = 0.795, r = 0.985) were similarly correlated as was an index derived soundscape power, the Shannon-Weaver Index (r = − 0.997, r = − 0.849). Other indices were correlated (r > 0.7) with only the number of avian species or only the number of calls.This methodology establishes an analysis protocol for analyzing large acoustic data sets, and demonstrates the effectiveness of using acoustic metrics for summarizing and interpreting long-term recordings.     

An operational method to assess impacts of land clearing on terrestrial biodiversity

We developed a methodology to objectively and transparently assess the impacts on terrestrial biodiversity of proposals to clear native vegetation in New South Wales (NSW), Australia. The methodology was developed to underpin a policy to permit land clearing only where it ‘improves or maintains environmental outcomes’. It was developed in the following steps: (1) operational requirements and resource constraints were defined. (2) Biodiversity surrogates and assessment techniques that matched these requirements and constraints were identified. (3) Sites were assessed locally, but also in the broader landscape, regional and national contexts. (4) Explicit rules and metrics were developed to facilitate transparent and consistent assessments. (5) These rules, metrics and the data that underpinned them were codified into a simple computer software tool. The tool did not permit clearing in vegetation communities or landscapes that were already over-cleared or listed as threatened, unless the vegetation was in ‘low condition’ (unlikely to persist in the long-term). Other native vegetation could be cleared if regional, landscape and site impacts could be offset. In the first year after the assessment methodology was implemented a net area of approximately 187 ha of native vegetation was approved for clearing with offsets. Most approvals (68%) were for proposals to clear native vegetation with a low likelihood of persistence under the existing land use (predominantly scattered trees among cultivation) and offset these impacts by improving the condition and likelihood of persistence of native vegetation in comparable ecosystems. Remaining approvals were for clearing relatively small areas (mean = 0.6 ha) of partially modified native vegetation. Proposals to offset the impacts of clearing substantially intact native vegetation or larger areas of partially modified native vegetation were generally assessed as unlikely to ‘improve or maintain environmental outcomes’.     

On using landscape metrics for landscape similarity search

Landscape similarity search involves finding landscapes from among a large collection that are similar to a query landscape. An example of such collection is a large land cover map subdivided into a grid of smaller local landscapes, a query is a local landscape of interest, and the task is to find other local landscapes within a map which are perceptually similar to the query. Landscape search and the related task of pattern-based regionalization, requires a measure of similarity – a function which quantifies the level of likeness between two landscapes. The standard approach is to use the Euclidean distance between vectors of landscape metrics derived from the two landscapes, but no in-depth analysis of this approach has been conducted. In this paper we investigate the performance of different implementations of the standard similarity measure. Five different implementations are tested against each other and against a control similarity measure based on histograms of class co-occurrence features and the Jensen–Shannon divergence. Testing consists of a series of numerical experiments combined with visual assessments on a set of 400 3 km-scale landscapes. Based on the cases where visual assessment provides definitive answer, we have determined that the standard similarity measure is sensitive to the way landscape metrics are normalized and, additionally, to whether weights aimed at controlling the relative contribution of landscape composition vs. configuration are used. The standard measure achieves the best performance when metrics are normalized using their extreme values extracted from all possible landscapes, not just the landscapes in the given collection, and when weights are assigned so the combined influence of composition metrics on the similarity value equals the combined influence of configuration metrics. We have also determined that the control similarity measure outperforms all implementations of the standard measure.     

Loss of aboveground forest biomass and landscape biomass variability in Missouri,US

Disturbance regimes and forests have changed over time in the eastern United States. We examined effects of historical disturbance (circa 1813 to 1850) compared to current disturbance (circa 2004 to 2008) on aboveground, live tree biomass (for trees with diameters ≥13 cm) and landscape variation of biomass in forests of the Ozarks and Plains landscapes in Missouri, USA. We simulated 10,000 one-hectare plots using random diameters generated from parameters of diameter distributions limited to diameters ≥13 cm and random densities generated from density estimates. Area-weighted mean biomass density (Mg/ha) for historical forests averaged 116 Mg/ha, ranging from 54 Mg/ha to 357 Mg/ha by small scale ecological subsections within Missouri landscapes. Area-weighted mean biomass density for current forests averaged 82 Mg/ha, ranging from 66 Mg/ha to 144 Mg/ha by ecological subsection for currently forested land. Biomass density of current forest was greater than historical biomass density for only 2 of 23 ecological subsections. Current carbon sequestration of 292 TgC on 7 million ha of forested land is less than half of the estimated historical total carbon sequestration of 693 TgC on 12 million ha. Cumulative tree cutting disturbances over time have produced forests that have less aboveground tree biomass and are uniform in biomass compared to estimates of historical biomass, which varied across Missouri landscapes. With continued relatively low rates of forest disturbance, current biomass per ha will likely increase to historical levels as the most competitive trees become larger in size and mean number of trees per ha decreases due to competition and self-thinning. Restoration of large diameter structure and forested extent of upland woodlands and floodplain forests could fulfill multiple conservation objectives, including carbon sequestration.     

Mapping changes to vegetation pattern in a restoring wetland: Finding pattern metrics that are consistent across spatial scale and time

Tidal salt marshes in the San Francisco Estuary region display heterogeneous vegetation patterns that influence wetland function and provide adequate habitat for native or endangered wildlife. In addition to analyzing the extent of vegetation, monitoring the dynamics of vegetation pattern within restoring wetlands can offer valuable information about the restoration process. Pattern metrics, derived from classified remotely sensed imagery, have been used to measure composition and configuration of patches and landscapes, but they can be unpredictable across scales, and inconsistent across time. We sought to identify pattern metrics that are consistent across spatial scale and time – and thus robust measures of vegetation and habitat configuration – for a restored tidal marsh in the San Francisco Bay, CA, USA. We used high-resolution (20 cm) remotely sensed color infrared imagery to map vegetation pattern over 2 years, and performed a multi-scale analysis of derived vegetation pattern metrics. We looked at the influence on metrics of changes in grain size through resampling and changes in minimum mapping unit (MMU) through smoothing. We examined composition, complexity, connectivity and heterogeneity metrics, focusing on perennial pickleweed (Sarcocornia pacifica), a dominant marsh plant. At our site, pickleweed patches grew larger, more irregularly shaped, and closely spaced over time, while the overall landscape became more diverse. Of the two scale factors examined, grain size was more consistent than MMU in terms of identifying relative change in composition and configuration of wetland marsh vegetation over time. Most metrics exhibited unstable behavior with larger MMUs. With small MMUs, most metrics were consistent across grain sizes, from fine (e.g. 0.16 m²) to relatively large (e.g. 16 m²) pixel sizes. Scale relationships were more variable at the landcover class level than at the landscape level (across all classes). This information may be useful to applied restoration practitioners, and adds to our general understanding of vegetation change in a restoring marsh.     

Fragmentation in the Legal Amazon,Brazil: Can landscape metrics indicate agricultural policy differences?

Large-scale land conversion for agriculture in Brazilian Amazonia is occurring at persistently high rates. Basin-wide net land use and land cover changes imply substantially different situations between distinct regions and states due to different agricultural policies. This research used eight landscape metrics to quantify and investigate the spatial patterns of cattle pasture and cropland throughout the states of Pará, Mato Grosso, Rondônia, and Amazonas. These metrics were patch density (DEN), mean patch size (MPS), largest patch index (LPI), mean edge density (MED), mean twist number (TWI), corrected perimeter-to-area ratio (CPA), fractal dimension (FDI), and fragmentation index (FRG). A total of 1852 patches were analyzed, originating from 86 samples in 71 different plots, covering a total of 177,500 km² throughout all four states.Principal component analysis showed a partial overlap in the spatial pattern of agricultural patches between all states. The largest percentage of variance was explained by patch area metrics, which can be related to the different approaches in agricultural policies, but no clear division between the states was identified in this dimension. The metrics quantifying patch shape were de facto independent of deforestation area, and related to the second principal component axis. Although some overlap in this dimension was present as well, these metrics proved a possible measure for discerning the patterns of agriculture attached to a certain state. Different land use policies are hypothesized to lead to more heterogeneity in landscape patterns in an early stage, yet the increasing influence of both cropland and pasture agriculture eventually leads to more uniform landscapes in which spatial differences gradually disappear.     

Changes and interactions between forest landscape connectivity and burnt area in Spain

The spatial structure, functionality and dynamics of forest landscapes in peninsular Spain and the Balearic Islands were compared over the last five decades. Two particular features were studied in the sample sites: forest connectivity for wildlife and areas burnt by wildfires. 191 Squares, each 4 km × 4 km, were selected from the SISPARES (the monitoring framework designed to evaluate the trends in the structure of Spanish rural landscapes) environmental strata. Aerial photographs from 1956, 1984, 1998 and 2008 were interpreted and 11 land cover categories mapped and checked in the field, using a minimum mapping area of one hectare. The Equivalent Connected Area Index was used to assess forest connectivity over the sampling period. Social and economical factors were assessed using indicators of farm intensiveness. The Spanish forest connectivity has improved in the last five decades although two different trends can be identified: the first 40 years are characterized by positive rates of growth whereas the 10 last years are characterized by their stability. Nevertheless the area of burnt land was higher along the first 25 studied years and decreased significantly over the last decade.Our results show the climate is the main driver in the evolution of forest connectivity and burnt area in the forest landscapes, playing a direct role on forest biomass production and wildfire ignition and propagation, as well as an indirect role by keeping vertical and horizontal forest continuity through the landscape spatial pattern. Social and economic factors are very important drivers as well: Rural population density and farm size average have been tested as good indicators of landscape artificiality, highly correlated to wildfire hazard and forest connectivity.Finally, we have pointed out the evolutionary path followed by SISPARES framework as a tool for monitoring rural landscapes. It emphasises on the requirement of a 30 years time window for building-up reliable dynamic multifunctional model.     

Regional nutrient thresholds in wadeable streams of New York State protective of aquatic life

Human influence on the landscape has caused nutrients in surface waters to increase to the point where their presence has substantially altered biological communities. Because this is a nationally recognized problem, the United States Environmental Protection Agency (USEPA) tasked each state, tribe, and territory to adopt numeric nutrient criteria. Here we integrate the concept of ecological thresholds with the derivation of effects-based numeric nutrient criteria. Acceptable levels of risk exceeding predefined biocriteria were determined using conditional probability and nonparametric changepoint analysis. We show how certain community metrics exhibit threshold responses to nutrients. Using these thresholds, we suggest nutrient values protective of aquatic life and characterize community composition. Nutrient criteria were suggested for two aggregations of USEPA's nutrient ecoregions in New York State an upland pristine forested region (Ecoregions VIII and XI) and a nutrient-enriched lowland region (Ecoregions VII and XIV). Of 11 biological community metrics evaluated, 5 had a strong response to nutrients (NBI-P, NBI-N, HBI, TRI, and DMA). Maximum probabilities of exceeding the biological impairment thresholds established for these metrics ranged from 81% to 100%. Changepoint analysis conducted on probability outcomes of these metrics resulted in nutrient thresholds at or above USEPA nutrient guidance values, depending on ecoregion and nutrient variable (Ecoregion VIII/XI: 15 μg/L TP, 472 μg/L TN, 150 μg/L NO₃-N, Ecoregion VII/XIV: 17 μg/L TP, 1133 μg/L TN, 356 μg/L NO₃-N). Results of taxonomic similarity percentages (SIMPER) and species contributions indicate that several orders of macroinvertebrates and diatoms exhibit significant shifts in their percent of contributions to sample similarity in response to changes in nutrient concentrations.     

A review of indicators of estuarine tidal wetland condition

This review critically evaluates indicators of tidal wetland condition based on 36 indicator development studies and indicators developed as part of U.S. state tidal wetland monitoring programs. Individual metrics were evaluated based on relative scores on two sets of evaluation factors. A rigor score evaluated metric development based on conceptual relevance, indicator development method, degree of independent validation, and temporal and spatial extent tested. An applicability score evaluated metrics based on cost of data collection, probable spatial extent of applicability, technical complexity, and indicator responsiveness. The majority of indicators could be classified as biotic condition indicators (81%), with vegetation (37%) and macroinvertebrate (28%) metrics composing the largest proportion. Most metrics provided a conceptual model or scientific justification (97%), were developed by correlation to environmental gradients (46%), were tested over multiple seasons or years (49%) and at multiple sites (88%). Few were independently validated (18%). Average rigor score was 10 (on a scale of 0–25) and ranged between 1 and 21. Highest rigor scores were for trematode community metrics (community similarity index, species richness) and metrics of grass shrimp (Palaemonetes pugio) individuals (gene expression, relative fecundity, embryo hatching success, larval survival). Most metrics had a high cost of data collection (63%), required field and laboratory processing (84%), would be applicable across the U.S. (72%), and were responsive to the variable of interest (44%). Mean applicability score was 4.9 (range: 2–8). Highest scores were found for metrics that only required field collection of data using simple or no instrumentation. Lowest scoring metrics required expensive equipment, specialized taxonomic knowledge, complex laboratory analysis, and/or culturing of organisms. Scores for individual metrics were grouped by indicator, then averaged and rescaled between 0 and 100 to provide a composite evaluation of the indicator they measured. Among major indicator types, biotic indicators had the highest rigor scores (mean = 44, range 20–79), followed by indicators of chemical/physical characteristics (mean = 36, range 16–56), landscape condition (mean = 31, range 24–37), and hydrology/geomorphology indicators (mean = 21, range 4–52). In contrast, biotic indicators scored lowest for applicability (mean = 58, range 25–100) and indicators of landscape condition scored highest. The results of this review suggest that the development and selection of tidal wetland indicators could be vastly improved by employing a standardized development methodology that provides uniform information about each indicator. In addition, tidal wetland indicator research should focus on the development of indicators of ecological processes and disturbance regimes.     

Extinction thresholds and negative responses of Afrotropical ant-following birds to forest cover loss in oil palm and agroforestry landscapes

Afrotropical ant-following birds are vulnerable to forest loss and disturbance, but critical habitat thresholds regarding their abundance and species richness in human-dominated landscapes, including industrial oil palm plantations, have never been assessed. We measured forest cover through Landsat imagery and recorded species richness and relative abundance of 20 ant-following birds in 48 plots of 1-km², covering three landscapes of Southwest Cameroon: Korup National Park, smallholder agroforestry areas (with farms embedded in forest), and an industrial oil palm plantation. We evaluated differences in encounter frequency and species richness among landscapes, and the presence of critical thresholds through enhanced adaptive regression through hinges. All species were detected in Korup National Park and the agroforestry landscape, which had similar forest cover (>85%). Only nine species were found in the oil palm plantation (forest cover = 10.3 ± 3.3%). At the 1-km² scale, the number of species and bird encounters were comparable in agroforests and the protected area: mean species richness ranged from 12.2 ± 0.6 in the park and 12.2 ± 0.6 in the agroforestry matrix to 1.0 ± 0.4 in the industrial oil palm plantation; whereas encounters decreased from 34.4 ± 3.2 to 26.1 ± 2.9 and 1.3 ± 0.4, respectively. Bird encounters decreased linearly with decreasing forest cover, down to an extinction threshold identified at 24% forest cover. Species richness declined linearly by ca. one species per 7.4% forest cover lost. We identified an extinction threshold at 52% forest cover for the most sensitive species (Criniger chloronotus, Dicrurus atripennis, and Neocossyphus poensis). Our results show that substantial proportions of forests are required to sustain complete ant-following bird assemblages in Afrotropical landscapes and confirm the high sensitivity of this bird guild to deforestation after industrial oil palm development. Securing both forest biodiversity and food production in an Afrotropical production landscape may be best attained through a combination of protected areas and wildlife-friendly agroforestry.     

Relationship of fish and macroinvertebrate communities in the mid-Atlantic uplands: Implications for integrated assessments

Indices developed for stream bioassessment are typically based on either fish or macroinvertebrate assemblages. These indices consist of metrics which subsume attributes of various species into aggregate measures reflecting community-level ecological responses to disturbance. However, little is known about the relationship between fish and macroinvertebrate metrics, or about how ecological health assessments are affected by assemblage-specific responses to disturbance. We used principal component analysis (PCA) and regression analysis of existing fish (n = 371) and macroinvertebrate (n = 442) stream bioassessment data from a multi-source dataset to determine broad scale, within-assemblage metric patterns, and to examine the intercorrelation of fish and macroinvertebrate metrics (n = 246) and their response to watershed area and land use/land cover gradients. Fish and macroinvertebrate metrics expressed as principal components (PCs) accounted for 72.4 and 85.4% of dataset variance, respectively, with PC-metric patterns reflecting aspects of stream impairment including water and habitat quality. Model components predicting fish metric response differed among fish PCs, with watershed area and macroinvertebrate metric response strongly correlated with the first fish PC, and remaining fish PC models consisting of watershed area, land use, and macroinvertebrate PCs. Correlation between fish and macroinvertebrate PCs, and models relating fish and macroinvertebrate PCs generally explained less variation (13–27%) than metric response models of fish (25–34%) and macroinvertebrates (8–38%) to watershed area and land use/land cover variables. Best-response models integrating fish and macroinvertebrate PCs, watershed area, and land use/land cover variables accounted for the greatest variation in fish PCs (32–50%) across sites. Because fish and macroinvertebrate metrics provide different information on ecological condition, integrated use of information from multiple groups may be appropriate when developing monitoring programs.