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In sum, this alternative measure of the achievement gap between students from higher and lower socioeconomic backgrounds also shows only minuscule change over the course of the past two decades. Figure 2 also shows the white-black achievement gap. While this is not accurately thought of as a socioeconomic gap because of the improvements in black incomes, it represents another potential dimension of continuing societal disparities. As Figure 2 shows, there is a sizable shrinking of the racial gap in the early period but little change across the last two decades. Some have hypothesized that the lack of success in diminishing the size of the socioeconomic gap is due to changes in the racial and ethnic composition of the school population.

It is true that the ethnic makeup of the school-age population has changed dramatically over the past half century, with the share that is white declining from about 75 percent to 55 percent. However, these changes do not seem to have materially affected trends in performance gaps. The 90—10 socioeconomic achievement gap among white students born in was one standard deviation.

By the middle of the period, the divide had declined by about 0. Trends for the 75—25 socioeconomic achievement gap among whites are much the same, confirming that changes in the ethnic composition of student cohorts do not account for the unwavering divide between the haves and have-nots. The average 90—10 income achievement gap across the surveys suggested by the Reardon analysis is very similar to the 90—10 socioeconomic achievement gap we identify.

His results may be a function of a reliance upon cross-sectional studies that use disparate methods for collecting both income and achievement information. Whatever the reason, the trends estimated in his analysis differ markedly from the gaps we observe by using a uniform measure of socioeconomic status and data from intertemporally linked surveys administered to students of the same age.

We might feel differently about these persistent achievement gaps if we found that all achievement was rising and thus suggesting improved economic futures for all. To place the achievement gaps in context, we describe changes in the average level of achievement among students at age 14 and age 17 for students born between and Figure 3 shows a significant upward trend in the average achievement level for all adolescent students of approximately 0. This trend differs by the age of the student, however.

Students at age 14 show an overall increase of about 0. Further, we see no improvement in the performance of older students after the birth cohort. Trends in average levels of achievement do differ in magnitude by subject, but the overall patterns are quite similar. In math, the younger adolescents register average gains of 0. At both ages, the reading gains are less. The trend among younger adolescents amounts to just 0. The differences in trend lines for students at different ages presents a puzzle for which we have no easy answer.

Even setting aside the oldest students in our data, we see that the average improvement in test performance among and year-olds who take the NAEP tests and the TIMSS is larger than that registered by year-olds on the PISA tests. This may reflect differences in test design, or it may suggest that the fade-out in gains begins in the early years of high school. The lack of a positive trend among year-olds for the past quarter century also suggests that high schools do not build upon gains achieved earlier, a signal, perhaps, that the high school has become a troubled institution.

In any event, there is no sign of a rising tide that lifts all boats at age 17 when these students are going into further schooling or into the labor force. Importantly, the age anomaly that we see in the trends in achievement levels is not found in the performance gaps. Constant social gaps are found across all age groups. The achievement gap between haves and have-nots in the U. That gap has not widened, as some have suggested.

The question remains: why has the gap remained constant? The tempting answer is that nothing significant enough has happened to alter its size. But this would ignore a wide variety of factors that have shifted over the years.

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It is more likely that some changes within families and within schools have worked to close the socioeconomic achievement gap while other changes have widened it, with these factors largely offsetting one another. Socioeconomic differences in the age of the mother at the birth of the child have also increased in the past 50 years. The incidence of single-parent households has increased and is likewise concentrated at the lower end of the socioeconomic spectrum. Each would tend to exacerbate socioeconomic achievement gaps. But these negative factors could be offset by other, countervailing demographic changes.

So have differences in the number of siblings in the household. Both factors are important determinants of student achievement. The balance among all these factors may well have left the family contribution to the achievement gap at much the same level today as it was for cohorts born in the s. Similarly, there may be opposing forces within the educational system that have offset one another.

On the one side, over the past 50 years, the federal government has enacted compensatory education programs for school-age children and the Head Start program for students at ages three and four. Brown v. Board of Education and the Civil Rights Act of accelerated school desegregation, particularly in the South. The Individuals with Disabilities Education Act funded school services for students with disabilities, a group disproportionately composed of children from low-income families.

States systematically changed their funding of local schools, often in response to court orders, leading to more equal funding between rich and poor school districts. Overall school funding increased dramatically on a per-student basis, quadrupling in real dollars between and And finally, states have introduced measures holding schools accountable for student performance, as required by the No Child Left Behind Act. Accountability mandates were disproportionately directed toward schools serving low-income students.

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Each is aimed at closing gaps. On the other hand, the quality of the teaching force—a centrally important factor affecting student achievement—may well have declined over the course of the past several decades. Women have greater access to opportunities outside the field of teaching. These changes affecting the quality of the teaching force are likely to have had a disproportionately adverse effect on disadvantaged students.

Collective-bargaining agreements and state laws have granted more-experienced teachers seniority rights, leaving disadvantaged students to be taught by less-effective novices. In other words, a growing disparity in teacher quality across the social divide may have offset the impacts of policies designed to work in the opposite direction.

Two surprises emerge from this analysis of long-term trends in student-achievement levels and gaps across the socioeconomic distribution. First, gaps in achievement between the haves and have-nots are mostly unchanged over the past half century. Second, steady gains in student achievement at the 8th-grade level have not translated into gains at the end of high school. Because cognitive skills as measured by standardized achievement tests are a strong predictor of future income and economic well-being, the unwavering achievement gap across the socioeconomic spectrum sends a discouraging signal about the possibilities of improved intergenerational social mobility.

Perhaps more disturbing, programs to improve the education of disadvantaged students, while perhaps offsetting a decline in the quality of teachers serving such students, have done little to close achievement gaps. These steadfast disparities suggest the need to reconsider the current direction of national education policy. Two areas for further exploration seem especially critical. First, researchers have uniformly found that teacher effectiveness is a predominant factor affecting school quality. While there has been ample commentary on teacher recruitment and compensation policies, few programs and policies at scale have directly focused on enhancing teacher quality, particularly for disadvantaged students.

Second, the achievement gains realized by students at age 14 fade away by age 17, yet policymakers have left high schools—like the achievement gap itself—in many ways untouched. Eric A. Laura M. Ludger Woessmann is professor of economics at the University of Munich. We use surveys from four testing programs to investigate achievement gaps and levels over time.

These surveys use consistent data-collection procedures to trace the achievement of representative samples of U. Each data set comprises student-level data that we aggregate by demographic group. Data are available for math in select years from — and for reading from — We create a panel of math and reading scores for students age 13 and 17, beginning with the birth cohort, who turned 17 in In a typical year, approximately 17, students participate. We create a panel of math and reading scores for 8th graders from — The Main NAEP is aligned to school curricula and designed to provide results for representative samples of students in the United States as a whole and for each participating state.

TIMSS, administered by the International Association for Evaluation of Educational Achievement IEA , is the current version of an international survey that originated as an exploratory study of mathematics conducted across 12 countries in the s. The tests are curriculum-based and developed by an IEA-directed international committee. Beginning with the birth cohort tested in , the TIMSS tests have been designed to generate scores that are comparable over time.

We use the TIMSS 8th-grade math and science tests beginning with this cohort by compiling national data files from , , and , and international data files from , , and The only difference between the national and international data is that the latter do not contain an indicator of race or ethnicity. For this reason, our estimates of the black-white achievement gap for TIMSS are only available for , , and The U. Coinciding with decreasing catch rates, the estimated annual zero-catch probability catching no hammerhead sharks at any given beach within a region per year increased by 4.

As whaler sharks encompass a broad group of sharks within the Carcharinidae family 26 spp. Catches of 1. Coinciding with ongoing declines in numbers of tiger sharks in nets and drumlines, the annual zero-catch probability of tiger sharks increased by 1. Such a result is broadly consistent with previous observations of geographic range constriction commensurate with population declines in pelagic predators As is common when reconstructing historical baselines 16 , some degree of uncertainty exists in the accuracy of effort records in the early years of the QSCP.

Prior to the review and standardisation of the programme in , exact setting of nets may have varied among regions, and differences in hook types and bait on the drumlines may have occurred among regions and through time. Similarly, the accuracy of catch records may be questioned as historical data has been collected by contracted commercial fisherman prior to standardised training in shark species identification from onwards. Further, it is unlikely that changes in drumline gear and bait types would have a substantial effect on CPUE as large sharks are omnivorous and opportunistic 12 , 21 , 22 , and likely do not exhibit strong preference for fish or shark flesh as bait.

While ongoing declines are a cause for concern, historical data from the long-term dataset — suggest that the historical baselines for populations may be substantially higher than that based on contemporary data. This represents a classic case of shifting baseline syndrome 5 , 24 , and implies that studies of sharks declines in the region in recent decades 12 , 25 , 26 may be predicated on a substantially shifted baseline.

Shark control programs operate with the intent of depleting local populations of sharks, yet the spatial scale at which these depletions occur is not well understood. Following the initial deployment of shark nets in Cairns and the Gold Coast in Fig. To quantify the spatial scale of population declines, we explored changes in initial catch rates calculated as the average CPUE for the first five years following the installation of gear at new beaches.

Within regions, CPUE varied substantially among beaches within years, consistent with differential habitat preferences and environmental drivers of shark distributions that operate over relatively small spatial scales 27 , At regional scales, initial CPUE in new net and drumline installations in recent decades occurred at a lower rate than earlier installations for hammerhead, whaler and white sharks Fig.

Symbols courtesy of the Integration and Application Network ian. From a management perspective, assessing the status of stocks through fisheries data can be problematic, as CPUE may be decoupled from abundance due to a range of behavioural and operational factors that can affect catch rates The initial declines in CPUE have been theorised to reflect depletions of local populations, with subsequent catches comprising an influx of sharks from adjacent regions Such a response would result in hyperdepletion, a phenomena by which CPUE declines more rapidly than population abundance The impact of shark control programs upon populations will vary among species, and is likely dependant on both movement patterns, habitat use and the degree of philopatry In theory, hyperdepletion would be more likely to occur in whaler sharks that exhibit small-scale movements and site attachment within bays on the Queensland coastline 33 than larger apex species that undergo large-scale transoceanic migrations 34 and whose populations cover entire ocean basins Indeed, declines in the early years of the programme and increases in the probability of annual zero catches for these taxa may represent selective depletion of site attached or resident individuals from the regional population.

However, the aseasonal migration of sharks to coastal nursery areas adjacent to the QSCP 12 , 39 would favour patterns of hyperstability e. The ongoing reduction in initial CPUE as the programme expanded implies that the scale of declines extend beyond local beaches where the shark control programme operates, and points to serial depletion of large apex sharks throughout the wider region over the past five decades.


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Dynamics population models should now be developed to explore the causes of declines and policy options for reversing them e. Life-history characteristics, such as growth, longevity and fecundity are largely correlated with body size in sharks 44 , Size is also strongly linked to trophic position 4 , 45 , and size-structuring in communities can be a strong determinant of the strength of competitive and predatory interactions While our results provide insight into long-term changes in size structure of large apex shark populations, a degree of uncertainty exists in historical records in the early years of the programme.

In the early years prior to , a bounty system was in place for large sharks over two metres in size, which may have provided an incentive to exaggerate the sizes of smaller sharks by contractors for monetary gain. While the extent to which this occurred is unclear, significant declines in size among hammerhead, whaler and tiger sharks have continued over the past 20 years following the removal of bounties and review of the QSCP — , and the rate of decline in the size of tiger sharks across the long-term dataset year dataset We used previously published estimates of sizes at maturity of shark populations from local studies 27 , 47 , 48 to quantify changes in the probability of maturity in hammerhead and tiger shark catches in the QSCP over the past two decades.

While our estimates assume that size at maturity is fixed over time, sharks are unlikely to exhibit rapid shifts in maturity due to their K -selected life-history strategies Our results indicate significant and substantial declines in the probability of recording mature male and female scalloped hammerheads over the past 20 years Fig. Given the multi-jurisdictional nature of apex shark movement 50 and paucity of historical records, causes of declines at regional scales can often be hard to pinpoint, and vary substantially among species. As nets and baited drumlines are highly efficient in catching sharks, the QSCP is likely to have exerted localised impacts on coastal shark populations in regions where gear has been deployed.

Indeed, shark control programmes are considered effective because they systematically target and reduce populations of large sharks that are believed to be dangerous 9. At regional scales, considering the widespread movement patterns of large apex sharks Fig. The rapid initial declines indicate that apex sharks may be susceptible to even relatively low levels of fishing pressure 3 , Regional movement patterns among coastal and oceanic ecosystems.

Additional increases in recreational and commercial fisheries for tiger sharks over the past 20 years indicates that current fishing pressure of these sharks may be unsustainable The apparent lack of recovery of protected white shark populations despite a complete ban on commercial and recreational fishing in Queensland and neighbouring New South Wales over two decades ago 15 is a cause for concern, and implicates ongoing catches in shark control programs on the eastern Australian coastline and fisheries bycatch as drivers of population declines. Thus, population declines in coastal habitats may have cascading effects in adjacent coastal and pelagic ecosystems.

Movement patterns of great and scalloped hammerhead shark species on the Queensland coastline are currently unknown. Genetic evidence supports connectivity of scalloped hammerheads along the continental shelf between Australia and Indonesia 52 , and although speculative, analysis of population structure suggests that adult females may migrate from Australia to Indonesia and Papua New Guinea Most species of whaler sharks for which data are available exhibited varying patterns of residency, dispersal and connectivity among coastal environments on the Queensland coastline Fig.

Such widespread movement patterns are consistent with recent studies indicating population connectivity spanning among eastern and western Australia, and Hawaii, resulting in a single large Indo-Pacific population of tiger sharks 35 , Such widespread movement patterns of large apex sharks among coastal and pelagic ecosystems indicates a degree of connectivity among habitats sandy beaches, coral reefs, seagrass beds, kelp forests along the eastern coastline of Australia and throughout Oceania Fig.

Depletion of shark populations recorded on the Queensland coastline over the past 50 years may have had cascading effects on broad-scale nutrient transfer and cross-ecosystem linkages among adjacent food webs throughout the region 57 , In terrestrial ecosystems, habitat loss and hunting have been the primary drivers of decline in large vertebrate species 1 , 2. The removal of large carnivores in terrestrial systems has substantial impacts at ecosystem scales 1 , 2 , which is often at direct odds with conservation objectives Hunting to reduce conflict is prevalent in terrestrial ecosystems, yet the extent to which it occurs in marine ecosystems is largely undocumented.

While the efficacy of shark control programs remains controversial, a general perception is that recovering shark populations are to blame for recent increases in unprovoked shark incidents in Queensland and New South Wales 8. By providing unique insight into past coastal ecosystem states, the QSCP data imply that increases in human—shark interactions are occurring at a time when shark populations are severely depleted compared to historical baselines. The timing of these observed declines precede previously reported collapses of coastal and pelagic apex sharks by several decades, and the magnitude of decline is either equal to or exceeding rates reported in coastal oceans elsewhere in the world 3 , 4 , Thus, shark populations within Australian coastlines may be predicated on a substantially shifted baseline.

Promising signs of recovery have been reported from coastal shark populations that have undergone a history of severe exploitation in the Atlantic 61 , yet ongoing serial depletions of large sharks under the QSCP may impact upon local recovery of vulnerable and endangered coastal shark populations. As a historical record of shark catches, the QSCP is unique in that it represents a continuous documented long-term effort, and that both size and identity of sharks have been recorded since the onset of the programme. Nets are considered a passive way of capturing sharks moving across beaches, whereas baited drumlines actively target feeding sharks However, evidence suggests that nets may actively target sharks, as bycatch trapped in nets attract feeding sharks Previous studies indicate that different gear types select for different sharks: hammerhead sharks and rays were particularly vulnerable to net capture, whereas higher catch rates of tiger sharks were observed for drumlines Gear types have been standardised and largely unchanged since around Species identification is generally considered unreliable prior to , while data on species identification following a review of the QSCP in is considered more robust 9.

For long-term analysis — we selected four readily identifiable groups: i hammerheads Sphyrinidae, readily identifiable by their flattened and laterally extended cephalofoil shaped head , ii requiem whaler sharks Carcharhinidae , iii tiger sharks Galeocerdo cuvier , readily identifiable by their large vertical body stripes and blunt head shape , and iv white sharks Carcharodon carcharias , readily identifiable by their robust, large, conical snout and countershading, with a white underside and a grey dorsal area.

Historical effort records account for seasonal lifting of gear and swapping of gear between beaches during seasons to avoid bycatch of turtles and whales, and annual effort was adjusted to reflect these changes. Catch data was standardised by effort at each site to calculate catch per unit effort CPUE 62 for both gear types.

Where catch records were unclear or uncertainty existed regarding number of drumlines or nets, beaches were excluded from the analysis. Similarly, with size data, individuals were excluded where contractors appeared to have recorded measurements in imperial units, or where sizes exceeded the maximum total length TL max for each group We modelled spatial and temporal variation in shark catches under the QSCP between and We used Bayesian generalised linear mixed models to model temporal change in catch as a function of time with nested random effects of region and sites within regions.

As catch rates peaked during the warmer austral summer months November to February , time was modelled following financial years e. July to June Time was also treated as a random effect and its effect on catch was modelled with an order two random walk, which is equivalent to a cubic spline Gear net, drumlines was included in the model as a fixed effect, to account for differences in catchability between gear types.

Catch was modelled with a negative binomial distribution, which was found to adequately account for over-dispersion in catch data. Each group hammerheads, whalers, tigers and white sharks was modelled separately. Prior parameters for the random walk component were specified using the penalized complexity method which controls over-fitting of the temporal trend We used prior parameters of 0. All other parameters were given vague broad priors. For each group two models were fitted, the first allowed the random walk to vary by regions though the random walk component for all regions shared the same hyper-parameters , whereas the second had only an additive regional effect and a shared global random walk.

We compared the two models using the WAIC We calculated annual zero-catch probability as the probability of catching no sharks at a given site per year. Initial catch rates were defined as the average CPUE for the first five years following the installation of gear at new beaches. To quantify the spatial scale of population declines, we explored changes in initial catch rates across all beaches between and We used Bayesian generalised linear mixed models with a random effect of region. Changes in the size of sharks over the long-term dataset — were explored for the four major groups hammerheads, whalers, tigers and white sharks , and for short-term data for scalloped hammerheads and great hammerheads — using linear mixed effects models with gear and sex as fixed effects and site and region as nested random effects.

We used previously published estimates of sizes at maturity of shark species from local studies 27 , 47 to quantify changes in the probability of maturity in shark catches in the QSCP over the past two decades. While our estimates assume that size at maturity is fixed over time, we argue that this is a reasonable assumption in that sharks are less likely to exhibit rapid shifts in maturity due to their K -selected life-history strategies.


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Changes in the probability of catching mature individuals were assessed for either sex using binomial general linear models with site and region as nested random effects. Generalised linear models and GLMMs for maturity were fit using the package lme4 68 and base package in R To assess potential large spatial scale effects of Queensland shark declines, we reviewed existing literature for shark movement data that included movements recorded within the Queensland coastline for shark species recorded in the QSCP catch.

We then extracted a distance metric to represent the maximum movement recorded by an individual of each species. For both satellite and acoustic telemetry this comprised the shortest in-water distance between the furthest points of a minimum convex polygon for the widest-ranging tagged individual.

For conventional mark-recapture or re-sighting, in the case of photographic identification studies using external tags, the greatest distance between initial capture and recapture point among individuals of each species was used. While the greatest distance moved by an individual of each species appears a relatively liberal representation of a species movement, we consider this metric to be somewhat conservative for a number of reasons. First, sample sizes in satellite tagging studies are generally small and deployments short.

Therefore, only the longest tracks are more likely to accurately capture any seasonal movements undertaken by migratory species. Second, acoustic telemetry is limited by receiver placement and any movements beyond the range of receiver arrays are unknown. Estes, J.

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Trophic downgrading of planet Earth. Science , — Ripple, W. Science , Ferretti, F. Patterns and ecosystem consequences of shark declines in the ocean. Roff, G. The ecological role of sharks on coral reefs.

Trends Ecol. Jackson, J. Historical overfishing and the recent collapse of coastal ecosystems. Gallagher, A. Shark recreational fisheries: status, challenges, and research needs. Ambio 46 , — Lotze, H. Depletion, degradation, and recovery potential of estuaries and coastal seas. McPhee, D. Unprovoked shark bites: are they becoming more prevalent?

Queensland Shark Control Program website. Dudley, S. Sumpton, W. Gear selectivity of large-mesh nets and drumlines used to catch sharks in the Queensland Shark Control Program.

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Noriega, R. Trends in annual CPUE and evidence of sex and size segregation of Sphyrna lewini: Management implications in coastal waters of northeastern Australia. Payne, N. Combining abundance and performance data reveals how temperature regulates coastal occurrences and activity of a roaming apex predator.

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Shark control records hindcast serious decline in dugong numbers off the urban coast of Queensland. Research Publication No. Simpfendorfer, C. Size, sex and geographic variation in the diet of the tiger shark, Galeocerdo cuvier, from Western Australian waters. Fishes 61 , 37—46